Journal of Computational and Cognitive Engineering
The Journal of Computational and Cognitive Engineering (JCCE) is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles on the recent trends in computational and cognitive engineering. The goal of the journal is to provide a new platform for the dissemination of the research and current practices in the emerging disciplines of the cognitive and computational engineering to solve the real-world problems. The journal welcomes submission related to various computational and cognitive modelling approaches in the areas of health, education, finance, environment, engineering, commerce and industry. The journal is a Gold Open Access journal, online readers don't have to pay any fees.
Deep Learning Approaches for Detecting Cyberbullying on Social Media
The widespread use of social media has brought many challenges, mainly due to a misconstrued interpretation of the right to freedom of expression. Cyberbullying is a particularly noteworthy issue with far-reaching global implications for both its victims and the wider community. It takes the form of bullying that happens on several social media websites. This paper's goal is to develop a deep learning model capable of recognizing cases of cyberbullying on social media. Four models, such as bidirectional long short-term memory (BiLSTM), convolutional neural network with bidirectional long short-term memory (CNN-BiLSTM), bidirectional long short-term memory with gated recurrent unit (BiLSTM-GRU), and artificial neural network (ANN), will be evaluated in a multiclass classification difficulty context. The results showed that the BiLSTM model outperformed the other models by achieving the highest accuracy in 91% of cases, while the CNN-BiLSTM and ANN models demonstrated relatively lower performance. In addition to determining the efficacy of the deep learning techniques, the work highlights the urgent requirement for strong systems to resist cyberbullying. By enhancing detection accuracy, the proposed model can contribute significantly to providing a safer digital environment for further studies in this field. Received: 24 August 2024 | Revised: 16 December 2024 | Accepted: 25 February 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Ghaith Jaradat: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Mohammad Shehab: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Dyala Ibrahim: Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Project administration. Saif Najdawi: Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization. Rami Sihwail: Software, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
Strengthening Security in Clouds Through Cloud Computing Authentication Using Facial Image Forensics
Numerous cyber threats may succeed in cloud platforms owing to unproductive authentication methods. Multiple credential authentication is a vital precautionary measure that helps reinforce cloud security for warding off imminent data breaches and illegal access. This kind of authentication approach strongly guarantees that trustworthy clients are only ratified to get cloud services, making it less tiresome for clients and more secure for organizations. This study proposes an Image Forensics-based Dual Credential Authentication (IF-DCA) based on username (i.e., preferably email ID to avoid replica) and client photographs as password. Along with a username, a rapid and acquainted user action of capturing a photograph using a web camera is adequate for this method; hence it does not need any expensive, special hardware devices. During the registration phase, along with the username, the user's face image is stored on the cloud server as a password. When registered clients need to access cloud resources or data, they should log in on the server with their username and their photograph. To improve the authentication approach, this work proposes an Attribute-controlled Conditional Generative Adversarial Network (ACC-GAN) to generate face images of the same user at various age groups. ACC-GAN includes an additional attribute control unit and an age prediction unit to synthesize photo-realistic face images with aging effects. After matching the username and face image with the registered username and ACC-GAN-generated images, the face identification module provides access to the authorized cloud user. The performance of IF-DCA is assessed through the Cross-Age Celebrity Dataset using the MATLAB R2018b/deep learning toolbox. The empirical analysis reveals that the ACC-GAN achieves better performance measures such as 98.40% accuracy, 99.3% sensitivity, 90.0% specificity, and 95.7% precision. The performance of IF-DCA is analyzed using the verification time for username, user image, and complete authentication against the number of users. Received: 4 September 2024 | Revised: 14 October 2024 | Accepted: 7 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Cross-Age Celebrity Datasets that support the findings of this study are openly available at https://paperswithcode.com/dataset/cacd. Author Contribution Statement Pranali Dahiwal: Conceptualization, Software, Formal analysis, Resources, Writing – original draft, Visualization, Project administration. Vijay Khare: Methodology, Validation, Investigation, Data curation, Writing – review & editing, Supervision.
Adaptive Gaussian-Based Kernel K-Means: Scalable Adaptive Kernel-Based Clustering for Big Data
In the field of big data, clustering remains a fundamental component of data mining and knowledge discovery, particularly in high-volume, heterogeneous environments. Although traditional clustering algorithms have been widely used, they often exhibit poor scalability, limited robustness, and sensitivity to noise when applied to large industrial-scale datasets. To address these limitations, this paper proposes a Spark-based clustering framework that incorporates an Adaptive Gaussian-Based Kernel K-Means (A-GBK-Means) algorithm, designed to mitigate performance degradation in the presence of noisy and nonhomogeneous data. The A-GBK-Means algorithm integrates fuzzy logic with adaptively determined kernel widths, using local density estimation, thereby enhancing both clustering accuracy and flexibility. Extensive experiments were conducted on real-world industrial sensor data as well as synthetic benchmarks, and performance was evaluated using multiple metrics, including execution time, Silhouette Score, Davies–Bouldin Index, and noise resilience. The comparative analysis demonstrates that the A-GBK-Means algorithm consistently outperforms classical approaches—namely K-Means, Density-Based Spatial Clustering of Applications with Noise, Fuzzy C-Means, and standard GBK-Means—in terms of clustering quality and computational efficiency. Furthermore, the proposed framework exhibits superior scalability, owing to its distributed architecture built on Apache Spark. This study provides practical insights into scalable and interpretable clustering methods for intelligent manufacturing and sensor-based systems and highlights the effectiveness of adaptive kernel learning in modern big data applications. Received: 18 June 2025 | Revised: 8 August 2025 | Accepted: 16 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/dnkumars/industrial-equipment-monitoring-dataset. The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/mohammedarfathr/sensor-data-from-an-industrial-machine. Author Contribution Statement Bakhshali Bakhtiyarov: Investigation, Data curation, Writing – original draft, Project administration. Aynur Jabiyeva: Conceptualization, Validation, Formal analysis. Anakhanim Mutallimova: Software, Resources. Rukhsara Novruzova: Methodology, Writing – review & editing, Supervision. Mahabbat Khudaverdiyeva: Visualization.
A Novel Approach to Model the Economic Characteristics of an Organization by Interval-Valued Complex Pythagorean Fuzzy Information
The success of a nation mainly depends on its economic power. The economic growth plays an important place in enhancing the caliber of life as it produces job opportunities. In this research, the effect of uncertainty on economic growth and investments is examined with gross domestic product (GDP). Currently, the relationship between financial market and economic growth is one of the most important issues in economics. These departments are often accompanied by uncertainty. Through that regard, the idea of interval-valued complex Pythagorean fuzzy relation (IVCPFR) is introduced that is handy in such situations. Moreover, the Cartesian product between interval-valued complex Pythagorean fuzzy sets and verities of IVCPFRs was described. Furthermore, these IVCPFRs are applied to analyze GDP, unemployment, price, demand, supply, interest rate, investment, and money supply effecting business markets. The possibility of the Hasse diagram is more suitable to make sense of the relationship between various economic factors. IVCPFRs will clearly help to set up the impact of one component on the other and change the grades of supportive and distinctive outcomes concerning the time. IVCPFR is used for tracking various multidimensional issues and produces quick and better outcomes rather than the previous methodologies. Finally, the proposed method is the best method to model uncertainty in economics rather than previous methods. Received: 12 May 2022 | Revised: 12 August 2022 | Accepted: 17 August 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
interval-valued complex Pythagorean fuzzy set
interval-valued complex Pythagorean fuzzy relation
Copyright (c) 2022 Authors https://creativecommons.org/licenses/by/4.0/
Switching-Algebraic Calculation of Banzhaf Voting Indices
This paper employs switching-algebraic techniques for the calculation of a fundamental index of voting powers, namely, the total Banzhaf power. This calculation involves two distinct operations: (a) Boolean differencing or differentiation, and (b) computation of the weight (the number of true vectors or minterms) of a switching function. Both operations can be considerably simplified and facilitated if the pertinent switching function is symmetric or it is expressed in a disjoint sum-of-products form. We provide a tutorial exposition on how to implement these two operations, with a stress on situations in which partial symmetry is observed among certain subsets of a set of arguments. We introduce novel Boolean-based symmetry-aware techniques for computing the Banzhaf index by way of two prominent voting systems. These are scalar systems involving six variables and nine variables, respectively. The paper is a part of our on- going effort for transforming the methodologies and concepts of voting systems to the switching-algebraic domain, and subsequently utilizing switching-algebraic tools in the calculation of pertinent quantities in voting theory. Received: 13 February 2023 | Revised: 5 April 2023 | Accepted: 19 May 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Copyright (c) 2023 Authors https://creativecommons.org/licenses/by/4.0/
Digital Transformation and Corporate ESG Performance – Evidence from China
The focal point of this paper is to study environmental, social, and governance (ESG) performance consequences of digital transformation for companies, as well as green technological innovation, corporate social responsibility, and internal control as mediating variables. A set of digital transformation indicators was developed utilizing data from 355 listed industrial companies in China between 2011 and 2019 and vector arithmetic. The study’s findings suggested that digital transformation has considerably raised ESG performance, with green technological innovation, corporate social responsibility, and internal control as significant mediating variables. The paper has several contributions; for example, it provides theoretical and empirical evidence for ESG improvement in Chinese firms and an essential factor associated with ESG performance, among others. The non-economic impacts of digital transformation on ESG performance are essential insights offered by this study. They also provide insight into these impacts' mechanisms, including green technology, corporate social responsibility, and internal control. Received: 9 May 2024 | Revised: 5 August 2024 | Accepted: 31 August 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Wenxin Zhang: Conceptualization, Methodology, Investigation, Resources, Data curation, Writing – original draft. Xiangyu Li: Software, Writing – review & editing.
Copyright (c) 2024 Authors https://creativecommons.org/licenses/by/4.0/
Beta-Transformed Inverse Length-Biased Exponential Distribution: Properties and Applications
In this article, we introduce a novel probability distribution, the beta-transformed inverse length-biased exponential (BTILBE) distribution, derived from the exponential distribution using the beta transformation technique. This approach is advantageous because it introduces no additional parameters beyond those of the baseline distribution. The BTILBE distribution features a highly flexible hazard function and exhibits unique statistical properties that we comprehensively analyze. We explore key statistical characteristics, including moments, inverted moments, incomplete moments, generating functions, mean residual life, and mean inactivity times. Additionally, we provide numerical illustrations of moments, skewness, kurtosis, and the coefficient of variation to offer a thorough understanding of the proposed model. We also derive expressions for entropy measures, such as Shannon entropy, Tsallis entropy, and Renyi entropy, accompanied by numerical examples. The parameters of the BTILBE distribution are estimated using maximum likelihood estimation (MLE), and a simulation study validates their accuracy. To demonstrate the model's practical applicability, we apply it to two real-world datasets, where the BTILBE distribution exhibits exceptional flexibility and outperforms several established distributions, establishing it as a robust and valuable tool for statistical modeling. Received: 1 August 2025 | Revised: 10 November 2025 | Accepted: 21 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data supporting the findings of this study are openly availableat https://doi.org/10.1007/0-387-21645-6_3, reference number [45], and https://doi.org/10.1175/1520-0493(1973)101<0701:TKDMLE>2.3.CO;2, reference number [46]. Author Contribution Statement Jabir Bengalath: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – review & editing, Visualization, Supervision. Mohamed Nejib Ouertani: Software, Validation, Formal analysis, Data curation. Hanene Hamdani: Software, Validation, Formal analysis, Data curation. Fasna Kottakkunnan: Conceptualization, Methodology, Software, Resources. Mohammed Elgarhy: Validation, Investigation, Writing – original draft, Writing – review & editing, Project administration, Funding acquisition.
inverse length-biased exponential distribution
Exploring Image Encryption Techniques: Challenges and Real-Life Application Insights with AI Influence
In today's rapidly evolving digital landscape, the importance of data security cannot be overstated. Every day, vast amounts of information are generated and shared, with each data type (whether text, image, audio, or video) possessing its own unique characteristics and vulnerabilities. Among these, image data stand out due to their intricate structure and the sheer volume of sensitive information that they can contain. Images are different from text data. Text data are straightforward data and are easy to protect. Images are difficult to protect because they are not straightforward. Ways used for encryption have long been considered suitable for encrypting image data. However, currently, they are not. Criminals are developing techniques every day to defeat the right-spotting methods of the business world. Further, this prompts scientists and professionals to find effective solutions. This is where artificial intelligence (AI) comes into play. Techniques for securing images enhanced by AI have the potential to improve image encryption significantly in a manner that increases both protective security efficiency and processing efficiency. This paper discusses various image encryption techniques in detail, including both ancient and modern methods for protecting visual information. It also discusses AI that helps in enhancing the accuracy and efficacy of these encryption techniques. When it comes to encryption of images, AI makes it more powerful. It can also counter newer threats that are emerging. Therefore, security is more dependable and efficient. This paper gives a detailed review of state-of-the-art methods and their effectiveness over traditional methods. By delving into various AI techniques for image encryption, this paper shows that the incorporation of these techniques makes image encryption more secure and efficient. Received: 25 July 2025 | Revised: 18 September 2025 | Accepted: 11 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Dyala Ibrahim: Conceptualization, Methodology, Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Omar Isam Al Mrayat: Methodology, Software, Validation, Investigation, Writing – original draft, Supervision, Project administration. Ahmad Reda Alzighaibi: Formal analysis, Writing – original draft, Visualization. Hasan Hashim: Writing – original draft, Visualization. El-Sayed Atlam: Writing – original draft, Visualization.
Measuring Satisfaction of Security Requirements Using Fuzzy Soft Set Theory
In recent years, a range of innovative approaches have been developed to tackle decision-making challenges more effectively. These approaches include concepts such as probability theory, fuzzy set theory, intuitionistic fuzzy sets, vague sets, interval mathematics, and rough set theory. These tools each offer unique approaches to managing uncertainty and imprecision, which are common when working with complex systems. One major issue in software development is failure to meet security requirements. This can be caused by a variety of factors, such as unclear security specifications, evolving threat landscapes, or gaps in communication among stakeholders. To address this persistent problem, this paper introduces a new method designed to quantify security requirements more accurately and streamline the decision-making process for assessing security risks. This approach is based on fuzzy soft set theory, which allows for more flexible and precise handling of uncertain or ambiguous information. By using this method, software engineers and security professionals can gain a clearer understanding of security risks and make better-informed decisions when building secure systems. To demonstrate the practical value of the proposed method, a case study is conducted to evaluate its effectiveness. The results of this case study suggest that security administrators can use this method to implement more targeted and efficient security measures, ensuring that the systems they develop are more resilient to potential threats. Received: 20 July 2025 | Revised: 20 October 2025 | Accepted: 31 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Dyala Ibrahim: Conceptualization, Software, Resources, Data curation, Writing – review & editing, Supervision, Project administration. Omar Isam Al Mrayat: Conceptualization, Methodology, Software, Resources, Data curation, Writing – review & editing, Supervision, Project administration. Lial Raja Alzabin: Software, Resources, Data curation, Visualization. Ahmad Alajarmeh: Validation, Visualization. Ahmad Reda Alzighaibi: Validation, Formal analysis, Writing – original draft. Hasan Hashim: Investigation. El-Sayed Atlam: Investigation. Malik Jawarneh: Investigation.
Layered Resource Sharing for Distributed Computing in IoT Using Hybrid MILP-Chameleon Swarm Optimization
The emergence of edge computing and IoT has allowed computational resources closer to the end users, hence improving applications that are latency-sensitive and demand significant resources. Distributed computing is a viable method for advancing computations through parallel tasking across multiple computation nodes. Research has been predominantly focused on the masters and worker paradigm, wherein resource sharing is confined to one-hop neighborhoods. This limitation can disrupt the efficiency of distributed computing, particularly when local resources are scarce or the connectivity is inconsistent. This study offers a unique distributed computing paradigm that expands resource-sharing capabilities beyond local one-hop neighbors by leveraging layered network topologies. We propose a hybrid centralized mixed-integer linear programming optimization method that incorporates a chameleon swarm optimization approach to derive the optimal solutions. The efficacy of the proposed scheme was assessed through theoretical analyses and extensive simulation experiments, which demonstrated its advantages over traditional distributed computing and computation offloading schemes. Received: 20 July 2025 | Revised: 4 November 2025 | Accepted: 24 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Sathishkumar Samiappan: Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Anand R: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Killer Whale Algorithm for Solving the Maximum Flow Problem on Transportation Networks
The most extreme stream issue (MFP) may be a principal subject in the chart hypothesis with applications in computer science, operations inquiry, and designing. It plays a key part in ranges such as activity control, communication frameworks, and the dissemination of assets. Classical calculations, especially Ford–Fulkerson and its expansions, have given the establishment of the means for tackling this issue. In any case, when systems develop bigger, more complex, or dynamically alter, these approaches regularly battle to preserve effectiveness and accuracy. Much like transportation frameworks, each course in an arrangement encompasses a capacity restraint, confining how much stream can move between hubs. Conventional strategies, which accept settled structures and unsurprising imperatives, are not continuously suited to taking care of these challenges. To deal with these limits, this think piece shows the Killer Whale Optimization (KWO) calculation; it is pushed by the chasing methods of killer whales. The strategy was based on multi-flow problems using real and made-up datasets. To show its useful meaning, a case study was made on a project stream in Kota Kinabalu, where made-up systems of different sizes were used to test flexibility and reliability. The result seems like KWO usually beats the Ford–Fulkerson method, especially because it does faster merging and shows more flexibility when dealing with complicated stream situations. These advantages make KWO a good choice for real world stream optimization problems. Beyond theoretical interest, the results suggest that the calculation may be used for project management, transport planning, and resource assignment in various organized systems. Received: 19 July 2025 | Revised: 18 September 2025 | Accepted: 22 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Afaf Edinat: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Mohammad Shehab: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Fatima Haimour: Methodology, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Supervision. Hanaa Fathi: Conceptualization, Software, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Katrina Sundus: Software, Validation, Writing – original draft, Writing – review & editing.
Sharded Fog Architecture for Scalable CP-ABE with Minimized Policy Trees in Blockchain Systems
The protection of Personally Identifiable Information (PII) remains a significant challenge, particularly within financial transactions. Current systems often fail to strike an optimal balance between security, performance, fine-grained access control, and distributed processing. A common dilemma is that some solutions prioritize security at the cost of efficiency, while others struggle with the complexity of managing detailed access controls in decentralized environments. Blockchain technology is widely recognized for its auditability, tamper resistance, and decentralization; however, the limitations of this method are computational efficiency of cryptographic operations and latency. Thus, to solve such limitations, this paper proposes an innovative approach that combines Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with a lightweight fog layer. This hybrid design offloads resource-intensive cryptographic operations to the fog layer, effectively reducing the complexity of the policy tree while also protecting against potential side-channel attacks through a multi-layer encryption strategy. The proposed method not only strengthens data confidentiality but also ensures that the impact on transaction performance is minimal. This approach has been proven to be more secure, efficient, and a significantly distributed solution for managing sensitive information. Additionally, the research validates the effectiveness of this design through practical implementation and experimental comparisons with state-of-the-art systems, conducted across various settings. These experiments highlight the advantages of the proposed approach in terms of both security and operational efficiency regarding decryption and encryption, as experimental results show that this paper achieves near-constant encryption and decryption times at the Data Owner and Data User sides, reducing latency by over 80% compared to existing schemes. Received: 12 July 2025 | Revised: 10 September 2025 | Accepted: 7 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitHub at https://github.com/Fraunhofer-AISEC/rabe. Author Contribution Statement Leon Wirz: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Pattarasinee Bhattarakosol: Validation, Writing – review & editing, Supervision, Project administration.
An Improved Computational Model Based on Cardiac Imaging Data for the Quantitative and Qualitative Assessment of the Mechanical Properties of the Human Left Ventricle
Computational modeling has contributed to many fields of medicine and has been proven very useful for diagnosing complex diseases. A special case is that of myocardial infarction (MI), a prevalent cardiovascular disease where computational modeling approaches have been useful for detecting abnormalities. The aim of this paper is to propose enhanced computational models for the human left ventricle (LV) to estimate the strain and stress distributions in healthy and diseased subjects. Computational models were developed and evaluated using human LV of 20 patients with MI and 20 healthy controls using cardiac magnetic resonance imaging acquisitions and simulation tools. The finite element technique was employed for LV modeling. Comparative analysis revealed higher global strains in healthy subjects compared to MI patients, particularly global circumferential, longitudinal, and radial strains. The average stress distributions were 67.9 ± 5.01 kPa in healthy models and 78.3 ± 8.21 kPa in infarcted regions. Model-derived strain data indicated an overall average of −0.15 ± 0.06 for healthy models and 0.2 ± 0.04 for infarcted regions. LV strain values were compared with those obtained from two feature-tracking algorithms to validate the proposed models, resulting in very promising findings. The work presented here highlights the importance of computational modeling for quantitative and qualitative analyses of heart disease and the potential of using such models in other organs. When combined with imaging data, the proposed models can have significant implications for improved patient care and treatment strategies. Received: 3 June 2025 | Revised: 30 July 2025 | Accepted: 8 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Rania Awadi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Narjes Benameur: Methodology, Validation, Investigation, Writing – review & editing, Visualization, Supervision, Project administration. Wafa Baccouch: Formal analysis, Writing – review & editing, Visualization. Mohamed Deriche: Methodology, Writing – review & editing, Visualization, Project administration, Funding acquisition. Arnab Palit: Validation, Visualization. Moncef Aloui: Resources, Data curation. Nadjia Kachenoura: Software, Validation, Resources, Data curation, Writing – review & editing, Visualization. Salam Labidi: Methodology, Investigation, Writing – review & editing, Visualization, Supervision, Project administration.
AI-Driven Prediction of Greenhouse Gas Emissions in Livestock Supply Chains: Towards a Data-Driven Model for Sustainable Agri-food Systems
This greenhouse gas (GHG) emissions mainly from enteric fermentation and manure management in livestock are mostly from copious methane (CH₄) and some nitrous oxide (N₂O). IPCC (International Panel on Climate Change) Tier 1 and Tier 2 emission prediction methods cannot provide details about where the emissions come from and are ineffective for different forms of agriculture. In our research, a machine learning model based on national agricultural data forecasts how much CH₄ and N₂O will be emitted from U.S. manure management. The performance of Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) was tested by using RMSE, MAE and R² as performance metrics. XGBoost performed better than SVR since its predictive results were better than reaching R² = 0.98. The analysis of feature importance found that livestock type, methods of managing manure and population density are the main factors leading to emissions. The models resulted in information that communities in various locations could use to improve their sustainability. An adaptable decision-making procedure is proposed by the research to assist environmental planning in the agri-food sector and to ensure that intelligent agricultural platforms can better manage GHG emissions. More research is needed to improve the model by studying additional aspects from the supply chain, covering both its upstream and downstream operations, to obtain a complete analysis of environmental results. Future work should aim to incorporate additional stages of the livestock supply chain and adopt explainable AI techniques to improve transparency and support realtime decision-making. Received: 22 April 2025 | Revised: 31 July 2025 | Accepted: 3 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in USDA at https://agdatacommons.nal.usda.gov/search?itemTypes=3&categories=30761,30746,30689,30821,33323. Author Contribution Statement Baha M. Mohsen: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Mohamad Mohsen: Methodology, Validation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization.
Early-Stage Diabetic Retinopathy Diagnosis with Feature Pyramid Networks and Spatial Pyramid Pooling Utilizing Full-Field Optical Coherence Tomography (FF-OCT)
Diabetes is a disease that is growing at the highest rate globally, and it has several impacts. One of such complications is diabetic retinopathy (DR), which causes damage to the retina and vision impairment. DR can be mild, and it can also be severe, and there are many shades in between. Therefore, it is possible to avoid vision loss if DR is diagnosed and treated in its early stages. Diagnosis of DR today is still a tedious process as only ophthalmologists can diagnose it using digitized colorful retinal fundus images. This article describes a new strategy for the early identification of DR that combines full-field optical coherence tomography (FF-OCT) with sophisticated deep learning (DL) algorithms, notably Feature Pyramid Networks (FPNs) linked with spatial pyramid pooling (SPP). FF-OCT produces high-resolution, cross-sectional pictures of the retina, revealing precise structural changes that occur before apparent symptoms of DR. We created a multiscale DL framework that takes advantage of the capabilities of FPNs and SPP to efficiently process and evaluate the fine features of FF-OCT images. The FPN design detects changes in the retinal structure at different scales, which improves the network's performance in detecting early signs of DR. On the other hand, the SPP module collects contextual information from several sub-regions of the image to provide a stable and accurate feature representation regardless of the size and location of the lesion. These models were trained and validated on this dataset using performance indicators such as sensitivity, specificity, and area under the ROC curve (AUC). The findings of this paper suggest that the FPNs with the SPP model are superior to the traditional image analysis methods and the standard Convolutional Neural Network in diagnosing early-stage DR. Received: 9 November 2024 | Revised: 10 March 2025 | Accepted: 3 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/paultimothymooney/kermany2018. Author Contribution Statement Anitha Jaikumar: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Sreenivasa Chakravarthi Sangapu: Conceptualization, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision, Project administration.
full-field optical coherence tomography (FF-OCT)
An Explainable AI for Stock Market Prediction: A Machine Learning Approach with XAI and Deep Neural Networks
Forecasting stock prices remains a complex challenge due to the inherent nonlinearity and volatility of financial markets. This study proposes a deep learning framework that integrates a long short-term memory (LSTM) network with a multiplicative attention mechanism to dynamically prioritize informative time steps in historical data. To improve robustness during training, a hybrid loss function combining mean absolute error (MAE) and mean absolute percentage error (MAPE) is employed, effectively penalizing both absolute and relative prediction errors. The framework incorporates sentiment signals and technical indicators from historical stock data to enrich the feature set. Explainable artificial intelligence (XAI) techniques, including SHAP and LIME, are integrated to ensure the global and local interpretability of the model's decisions. Experimental evaluation across 25 independent runs demonstrates that the proposed model consistently outperforms baseline approaches such as XGBoost, random forest, and conventional LSTM, achieving a low MSE of 2.45, an RMSE of 1.56, an MAE of 1.08, and an R² score of 0.88. These results validate the model's effectiveness in delivering highly accurate and interpretable stock price forecasts, thereby supporting more transparent and informed financial decision-making in real-world applications. Received: 10 June 2025 | Revised: 30 September 2025 | Accepted: 11 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Yahoo Finance at https://finance.yahoo.com/quote/AAPL/history/?utm_source=chatgpt.com&period1=1577836800&period2=1735603200. Author Contribution Statement Kangana Wallapure Manikrao: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Shridhar Allagi: Validation, Resources, Writing –review & editing, Supervision, Project administration. Wai Yie Leong: Supervision, Project administration. Mahantesh Laddi: Visualization.
Optimized Ensemble Machine-Learning-Driven Transparent Thyroid Cancer Prediction Using Explainable Artificial Intelligence
Thyroid cancer disease diagnosis is a critical medical challenge, requiring accurate and reliable predictions to support clinical decision-making. Patients may suffer from an incomplete diagnosis while using traditional machine learning models. In this study, we present an optimized ensemble machine-learning framework for predicting thyroid cancer disease. The methodology integrates multiple classifiers, including support vector machine, random forest, Naïve Bayes, K-nearest neighbors, and decision tree. The final classification decision is determined with the help of soft voting by a total predictive support vector machine, which selects the classifier with the highest confidence score among the ensemble models. The ensemble strategy enhances predictive accuracy and robustness by combining the strengths of individual classifiers. The model was trained and evaluated, achieving an impressive accuracy of 0.9633 and an area under the receiver operating characteristic curve of 0.9914. The proposed method of this study is very accurate, but there is still a black box problem. To overcome this issue and to ensure interpretability, Explainable Artificial Intelligence techniques, including Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations, are implemented, providing insights into feature contributions towards the performance of the proposed method and model decisions. The dataset contains a total of 30 features with 3,772 different cases consisting of two classes. Received: 16 June 2025 | Revised: 14 August 2025 | Accepted: 24 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/bidemiayinde/thyroid-sickness-determination. The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/bhargavchirumamilla/thyroid-cancer-risk-dataset. The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/sikandaraidev/thyroid-dataset. Author Contribution Statement Syed Younus Ali: Conceptualization, Software, Data curation, Writing – original draft, Writing – review & editing. Bilal Shoaib Khan: Conceptualization, Methodology, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision. Abdul Hanan Khan: Software, Validation. Muhammad Adnan Khan: Formal analysis, Investigation. Asghar Ali Shah: Validation, Investigation. Sagheer Abbas: Formal analysis, Project administration. Khan Muhammad Adnan: Methodology, Resources.
Explainable Artificial Intelligence (XAI)
From Tissue Image to Transcriptomics: Dual Modality Deep Learning Model for Classification of Cancer Using Histopathological Images and Gene Expression Pattern
A primary method for cancer detection involves the examination of histopathological images. However, traditional approaches to analyze these images are time-consuming and prone to errors. With recent advancements in deep learning, researchers are increasingly leveraging these models to enhance the accuracy and efficiency of histopathological image analysis. In this study, a deep learning-based model is proposed for multi-class cancer classification that integrates gene expression prediction with histopathological image analysis to enhance diagnostic precision and streamline the detection process. The model is intended for use in real-world clinical settings where it is a challenge to classify data correctly because predefined sequences of gene expression and cancer labels are often missing. Fourier Neural Networks (FNN) and EfficientNetB0 are utilized to get a full set of spatial and frequency-based features from Whole Slide Images (WSIs). To select the best set of features, Incremental Principal Component Analysis (IPCA) was used, and the resulting representations were subsequently reconstructed from patch-level features to WSI representations. A DeepONet model, one of the advanced deep learning models, was selected for mapping the generated histopathological image features to predict gene expression patterns. After training, the model achieved 93% accuracy in classifying cancer types with a 0.92 precision value indicating acceptable performance with respect to multiple cancer classifications. Additionally, the model achieved strong performance in gene expression prediction, with a Mean Absolute Error (MAE) of 0.033 and an R² score of 0.765, demonstrating its reliability in capturing gene expression patterns. Addressing real-world challenges such as missing gene expression data or ambiguous cancer-type classifications, the proposed Dual Deep Learning Model enhances cancer diagnosis by improving accuracy in oncology. By integrating histopathological image analysis with gene expression prediction, the model enables automated clinical decision-making, offering a robust solution for distinguishing between cancerous and non-cancerous cases. Received: 11 June 2025 | Revised: 15 August 2025 | Accepted: 13 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Anju Das: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Neelima Nizampatnam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Somnath Ganguly: Investigation, Data curation, Writing – review & editing, Visualization, Supervision, Project administration. Joon Ho Choi: Validation, Investigation, Writing – review & editing, Visualization, Supervision, Project administration.
Fake News Detection with Deep Learning: Insights from Multi-dimensional Model Analysis
This study aims to systematically evaluate and compare various deep learning models in terms of accuracy, efficiency, and interpretability for fake news detection. Leveraging recent advancements in pretrained models (e.g., BERT, RoBERTa) and lightweight frameworks (e.g., TextCNN), we implemented and optimized multiple detection models. Comparative analysis was conducted on a dataset containing approximately 40,000 news texts. Results revealed that BERT Large significantly outperformed other models, achieving an accuracy of 99.33%, attributed to its extensive semantic understanding capabilities. Conversely, TextCNN, despite its simpler architecture, achieved competitive accuracy (98.77%), demonstrating substantial practical value for resource-limited environments. Interpretability analysis via attention visualization highlighted distinct cognitive strategies of pretrained models when classifying real versus fake news. While the study addresses critical technical challenges in fake news detection, limitations related to potential dataset biases and domain specificity were acknowledged, suggesting opportunities for future research on multimodal and cross-domain adaptations. This research contributes substantially by providing practical benchmarks and interpretability insights, significantly enhancing real-world fake news detection systems, thus aiding platforms in combating misinformation effectively. Received: 30 April 2025 | Revised: 11 June 2025 | Accepted: 7 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Qiuping Li: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Fen Fu: Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Yinjuan Li: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Bhunnisa Wisassinthu: Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Wirapong Chansanam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Tossapon Boongoen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.
Hybrid AI Ensemble and Blockchain-Based Chatbot for Decentralized Toddler Nutritional Status Classification
The accurate and timely classification of toddlers' nutritional status is critical for early intervention, particularly in remote or underserved communities with limited access to healthcare professionals. However, data security, especially for children's health data, is equally essential to ensure safe storage and access. To address these challenges, this study proposes a hybrid AI-powered chatbot that integrates ensemble learning, blockchain, and decentralized storage to support both nutritional status classification and educational interaction. The system combines a random forest model for classification with GPT-3.5 Turbo for bilingual (Indonesian–English) stunting education deployed via Telegram. Preprocessing includes standardizing, normalizing, and encoding Indonesian-language nutrition data to ensure machine learning readiness. Six ensemble algorithms are evaluated using stratified five-fold cross-validation, with classification results hashed using SHA-256 and immutably stored on the Interplanetary File System (IPFS) and a local Ethereum blockchain. The chatbot effectively manages both structured inputs and natural language queries, ensuring secure, transparent, and real-time nutritional assessments. Results demonstrate high classification performance, with the random forest model achieving the highest mean F1-score (0.9987) and the lowest deviation. Its robustness was validated by a 20% hold-out test set and stratified five-fold cross-validation, which obtained excellent balanced performance across nutritional status categories (F1-macro, precision, recall, accuracy ≈ 0.99; ROC AUC = 1.00). External validation also yielded robust and consistent results (F1-macro = 0.97, precision = 0.97, recall = 0.96, ROC AUC = 0.98, and accuracy = 0.97), demonstrating the model's generalization ability and mitigating concerns regarding overfitting. Blockchain evaluation confirmed stable and linear CID transaction throughput (blocks 29–46) with no observed latency, ensuring reliable and continuous data recording. Furthermore, gas prices decreased by ~87.5%, highlighting significant improvements in cost efficiency and scalability, which reinforces blockchain's feasibility for decentralized, AI-driven health data management. Received: 9 June 2025 | Revised: 29 September 2025 | Accepted: 31 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/rendiputra/stunting-balita-detection-121k-rows and https://www.kaggle.com/datasets/jabirmuktabir/stunting-wasting-dataset. Author Contribution Statement Wa Ode Siti Nur Alam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Riri Fitri Sari: Conceptualization, Writing – review & editing, Supervision, Funding acquisition.
An Efficient Recommendation System in E-commerce Using Passer Learning Optimization Based on Bi-LSTM
Online reviews play a crucial role in shaping consumer decisions, especially in the context of e-commerce. However, the quality and reliability of these reviews can vary significantly. Some reviews contain misleading or unhelpful information, such as advertisements, fake content, or irrelevant details. These issues pose significant challenges for recommendation systems, which rely on user-generated reviews to provide personalized suggestions. This article introduces a recommendation system based on a Passer Learning Optimization-enhanced Bidirectional Long Short-Term Memory network classifier applicable to e-commerce recommendation systems with improved accuracy and efficiency compared to state-of-the-art models. More specifically, the proposed model achieves a high accuracy of 98.03%, F1 score of 98.03%, precision of 98.49%, recall of 97.57%, and minimum mean square error of 1.97 based on training percentage using the patio lawn garden dataset. These results, made possible by advanced graph embedding for effective knowledge extraction and fine-tuning of classifier parameters by the hybrid PLO algorithm, establish the suitability of the proposed PLO-Bi-LSTM model in various e-commerce environments. Received: 9 April 2025 | Revised: 26 June 2025 | Accepted: 24 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The Amazon product data that support the findings of this study are openly available at https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html. Author Contribution Statement Hemn Barzan Abdalla: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Mehdi Gheisari: Conceptualization, Software, Data curation. Awder Ahmed: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Bahtiyar Mehmed: Formal analysis, Investigation, Resources. Maryam Cheraghy: Validation, Resources, Writing – review & editing. Yang Liu: Validation, Investigation, Resources.
Multitask Virtual Keyboard Controlled by an Eye-Gaze-Based Intelligent Entry System
In modern society, computers and cell phones play an integral role in individuals' everyday lives. Individuals utilize messaging platforms such as WhatsApp, WeChat, and Facebook daily to communicate with others. Virtual keyboards are a significant tool in assistive technology because they aid individuals with significant motor disabilities in making effective contact with computers. This study proposes an eye-gaze-controlled virtual keyboard using a new method called Distance Eyelid-Iris-MediaPipe, which enables individuals with disabilities to write effectively. The suggested virtual keyboard is an affordable assistive device because it only necessitates a webcam for operation. The proposed virtual keyboard consists of six supplementary menus: two menus for English letters, one for numerical digits and mathematical operations, one for important text operations, one for Latin symbols, and an emoji menu. The suggested keyboard has special characteristics such as auditory feedback and visual highlighting of pressed keys. The testing of the proposed system received positive feedback from several users, and the empirical results of the proposed system were better than those of the previous models. It achieved this with an average typing rate of 18 characters per minute and 4 words per minute, and it had a NASA-TLX score of 10% and a system usability scale score of 93. Received: 27 April 2025| Revised: 25 June 2025| Accepted: 11 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Amal Hameed Khaleel: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Thekra Abbas: Methodology, Validation, Resources, Writing – original draft, Writing – review & editing, Supervision. Abdul-Wahab Sami Ibrahim: Conceptualization, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision.
Semantic-Driven Automation of BIM for Stone-Paved Roads: An Ontology-VPL Integrated Approach
The integration of semantic technologies and procedural automation is redefining how infrastructure assets are modeled and managed. Yet, stone-paved roads, common in historical urban centers, remain largely unsupported by existing building information modeling (BIM) standards and tools. This paper presents an innovative, fully automated workflow that bridges this gap by combining a visual programming language (VPL) approach with an ontology-driven semantic framework. Compared to the traditional manual modeling workflow in the BIM-Authoring software Autodesk Civil 3D, which may take several hours, the proposed method generates a complete 3D BIM model by fully automating several modeling operations via a Dynamo graph that takes very short computational times. To achieve this, we developed a parametric modeling pipeline that, based on structured input data, can rapidly generate high-fidelity 3D models of stone-paved roads. To overcome semantic limitations in the Industry Foundation Classes (IFC) schema, we introduce the IFC for stone-paved roads (IFC-SPRO) ontology, designed to enrich geometric models with machine-readable knowledge about modular pavement typologies and maintenance strategies. By converting IFC data into resource description framework (RDF) format and querying it via SPARQL and AI-driven interfaces, the model supports advanced information retrieval for asset management and heritage conservation. The proposed approach not only enhances interoperability and reduces modeling time dramatically but also establishes a scalable foundation for integrating historical road infrastructure into modern digital workflows. Received: 14 April 2025 | Revised: 10 September 2025 | Accepted: 25 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Mattia Intignano: Conceptualization, Methodology, Software, Formal analysis, Resources, Writing – original draft,Writing – review & editing, Visualization. Salvatore Antonio Biancardo: Conceptualization, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Sara Guerra de Oliveira: Conceptualization, Validation, Resources, Writing – review & editing,Visualization. Andrej Tibaut: Conceptualization, Methodology, Software, Resources, Data curation, Writing – review & editing, Supervision. Gianluca Dell'Acqua: Conceptualization, Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration.
A Study on Zeek IDS Effectiveness for Cybersecurity in Agricultural IoT Networks
As agriculture moves toward Agriculture 4.0, which uses Internet of Things (IoT) devices to collect data in real time and monitor things from a distance, these networks are becoming increasingly vulnerable to cyberattacks. A common method used to protect against these kinds of threats is the use of intrusion detection systems (IDS). However, the agricultural environment is often changing and has limited resources, which makes cybersecurity challenging. Several available IDS tools are not designed to work properly in places with few resources, intermittent access, and unpredictable network conditions. This paper investigates the performance of Zeek, an open-source IDS, in identifying potential threats in agricultural IoT networks. We performed both offline and real-time experiments: offline analysis used pcap files from the Stratosphere Laboratory dataset, and real-time evaluation involved simulated live attack scenarios, focusing on unauthorized access attempts and distributed denial-of-service (DDoS) attacks. Zeek's performance was assessed based on CPU and memory utilization, as well as quality of service (QoS) metrics. From the experimental results, we found that Zeek was quite effective in protecting agricultural IoT networks against typical threats. Memory usage remained stable around 5% during offline analysis and under 20% during active attacks. However, CPU usage was more volatile, peaking at 120% during DDoS events. In terms of QoS, the system maintained a good throughput (1,375 kbits/s) with minimal packet loss (0.000186%). Among the attack types that we tested, brute force attacks, which represent attempts at unauthorized access, had the strongest effect on network performance, increasing delay to 2.159 ms and jitter to 0.793 ms. It seems clear that a heavier traffic load during such attacks can interfere with QoS. On the basis of our observation, we recommend practical deployment strategies for agricultural IoT systems that take these limitations into consideration, aiming to keep networks both secure and efficient under pressure. Received: 2 June 2025 | Revised: 2 September 2025 | Accepted: 13 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Stratosphere Laboratory at https://www.stratosphereips.org/datasets-overview, in GitHub at https://github.com/zeek/zeek, https://github.com/vanhauser-thc/thc-hydra, and https://github.com/antirez/hping. Author Contribution Statement Samsul Huda: Conceptualization, Formal analysis, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Muhammad Bisri Musthafa: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing – review & editing, Visualization. S. M. Shamim: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing – review & editing, Visualization. Yasuyuki Nogami: Supervision, Funding acquisition.
Adaptive Fault-Tolerant Scheduling in Dynamic Clouds
The transition from product-centric to service-driven paradigms in cloud computing has enabled the on-demand provisioning of scalable resources. However, the extensive operational complexity and interdependent infrastructure of cloud environments make them highly susceptible to failures, posing significant challenges to ensuring robust fault tolerance in dynamic and distributed systems. This study introduces a novel Hybrid Value-Policy Double Deep Q-Learning (DDQL) Scheduling approach, integrating value-based DDQL with policy-based reinforcement learning techniques, which are Proximal Policy Optimization and Soft Actor-Critic, to enhance fault-tolerant scheduling dynamically. Combining the advantages of both learning paradigms, the proposed method improves decision-making efficiency, enhances system resilience against failures, and optimizes scheduling performance in real-time cloud environments. Improved mean time between failures, mean time to recovery, failure recovery time, and fault tolerance success rate are guaranteed by the Hybrid DDQL scheduler's dynamic adjustments to scheduling and resource allocation algorithms. Also, it reduces the ratio of task deadline miss, energy consumption, and response time and maximizes the throughput. These results confirm the flexibility and strength of the model in dynamic and unreliable real-time cloud computing systems. Received: 27 May 2025 | Revised: 30 September 2025 | Accepted: 16 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Chetankumar Kalaskar: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Thangam Somasundaram: Methodology, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.
Nonclinical Approaches to Autism Trait Prediction Using Deep Learning: An Extensive Review
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in communication and social behavior. Early identification of autistic traits is necessary for timely treatment. However, clinical diagnostic methods remain costly, time-consuming, and inaccessible. AI-guided nonclinical screening techniques such as eye tracking and behavior analysis may offer a promising alternative. This is the first systematic bibliometric analysis attempting to discuss the effectiveness, limitations, and trends of AI-based methods proposed for identifying autistic traits using nonclinical analysis. The current study is an unprecedented bibliometric analysis of 152 Scopus and Web of Science articles (2018–2025). This analysis uses VOSviewer and Gephi to map different relations. Deep learning and hybrid transfer learning yield better results. However, most of the proposed methods possess low specificity. The proposed research establishes eye-gaze analysis as the most commonly employed technique in the present study, while key behavioral cues—facial expressions, attention changes, and verbal patterns—are yet to be studied. The key shortcoming is that no standardized evaluation system exists, with earlier work lacking rigorous benchmarks for specificity, interpretability, and bias mitigation. Furthermore, while transfer learning techniques are widely used due to dataset scarcity, no publicly available video-based datasets exist, restricting the development of multimodal ASD screening models. To fill these gaps, this paper suggests a benchmarking framework that focuses on multifeature evaluation (facial expressions and attention shifts) for better diagnosis. In addition, standardized specificity thresholds support clinical reliability and foster geographically diverse, openly available datasets to obtain fairness and generalizability. This study provides the bibliometric synthesis of nonclinical AI-based ASD screening. It proposes a benchmarking framework with multifeature evaluation, standardized specificity thresholds, and diverse open datasets and maps data modalities to suitable deep learning models. These contributions provide practical guidance and deliver actionable insights to advance scalable, multimodal, and interpretable ASD screening tools for early, noninvasive detection. Received: 20 May 2025 | Revised: 10 September 2025 | Accepted: 15 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/shahriarrafi1071/autism-dataset?select=AutismDataset. Author Contribution Statement Ranjeet Vasant Bidwe: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Sashikala Mishra: Conceptualization, Methodology, Software, Validation, Data curation, Writing – review & editing, Supervision, Project administration. Simi Bajaj: Conceptualization, Methodology, Software, Validation, Writing – review & editing, Supervision, Project administration. Suraj Sawant: Validation, Formal analysis, Investigation, Resources, Writing – review & editing, Supervision, Project administration. Kailash Shaw: Validation, Writing – review & editing, Supervision. Ketan Kotecha: Validation, Supervision, Project administration.
Micro-expression and Masked Expression Classification Using Neural Networks
Micro-expressions are short, involuntary facial expressions unnoticeably occurring for a fraction of a second. They are highly valuable in various real-life situations since the facial muscles associated with micro-expressions cannot be consciously controlled. They can also aid psychiatrists in diagnosing patients or police officers in detecting the true emotions of suspects. In contrast, masked expressions are intended to hide genuine emotions, and these occur when someone uses a falsified expression to conceal their true feeling. In this study, a custom Micro Mask Emotion Dataset-Face Speech Text was developed for both micro-expressions and masked expressions using questionnaires and images as stimuli. The study participants were 18–25 years old and did not wear any face mask. The dataset was compiled and evaluated using various neural network models. The results indicate that females are more expressive and the questionnaire-induced dataset showed better prediction ability than the image-induced dataset on a similar set of neural network models. Received: 13 May 2025| Revised: 13 August 2025 | Accepted: 2 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Karan Suresh: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Shamyuktha Ramachandran Sugumaran: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Suja Palaniswamy: Conceptualization, Data curation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Manju Priya Arthanarisamy Ramaswamy: Writing – review & editing, Visualization.
A Computational Complexity-Based Framework for Predicting the Difficulty of Sudoku Puzzles Using CSP-Based Symbolic Modeling and Constraint Metrics
This paper provides a logic-based method for the categorization of the puzzle difficulty levels of Sudoku using deterministic measures of computational complexity. Shifting from heuristic, statistical, or machine-based paradigms, the method presents four symbolic measures: constraint density metric (CDM), logical inference depth (LID), guessing complexity (GC), and constraint graph tightness (CGT). These are derived from the Constraint Satisfaction Problem theory and implemented using R programming without the application of data-driven inference. The new method assesses puzzle complexity based on pre-solving properties yielding consistent outputs that align with theoretical NP-completeness measures. Difficulty levels are grounded on strict numerical criteria, e.g., CDM over 700, LID 14 or higher, GC over 6, and CGT approaching 30, indicative of increased structural complexity. The method is compared with rule-based, backtracking, and solver-based methods using benchmarks and shown to run with linear time and space complexity O(n) compared with traditional methods with time and space complexity O(n²) or exponential growth O(2ⁿ). The method presents a reproducible, scalable method for analytically assessing puzzles and provides a formal, non-heuristic method for evaluating complexity within combinatorial logic systems. Received: 12 May 2025 | Revised: 29 July 2025 | Accepted: 17 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/bryanpark/sudoku. Author Contribution Statement Rajan Thangamani: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization, Project administration. Pallavi Regulagedda: Software, Validation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision.
Factors Affecting the Adoption of Generative AI Tools Among Information Technology Employees: A UTAUT3, TTF, and SOR Perspective
This empirical study examined the components that affect the adoption and actual use of generative AI tools among information technology (IT) employees in Hyderabad. This study also attempted to unravel the SOR, UTAUT3, and TTF models by integrating them for practical application in industry and by IT sector employees. Data were gathered from 470 employees working in several IT industries in Hyderabad by adopting a quantitative method for this investigation. IBM-AMOS was used to test the hypotheses. Thirty-six variables were used to measure the following 10 reflective constructs: optimism, innovativeness, trust, performance expectancy, effort expectancy, hedonic motivation, social influence, task–technology fit, adoption intentions toward generative AI tools, and actual use of generative AI tools. EFA and CFA analyses were conducted to unravel the structural relationships between constructs, and hypotheses were tested using SEM. A 12% variance in the adoption intention of generative AI tools by IT industry employees was explained by optimism, innovativeness, trust, performance expectancy, effort expectancy, hedonic motivation, social influence, and task–technology fit, and a 5% variance in the actual use of generative AI tools was explained by the adoption intentions. The construct trust fully mediated the nexus between adoption intentions to use generative AI tools and actual use. All constructs, except hedonic motivation, statistically significantly influenced the adoption intentions of generative AI tools. In turn, adoption intentions to use generative AI tools influenced the actual use of generative AI tools. A study involving Hyderabad IT industry employees revealed that they could adopt useful technology to improve performance. Received: 10 May 2025 | Revised: 13 August 2025 | Accepted: 9 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://figshare.com/s/cad95b195de4aa53e43a. Author Contribution Statement KDV Prasad: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft. Shivoham Singh: Conceptualization, Methodology, Writing – original draft. Ved Srinivas: Formal analysis. Hemant Kothari: Validation, Resources, Writing – review & editing, Supervision, Project administration. Ankita Pathak: Methodology, Investigation, Data curation, Visualization. Devendra Shrimali: Investigation, Resources, Writing – review & editing.
AI-Driven Augmented Software Engineering: Leveraging Cognitive Models for Enhanced Code Generation
Artificial intelligence (AI) is transforming the software engineering landscape that allows for new development approaches. This paper proposes a framework that integrates cognitive models with AI-driven code generation to enhance the software development process. By leveraging cognitive principles, the proposed system performs human-like decision-making to optimize code generation, refactor existing code, and fix bugs. The framework was evaluated based on code quality, developer productivity, usability, and system adaptability. Results demonstrate improvements by AI-driven system such as speed of code generation increased by 10% compared with human-written baseline and complexity reduced by 15% compared with human-generated code. Developers using the system reported a 25–29% reduction in task completion time, and errors were minimized by 60–67%. Usability feedback indicated that the system integrated seamlessly into developers’ workflows but requires further development, including enhanced personalization and a better understanding of complex code contexts. This study highlights the potential of AI-driven systems to assist developers in producing high-quality software more efficiently and provides a foundation for future research in AI-enhanced software engineering tools. Received: 10 May 2025 | Revised: 24 September 2025 | Accepted: 11 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the CodeSearchNet repository at https://github.com/github/CodeSearchNet. Author Contribution Statement Ameen Shaheen: Conceptualization, Software, Writing – original draft, Visualization. Mohammad Al Khaldy: Methodology, Writing – review & editing, Supervision, Project administration. Wael Alzyadat: Validation, Formal analysis, Resources, Writing – review & editing. Aysh Alhroob: Investigation, Data curation, Writing – review & editing.