Artificial Intelligence and Applications

Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes

Maryam Asadzadehkaljahi, Arnab Halder, Umapada Pal, Shivakumara Palaiahnakote — Wed, 08 Feb 2023 00:00:00 +0800

Classification of arbitrary moving objects including vehicles and human beings in a real environment (such as protected and sensitive areas) is challenging due to arbitrary deformation and directions caused by shaky camera and wind. This work aims at adopting a spatiotemporal approach for classifying arbitrarily moving objects. The intuition to propose the approach is that the behavior of the arbitrary moving objects caused by wind and shaky camera is inconsistent and unstable, while, for static objects, the behavior is consistent and stable. The proposed method segments foreground objects from background using the frame difference between median frame and individual frame. This step outputs several different foreground information. The method finds static and dynamic edges by subtracting Canny of foreground information from the Canny edges of respective input frames. The ratio of the number of static and dynamic edges of each frame is considered as features. The features are normalized to avoid the problems of imbalanced feature size and irrelevant features. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples, and the random forest classifier is used for the final classification of frames with static objects and arbitrarily moving objects. For evaluating the proposed method, we construct our own dataset, which contains video of static and arbitrarily moving objects caused by shaky camera and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance (76% classification rate) which is 14% better than the best existing method.

Received: 8 November 2022 | Revised: 30 December 2022 | Accepted: 17 January 2023

Conflicts of Interest

Palaiahnakote Shivakumara is the editor-in-chief and Umapada Pal is an advisory board member for Artificial Intelligence and Applications, and were not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data available on request from the corresponding author upon reasonable request.

Towards AI-Based Condition Monitoring and Predictive Maintenance for Water Smart Pipes: The SANDMAN Approach

Wed, 27 Dec 2023 00:00:00 +0800

Pipes age and corrosion are the main factors of leakage in water distribution networks. According to theWorld Resources Institute, European countries will face water problems by 2040. If we take Italy as an example, more than 40% of drinking water was lost in 2020 due to leaky aqueducts. Decrepit pipes can lead to environmental concerns, economical losses, and potential public health problems if water gets contaminated. Localizing leakage positions in an accurate way is often a big challenge. On the other side, replacing decrepit pipes is not an easy task and usually costly. An optimal solution to deal with water leakage is to use smart pipes where appropriate sensors monitoring the conditions of the pipes are incorporated in. Digitalization plays a crucial role here. By providing accurate information about the pipes and using artificial intelligence techniques for data analysis, potential leakages and their corresponding positions can be detected in time, which allows to schedule a maintenance task as soon as possible. The current paper discusses the use of smart pipes combined with predictive maintenance and shows how this combination improves water leakage detection, hence minimizing water waste and protecting the environment. The solution was validated in an experimental setup put in place by the Italian company EKSO S.R.L in its factory facilities in Rozallo, Italy. The obtained results show the feasibility of the solution and the relevance of using artificial intelligence techniques for predicting degradation in smart pipes.

Received: 14 August 2023 | Revised: 12 December 2023 | Accepted: 14 December 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data available on request from the corresponding author upon reasonable request.

Implications of Classification Models for Patients with Chronic Obstructive Pulmonary Disease

Mengyao Kang, Jiawei Zhao, Farnaz Farid — Thu, 14 Sep 2023 00:00:00 +0800

Machine learning (ML)-based prediction models have the potential to revamp various industries, and one such promising area is healthcare. This study demonstrates the potential impact of ML on healthcare, particularly in managing patients with chronic obstructive pulmonary disease (COPD). The experimental results showcase the remarkable performance of ML models, surpassing doctors’ predictions for COPD patients. Among the evaluated models, the gradient-boosted decision tree classifier emerges as the top performer, displaying exceptional classification accuracy, precision, recall, and F1-score compared to doctors’ experience. Notably, the comparison between the best ML model and doctors’ predictions reveals an interesting pattern: ML models tend to be more conservative, resulting in an increased probability of patient recovery.

Received: 25 July 2023 | Revised: 27 August 2023 | Accepted: 11 September 2023

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 PLOS ONE at https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188532.

Supportive Environment for Better Data Management Stage in the Cycle of ML Process

Lama Alkhaled, Taha Khamis — Thu, 10 Aug 2023 00:00:00 +0800

The objective of this study is to explore the process of developing artificial intelligence and machine learning (ML) applications to establish an optimal support environment. The primary stages of ML include problem understanding, data management (DM), model building, model deployment, and maintenance. This paper specifically focuses on examining the DM stage of ML development and the challenges it presents, as it is crucial for achieving accurate end models. During this stage, the major obstacle encountered was the scarcity of adequate data for model training, particularly in domains where data confidentiality is a concern. The work aimed to construct and enhance a framework that would assist researchers and developers in addressing the insufficiency of data during the DM stage. The framework incorporates various data augmentation techniques, enabling the generation of new data from the original dataset along with all the required files for detection challenges. This augmentation process improves the overall performance of ML applications by increasing both the quantity and quality of available data, thereby providing the model with the best possible input.

Received: 16 June 2023 | Revised: 24 July 2023 | Accepted: 9 August 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.

Mask YOLOv7-Based Drone Vision System for Automated Cattle Detection and Counting

Rotimi-Williams Bello, Mojisola Abosede Oladipo — Wed, 17 Jan 2024 00:00:00 +0800

Conventional method of counting animals is one of the most challenging tasks in livestock management; moreover, counting of animals in drone-acquired imagery, though promising, is more challenging in intelligent livestock management. In this paper, we apply state-of-the-art object detection model, Mask YOLOv7, for detection and counting of cattle in different scenarios such as in controlled (feedlot) environment and uncontrolled (open-range) environment. Mask mechanism was embedded into the backbone of the YOLOv7 algorithm (Mask YOLOv7) for instance segmentation of individual cattle object. We evaluate the performance of the model proposed in this study using Intersection over Union threshold of 0.5, average precision (AP), and mean average precision. The results of the experiment conducted in this study show that the proposed model achieves an accuracy of 93% in counting cattle in controlled environment and 95% in uncontrolled environment. These results affirm the potential of the model, Mask YOLOv7, to perform competitively with any other existing object detection and instance segmentation models in terms of accuracy and AP especially when the speed of object detection matters. Moreover, the research has potential applications in livestock inventory, which helps in tracking, monitoring, and reporting vital information about individual cattle.

Received: 29 August 2023 | Revised:19 December 2023 | Accepted: 16 January 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.

University Auto Reply FAQ Chatbot Using NLP and Neural Networks

Sun, 25 Jun 2023 00:00:00 +0800

When new students enter college, they often have similar questions – “Where to study for this subject?,” “How to prepare Data Structures and Algorithms?,” “How to connect with seniors?,” and so on. The use of chatbots can help them get answers to their questions quickly and efficiently. This study proposes a deep learning (DL) chatbot for addressing common doubts of university students, providing efficient and accurate responses to college-specific questions. A self-curated dataset is used for the purpose of building the chatbot, and natural language processing techniques are utilized for the pre-processing of raw data gathered. The study compares two deep learning models – a bidirectional long- and short-term memory network and a simple feed-forward neural network model.

Received: 4 January 2023 | Revised: 9 May 2023 | Accepted: 9 June 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.

ERNIE and Multi-Feature Fusion for News Topic Classification

Weisong Chen, Boting Liu, Weili Guan — Thu, 26 Oct 2023 00:00:00 +0800

Traditional news topic classification methods suffer from inaccurate text semantics, sparse text features and low classification accuracy. Based on this, this paper proposes a news topic classification method based on Enhanced Language Representation with Informative Entities (ERNIE) and multi-feature fusion. A semantically more accurate representation of text embedding is obtained by ERNIE. In addition, this paper extracts word, context and key sentence based on the news text. The key sentences of the news are obtained through the TextRank algorithm, which enables the model to focus on the content points of the news. Finally, this paper uses the attention mechanism to realize the fusion of multiple features. The proposed method is experimented on BBCNews. The experimental results show that we achieve classification accuracies superior to those of the compared methods, while validating the structural validity of the proposed method. The method in this paper has a positive effect on promoting the research of news topic classification.

Received: 17 September 2023 | Revised: 10 October 2023 | Accepted: 20 October 2023

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 https://doi.org/10.1145/1143844.1143892 and https://
github.com/yao8839836/text_gcn.

Comparative Study of Suspended Sediment Load Prediction Models Based on Artificial Intelligence Methods

Cynthia Borkai Boye, Paul Boye, Yao Yevenyo Ziggah — Thu, 01 Jun 2023 00:00:00 +0800

Quantification of suspended load sediment is crucial for maintaining the ecosystem and quality of water/river bodies that serve as the habitat for many living organisms. Because the influencing factors are nonlinearly related to the suspended load sediment, it is a challenge to apply linear statistical models to predict accurately. To address such a problem, this study appliedartificial intelligence methods to simulate and predict suspended load sediment. The artificial intelligence methods are robust andcan handle adequately issues related to nonlinearity in modeling. In the present study, four artificial intelligence methods were developed to predict suspended sediment load distribution. The methods include a backpropagation neural network, group method of data handling, least squares support vector machine, and generalised regression neural network. In developing the respective models, drainage areas, river slopes, and length of rivers served as predictor variables while suspended sediment load was the response variable. The models were evaluated using the metrics of root mean square error, percentage root mean square error, uncertainty at 95%, root mean square error observations standard deviation ratio, and Legates and McCabe index. According to the results, the generalised regression neural network model achieved higher prediction accuracy than the other competing methods. The performance of the generalised regression neural network model can be attributed to its ability to calibrate and generalise appropriately to the training and testing data set. Hence, in practice, the generalised regression neural network model is proposed for suspended sediment load prediction for the study area which can be useful to policymakers and managers of water resources.

Received: 6 March 2023 | Revised: 11 May 2023 | Accepted: 26 May 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data available on request from the corresponding author upon reasonable request.

Exploring Intervention Techniques for Alzheimer’s Disease: Conventional Methods and the Role of AI in Advancing Care

Sun, 07 Apr 2024 00:00:00 +0800

Alzheimer’s disease (AD) is a neurodegenerative condition characterized by cognitive decline and functional impairment. This study compares conventional intervention techniques with emerging artificial intelligence (AI) approaches to AD. Intervention technique refers to a specific method or approach employed to bring about positive change in a particular situation. In the context of AD, such techniques are crucial as they aim to slow down the progression of symptoms, alleviate behavioral challenges, and support patients and their caretakers in managing the complexities of the condition. Conventional intervention techniques, such as cognitive stimulation and reality orientation, have demonstrated benefits in improving cognitive function and emotional well-being. Conventional intervention approaches are widely preferred as they have a proven track record of effectiveness, personalized response, cost-effectiveness, and patient-centered care. Despite these benefits, they are limited by individual variability in response and long-term effectiveness. On the other hand, AI-based approaches such as computer vision and deep learning hold the potential to revolutionize Alzheimer’s interventions. These technologies offer early detection, personalized care, and remote monitoring capabilities. They can provide tailored interventions, assist decision-making, and enhance caregiver support. Although AI-based interventions face challenges such as data privacy and implementation complexity, their potential to transform Alzheimer’s care is significant. This research paper compares conventional and AI-based approaches. It reveals that while traditional techniques are well established and have proven benefits, AI-based interventions offer novel opportunities for personalized and advanced care. Combining the strengths of both approaches may lead to more comprehensive and effective interventions for individuals with AD. Continued research and collaboration are crucial to harness the full potential of AI in improving Alzheimer’s care and enhancing the quality of life for affected individuals and their caregivers.

Received: 19 January 2024 | Revised: 30 March 2024 | Accepted: 2 April 2024

Conflicts of Interest
The authors declare that they have no conflicts of interest in this work.

Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.