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Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity

Alexandria Engineering Journal 2025年 127卷 522-540页

作者： Alzahrani, Abdulrahman Kostkova, Patty Alshammari, Hamoud Habibullah, Safa Alzahrani, Ahmed Department of Information System and Technology College of Computer Science and Engineering University of Jeddah Saudi Arabia University College London United Kingdom Department of Information systems College of Computer and Information Sciences Jouf University Saudi Arabia

Artificial Intelligence (AI) and the Internet of Things (IoT) are developing so fast that they can bring revolutionary changes in ecological sustainability, public health, and community welfare. In contrast, the present waste management system has a set of inefficiencies due to some challenges, such as poor waste stream segregation, limited real-time data analysis, and negligible integration of recent technology. These challenges lead to environmental degradation, public health hazards, and inefficient usage of resources. This research targets these challenges by designing an IWM framework like AI-IoT for smart waste management. The system employs AI models powered by IoT sensors for efficient waste collection, classification, and optimization of recycling schedules. CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. Bidirectional Encoder Representations from Transformers (BERT) allow multilingual users to interact and communicate properly in any linguistic environment. Data collected from IoT-enabled smart bins is transmitted in real-time to a central control system for dynamic decision-making and follow-up analysis. A pilot exercise to verify the system's effectiveness was implemented in metropolitan settings to show the transformation: landfill dependency was decreased by 30 %, recycling efficiency was greatly increased to 90 %, and thus the cost of waste management was optimized. At the same time, environmental health inequity, causing pathogen-related threats, was reduced by 35 %. The model has an accuracy of 96.8 %. The features of the proposed framework not only provide solutions to the existing inefficiencies but also enhance scalability, cost-effectiveness, and global environmental standardization. This dawns the futuristic growth of AI- and IoT-enabled waste management systems, which hinge on sustainability, public health, and resource efficienc

关键词： Artificial Intelligence Internet of Things BERT Medical Services Res-Net Waste Management

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Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

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Multimedia Tools and Applications 2025年第1期84卷 1-36页

作者： Singh, Pratima Tripathi, Mukesh Kumar Patil, Mithun B. Shivendra Neelakantappa, Madugundu Department of Computer Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering N K Orchid College of Engineering & Technology Maharastra Solapur India Department of Computer Application D. K. College Dumraon Bihar Buxar India Department of Information Technology Vasavi College of Engineering Telangana Hyderabad India

In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. This paper proposes a novel multimodal emotion recognition model that employs a hybrid model with AMIG-based feature fusion on facial and EEG feature sets. The EEG signal and facial image are subjected to preprocessing to remove unwanted background noises with the Butterworth filter and Viola Jones Algorithm, respectively. While considering the pre-processed EEG signal, features such as EWFS-transform, wavelet features, and CSP-based features are extracted. In the proposed EWFS-transform, the window function is modified by using the frequency function and updated STFT. Conversely, the features including SE-AMM-EST-based features;LGXP and GLCM are extracted while considering the preprocessed face image. In the proposed SE-AMM-EST-based features, the mean of shape is updated and covariance in PCA for extracting texture-based features. In order to extract redundant free essential features, AMIG-based feature fusion is proposed. Further, the fused features are fed into a proposed hybrid model that includes LSTM and AML-CNN models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

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Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

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computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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Detection of Driver Drowsiness Using Adaptive Eye Characteristic Ratio for Enhanced Road Safety

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IEEE Access 2025年 13卷 84313-84328页

作者： Lamba, Puneet Singh Jain, Rimjhim Khan, Shakir Alanazi, Sultan M. Jain, Achin Zamani, Abu Taha Panwar, Arvind Abdallah, Alsadig Mohammed Adam University of Delhi S.G.T.B Khalsa College Department of Computer Science Delhi110007 India University of North Carolina Charlotte United States Imam Mohammad Ibn Saud Islamic University College of Computer and Information Sciences Information Technology Department Riyadh Saudi Arabia Northern Border University Department of Department of Computer Science Faculty of Science Arar Saudi Arabia Bharati Vidyapeeth’s College of Engineering Department of Information Technology New Delhi India Galgotias University School of Computer Science and Engineering Uttar Pradesh Greater Noida India

Every year, countless people lose their lives in serious car accidents, and drowsy driving is a major cause. However, because the earliest indications of exhaustion can be identified before a dangerous scenario develops, the detection and signaling of driver drowsiness are ongoing research topics. This study presents a real-time detection system designed to detect driver’s sleep and deliver timely warnings. The mechanism employs an eye monitoring system that uses eye feature points to detect whether a driver’s eye is tired and delivers an alert if the driver is drowsy. The proposed work recognizes the driver’s face by collecting facial landmarks and gives warning to the driver to avoid crashes by performing adaptive thresholding and drowsiness diagnoses based on Eye Characteristic Ratio. The empirical results show that using random forest classifier, decision tree classifier, KNN classifier, and multinomial classifier, the proposed model achieved 90.48%, 89.44%, 88.63%, and 84.32% accuracy, respectively. © 2013 IEEE.

关键词： Highway accidents

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Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

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IEEE Access 2024年 12卷 514-533页

作者： Taherinavid, Siavash Moravvej, Seyed Vahid Chen, Yen-Lin Yang, Jing Ku, Chin Soon Yee, Por Lip Iran University of Science and Technology School of Civil Engineering Tehran13114-16846 Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve

关键词： Smartphones

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Pneumonia Net: Pneumonia Detection and Categorization in Chest X-ray Images

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Recent Advances in computer science and Communications 2024年第3期17卷 20-31页

作者： Srivastava, Somya Verma, Seema Das, Nripendra Narayan Sharma, Shraddha Dubey, Gaurav Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science and Engineering Guru Gobind Singh Indraprastha University Delhi India Department of Information Technology Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science Pandit Deendayal Energy University Gujarat India Department of Computer Science KIET Group of Institutions Ghaziabad India

Background: Pneumonia is one of the leading causes of death and disability due to respiratory infections. The key to successful treatment of pneumonia is in its early diagnosis and correct classification. PneumoniaNet is a unique deep-learning model based on CNN for identifying pneumonia on chest X-rays. Objective: A deep learning model that combines convolutional, pooling, and fully connected layers is presented in this study. Methods: In order to learn how to identify cases of pneumonia and healthy controls on chest X-ray pictures, PneumoniaNet was trained on a large labeled library of such images. A robust data augmentation technique was adopted to enhance the model generalization and training set diversity. Standard measures like as accuracy, precision, recall, and F1-score were applied to PneumoniaNet's performance evaluation. Results: The suggested model performed effectively in detecting pneumonia cases with an accuracy of 93.88%. Conclusion: The model was evaluated against the current state-of-art methods and showed that PneumoniaNet outperformed the other models. © 2024 Bentham science Publishers.

关键词： Deep learning

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Blockchain-Enabled Edge Computing Techniques for Advanced Video Surveillance in Autonomous Vehicles

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computers, Materials & Continua 2025年第4期83卷 1239-1255页

作者： Mohammad Tabrez Quasim Khair Ul Nisa Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBishaP.O.Box 551Saudi Arabia

The blockchain-based audiovisual transmission systems were built to create a distributed and flexible smart transport system(STS).This system lets customers,video creators,and service providers directly connect with each ***-based STS devices need a lot of computer power to change different video feed quality and forms into different versions and structures that meet the needs of different *** the other hand,existing blockchains can’t support live streaming because they take too long to process and don’t have enough computer *** amounts of video data being sent and analyzed put too much stress on networks for vehicles.A video surveillance method is suggested in this paper to improve the performance of the blockchain system’s data and lower the latency across the multiple access edge computing(MEC)*** integration of MEC and blockchain for video surveillance in autonomous vehicles(IMEC-BVS)framework has been *** deal with this problem,the joint optimization problem is shown using the actor-critical asynchronous advantage(ACAA)method and deep reinforcement training as a Markov Choice Progression(MCP).Simulation results show that the suggested method quickly converges and improves the performance of MEC and blockchain when used together for video surveillance in self-driving cars compared to other methods.

关键词： Blockchain multiple access edge computing video surveillance autonomous vehicles

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PBCounter:weighted model counting on pseudo-boolean formulas

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Frontiers of computer science 2025年第3期19卷 55-63页

作者： Yong LAI Zhenghang XU Minghao YIN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China School of Computer Science and Information Technology Northeast Normal UniversityChangchun 130017China

In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its *** current WMC solvers work on Conjunctive Normal Form(CNF)***,CNF is not a natural representation for human-being in many *** by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB *** on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool *** compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF *** experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.

关键词： weighted model counting pseudo-boolean constraint algebraic decision diagrams preprocessing techniques

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and Systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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Incorporating syntax information into attention mechanism vector for improved aspect-based opinion mining

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Neural Computing and Applications 2024年第22期36卷 13957-13974页

作者： Aziz, Makera Moayad Yaakub, Mohd Ridzwan Bakar, Azuraliza Abu Computer Science Department College of Computer Science and Mathematics University of Mosul Nineveh Mosul41002 Iraq Center for Artificial Intelligence Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia

In Aspect-based Sentiment Analysis (ABSA), accurately determining the sentiment polarity of specific aspects within text requires a nuanced understanding of linguistic elements, including syntax. Traditional ABSA approaches, particularly those leveraging attention mechanisms, have shown effectiveness but often fall short in integrating crucial syntax information. Moreover, while some methods employ Graph Neural Networks (GNNs) to extract syntax information, they face significant limitations, such as information loss due to pooling operations. Addressing these challenges, our study proposes a novel ABSA framework that bypasses the constraints of GNNs by directly incorporating syntax-aware insights into the analysis process. Our approach, the Syntax-Informed Attention Mechanism Vector (SIAMV), integrates syntactic distances obtained from dependency trees and part-of-speech (POS) tags into the attention vectors, ensuring a deeper focus on linguistically relevant elements. This not only substantially enhances ABSA accuracy by enriching the attention mechanism but also maintains the integrity of sequential information, a task managed by adopting Long Short-Term Memory (LSTM) networks. The LSTM’s inputs, consisting of syntactic distance, POS tags, and the sentence itself, are processed to generate a syntax vector. This vector is then combined with the attention vector, offering a robust model that adeptly captures the nuances of language. Moreover, the sequential processing capability of LSTM ensures minimal information loss across the text by preserving the context and dependencies inherent in the sentence structure, unlike traditional pooling methods. Our experimental findings demonstrate that this innovative combination of SIAMV and LSTM significantly outperforms existing GNN-based ABSA models in accuracy, thereby setting a new standard for sentiment analysis research. By overcoming the traditional reliance on GNNs and their pooling-induced information loss, our method

关键词： Sentiment analysis

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