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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xu, Jinpeng Zhao, Chunna Yang, Jing Huang, Yaqun Yang, Yaoyuan Yee, Por Lip School of Information Science & Engineering Yunnan University Kunming China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350368741

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data;2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model. © 2025 IEEE.

关键词： dynamic graph learning Fractional Residual decomposition spatial-temporal graph Traffic prediction

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An empirical study to test the significant effect of bellwethers on predictive modeling in smart homes

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Journal of Data, information and Management 2024年第2期6卷 111-123页

作者： Mensah, Solomon Kudjo, Patrick Kwaku Brown, Selasie Aformaley Dick, Elizabeth Akuafum Koffie-Ocloo, Dzordzoe Woelorm Department of Computer Science University of Ghana Legon Accra Ghana Department of Information Technology Wisconsin International University College Accra Ghana Department of Information Technology University of Professional Studies Accra Ghana Department of Information Technology Ghana Communication Technology University Accra Ghana

Smart homes are envisioned and expected to be furnished with effective and reliable sensors for constant monitoring, sensing, and actuating the environment. Data from these sensors can be analyzed and used for the provision of various services by automating common tasks undertaken in the homes with minimal disruption to human activities. Thus, in order to provide these services, there is the need to obtain an intelligent system to accurately predict the most likely event to occur in a smart home. These intelligent systems rely on prediction algorithms trained from existing data for their functioning. The study seeks to invesigate the feasibility and significance of using Bellwethers for predictive modelling in smart homes. Recent works in the domain of software engineering – software defect and effort estimation have shown that the use of exemplary data (referred to as Bellwether) in the training set relatively improves prediction accuracy. These results were obtained based on an empirical analysis of the Bellwether effect i.e., the existence of Bellwethers in a given dataset. Existing studies in the context of smart homes focus on using all data from various sensors for training and validating the prediction algorithms for intelligent systems in smart homes. This mode of training and validating the prediction algorithms does not always yield accurate prediction results due to the variations and heterogeneous nature of the population dataset. A novel framework based on Markov chains and Deep learning algorithms is constructed to sample Bellwethers for predictive modelling in smart homes. It was observed that the sampled Bellwethers yielded improved prediction accuracy for the studied dataset as compared to using all data instances or population set (growing portfolio). It was evident that Bellwethers can improve the reliability and accuracy of intelligent systems built for smart homes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Bellwether Intelligent systems Internet of Things Prediction Smart Homes

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IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

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computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： Saeed Masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.Alzahrani Mohammed Maray Sana Alazwari Mohamed A.Shamseldin Mesfer Al Duhayyim Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)*** presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RSO algorithm is exploited for effective selection of subset of ***,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of ***,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

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Image Caption Generation Using Deep Learning 12th

Image Caption Generation Using Deep Learning

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12th International Conference on Big Data Analytics, BDA 2024

作者： Mehta, Shikha Baranwal, Honey Saxena, Sarvagya Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida100190 India

ISBN: (纸本)9783031861925

Artificial intelligence (AI) often lacks cultural diversity with limited representation of non-Western contexts which results in biased outputs. Addressing this gap, this paper presents a deep learning-based image captioning system specifically for Indian deities, a culturally rich but underrepresented subject in AI research. Due to the lack of benchmark datasets for such a culturally significant imagery, this work introduces the DIVINE 1K dataset. This dataset contains 1,000 images of Indian deities, each annotated with five manually curated captions. To develop an accurate caption model we evaluated several deep learning architectures such as VGG16, ResNet50, ResNet152, InceptionV3, MobileNetV2, and DenseNet201 for feature extraction. Performance is assessed through BLEU, ROUGE, and METEOR scores, with DenseNet201 emerging as the most effective model. This optimized model is subsequently deployed within a mobile application, enabling real-time caption generation for images of Indian deities. This work not only contributes a specialized dataset but also advances culturally inclusive AI by pioneering automated image captioning tailored to Indian iconography, thus bridging a critical gap in AI cultural representation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature extraction

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Stress Analysis Prediction for Coma Patient Using Machine Learning 5th

Stress Analysis Prediction for Coma Patient Using Machine Le...

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5th International Conference on Data and information sciences, ICDIS 2023

作者： Alwin Infant, P. Charulatha, J. Sadhana, G. Ragavendra, K. Computer Science and Engineering Panimalar Engineering College Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819969050

Today’s working IT professionals frequently struggle with stress issues. The patient is now more likely to experience stress due to changing lifestyle and workplace cultures. Even while many companies and sectors deliver welfare programs and also make efforts to enhance work environments, the issue remains off from grasp. In this paper, we will utilize various techniques to analyze the stress patterns of working people and identify the variables that exert a massive effect on the system's distress. By utilizing machine learning techniques to forecast models to predict the threats of stressor encountered prognosis through using machine learning, we hope to streamline this procedure. In the working class, mental health illnesses linked to stress are not unusual. Concerns about the same have previously been highlighted by several researchers. In order to do this, information from coma patient answers from working medical experts’ mental health data was taken into consideration. After data extraction and preprocessing, various machine learning approaches were employed to supervise our framework. In this case, the mechanism can apply the machine learning techniques such as decision tree, random forest, KNN, logistic regression, and Naive Bayes. Results of the experiments demonstrated the system’s improved performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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Advanced Hydro-Informatic Modeling through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the computer Society 2025年第1期6卷 726-738页

作者： Mahir, Shahariar Hossain Tashrif, Md Tanjum An Karim, Md Ahsan Kundu, Dipanjali Rahman, Anichur Hamza, Md. Amir Farid, Fahim Al Miah, Abu Saleh Musa Mansor, Sarina Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Multimedia University Faculty of Engineering Cyberjaya63100 Malaysia Department of CSE Nilphamari Bangladesh

Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylhet, Bangladesh where transboundary water flows and climate change have increased the risk of disasters. Accurate flood detection plays a vital role in mitigating these impacts by allowing timely early warnings and strategic planning. Recent advancements in flood prediction research include the development of robust, accurate, and low-cost flood models designed for urban deployment. By applying and utilizing powerful deep learning models show promise in improving the accuracy of prediction and prevention. But those models faced significant issues related to scalability, data privacy concerns and limitations of cross-border data sharing including the inaccuracies in prediction models due to changing climate patterns. To address this, our research adopts the Federated Learning (FL) framework in an effort to train state-of-the-art deep learning models like Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Feed-Forward Neural Network (FNN) and Temporal Fusion Transformer-Convolutional Neural Network (TFT -CNN) on a 78-year dataset of rainfall, river flow, and meteorological variables from Sylhet and its upstream regions in Meghalaya and Assam, India. This approach promotes data privacy and allows collaborative learning while working under cross-border data-sharing constraints, therefore improving the accuracy of prediction. The results showed that the best-performing FNN model achieved an R-squared value of 0.96, a Mean Absolute Error (MAE) value of 0.02, Percent bias (PBIAS) value of 0.4185 and lower Root Mean Square Error (RMSE) in the FL environment. Explainable AI techniques, such as SHAP, sheds light on the most significant role played by upstream rainfall and river dynamics, particularly from Cherrapunji and the Surma-Kushiyara river system, in d

关键词： Convolutional neural networks

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Performance Evaluation of Machine Learning Approaches in Detecting IoT-Botnet Attacks

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 136-146页

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Mughaid, Ala Taamneh, Salah Wedyan, Fadi I. Obiedat, Mamoon Department of Computer Science and Applications Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Information Technology Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Engineering Computing and Mathematical Sciences Lewis University RomeovilleIL United States

Botnets are today recognized as one of the most advanced vulnerability threats. Botnets control a huge percentage of network traffic and PCs. They have the ability to remotely control PCs (zombie machines) by their creator (BotMaster) via Command and Control (C&C) framework. They are the keys to a variety of Internet attacks such as spams, DDOS, and spreading malwares. This study proposes a number of machine learning techniques for detecting botnet assaults via IoT networks to help researchers in choosing the suitable ML algorithm for their applications. Using the BoT-IoT dataset, six different machine learning methods were evaluated: REPTree, RandomTree, RandomForest, J48, metaBagging, and Naive Bayes. Several measures, including accuracy, TPR, FPR, and many more, have been used to evaluate the algorithms’ performance. The six algorithms were evaluated using three different testing situations. Scenario-1 tested the algorithms utilizing all of the parameters presented in the BoT-IoT dataset, scenario-2 used the IG feature reduction approach, and scenario-3 used extracted features from the attacker’s received packets. The results revealed that the assessed algorithms performed well in all three cases with slight differences. © 2023 by the authors of this article. Published under CC-BY.

关键词： Internet of things

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DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm

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Intelligent Automation & Soft Computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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Two-stage IDS for IoT using layered machine- and deep-learning models

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Cyber-Physical systems 2024年第1期10卷 60-83页

作者： van der Walt, André Quazi, Tahmid van Niekerk, Brett School of Engineering University of KwaZulu-Natal Durban South Africa School of Mathematics Statistics and Computer Science University of KwaZuluNatal South Africa Department of Information Technology Durban University of Technology South Africa

The ever-growing integration of Internet-of-Things (IoT) devices into our daily lives provides us with a level of convenience never before seen. However, the effects of attacks on these devices can be devastating. The discrete, low-powered nature of IoT devices makes their security a difficult problem to solve. To provide a solution, this work proposes a two-stage Intrusion Detection systems (IDS) using layered machine and deep learning models. The potential benefits of the system are examined and the results presented show a reduction of threat detection/identification time of 0.51 s on average and an increase of threat classification F1-Score by 0.05. . © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of things

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The Role of the Internet of Things in Monitoring Air Pollution-A Survey 11

The Role of the Internet of Things in Monitoring Air Polluti...

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11th International Conference on information technology, ICIT 2023

作者： Abusukhon, Ahmad Alghannam, Bareeq Al-Zaytoonah University of Jordan Faculty of Science and Information Technology Computer Science Dep. Amman Jordan College of Business Studies the Public Authority for Applied Education and Training Department of Computer Science and Information System Kuwait City Kuwait

ISBN: (纸本)9798350320060

Air pollution is one of the most common problems that the world is facing today. In fact, there are numerous causes of air pollution, including the large number of industries and automobiles that emit carbon dioxide (CO2) and carbon monoxide. Pollution is increasing on a daily basis, making air quality monitoring and management a major challenge for governments in both developed and developing countries. Air pollution affects human health as well as the atmosphere, causing climate change. One of the techniques used for monitoring air pollution is the Internet of Things (IoT). In this paper, we demonstrate various techniques for monitoring air quality using IoT and make a comparison based on various factors elicited from the previous work. © 2023 IEEE.

关键词： Internet of things

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