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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Ibrahiem, Soha S. El-Sayed Mohammed, Bahy Ibrahim El Sayed Ahmed, Mohammed Ahmed Elbahnasy, Khaled Menofia University Computality R&D System Analyst & Nlp Researcher Faculty of Science Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Jeddah International College Computer Science and Information Technology Department Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

Natural language in social media is often ambiguous, carrying diverse perspectives that are critical for opinion analysis. With vast amounts of unstructured data, particularly on platforms like Twitter, it is impractical to manually analyse relevant insights. This rapid data growth has highlighted the need for intelligent filtering and mining techniques in decision support systems to help organizations build brand reputation, foster online communities, and enhance profitability. Multi-Emotion detection is key for capturing implicit user opinions and specific emotional states;however, challenges such as text bipolarity, limited representative feature vectors, and inadequate annotated lexicons often hinder accuracy. This research aims to improve multi-emotion detection accuracy by extracting emotion implied tokens and converting them into feature vectors that capture semantic relevance. Using annotated lexicons, word embeddings, unigrams, and term frequency, these vectors are trained across five machine learning models, which are Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K- nearest neighbor (KNN), and Multi- layer Percepton (MLP). Finally, two ensemble techniques are applied to refine model outputs, achieving an improvement accuracy over previous work worked. The proposed system, evaluated on a real dataset, achieves a hamming score of 0.55 and an average F1 score of 0.68, demonstrating its potential for effective multi-label emotion detection. © 2025 IEEE.

关键词： Tweets

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Smart Automated Zebra Crossing system Using PIR Sensors, IoT Integration, and IP Camera-Based Headcount for Enhanced Road Safety

Smart Automated Zebra Crossing System Using PIR Sensors, IoT...

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International Conference on computer and Communications Technologies (ICCCT)

作者： M.kumar M.E G. Adiline Macriga Department of Information Technology Computer Science and Engineering (with specialization in networks) Sri Sairam Engineering College Department of Information Technology Sri Sairam Engineering College

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The Smart Automated Zebra Crossing system operationalizes Passive Infrared (PIR) sensors and Internet of Things (IoT) technology with IP camera-based headcount capabilities to foster pedestrian safety. A data-driven control system protected pedestrian safety by integrating adaptive management structures and intelligent traffic methods. The system actively switches on its crossing systems only after PIR sensors detect approaching pedestrians. IP cameras enabled by advanced computer vision algorithms provide real-time pedestrian counting and density measurement services. networks based on IoT technology carry system data to produce shifting crosswalk time lengths through dynamic adjustments based on variations in pedestrian numbers. Smart LED warning indicators inform drivers about traffic while adaptive timing systems shorten the time pedestrians and drivers need to wait. Real-time decision capabilities and reduced web dependence emerge from the system's edge computing architecture which ensures operational reliability. Through solar power technology all system components underwent upgrades to become durable in varying weather conditions and maintain sustainable operation in any environment. The gathered system data helps analytical experts predict dangerous traffic areas and peak times which supports better precautionary traffic management and future infrastructure development strategies. This modern framework takes pedestrian safety to new heights while efficiently utilizing roads through combination of real-time detection systems and dynamic adaptiveness with data analytical features. Researchers studied architectural elements of the system together with implementation methods and performance results in order to show how this technology will transform smart transportation infrastructure in the future.

关键词： Pedestrians Adaptive systems Traffic control Sensor systems Road safety Safety IP networks Vehicle dynamics Intelligent sensors Vehicles

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Supervised Machine Learning-Based Prediction of COVID-19

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computers, Materials & Continua 2021年第10期69卷 21-34页

作者： Atta-ur-Rahman Kiran Sultan Iftikhar Naseer Rizwan Majeed Dhiaa Musleh Mohammed Abdul Salam Gollapalli Sghaier Chabani Nehad Ibrahim Shahan Yamin Siddiqui Muhammad Adnan Khan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information System College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of CIT Faculty of Applied StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Directorate of IT The Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Minhaj University LahoreLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan

COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the *** yet,no satisfactory vaccine or medication has been developed that could guarantee its mitigation,though several efforts and trials are *** around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely *** this regard,healthcare experts,researchers and scientists have delved into the investigation of existing as well as new *** situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this *** state-of-the-art research in Artificial intelligence(AI),Machine learning(ML)and cloud computing have encouraged healthcare experts to find effective detection *** study aims to provide a comprehensive review of the role of AI&ML in investigating prediction techniques for the COVID-19.A mathematical model has been formulated to analyze and detect its potential *** proposed model is a cloud-based smart detection algorithm using support vector machine(CSDC-SVM)with cross-fold validation *** experimental results have achieved an accuracy of 98.4%with 15-fold cross-validation *** comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.

关键词： COVID-19 CSDC-SVM artificial intelligence machine learning cloud computing support vector machine

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and Communication Engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Balance Multi-Head Attention based on Software and Hardware Co-design 9

Balance Multi-Head Attention based on Software and Hardware ...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Xu, Dian Hu, Wei Liu, Fang Fan, Zimeng Shi, Qingsong Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science Wuhan China Wuhan University Department of Information Engineering Wuhan Institute of City School of Computer Science Wuhan China Zhejiang University College of Computer Science Hangzhou China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Recently, the Transformer-based models have achieved leading results in many research areas such as natural language processing and computer vision. However, since Transformer-based models have a huge computational complexities, optimization for Transformer is the focus of current research. the core of Transformer models is the attention module, and in this paper we propose a software and hardware co-designed attention module, which reduces the computational units of the multi-head attention module by balancing the The inference time of the attention module is reduced by balancing the computational units of multiple attention modules. At the same time, we design the corresponding structure on FPGA, which accelerate 14.6x inference time in comparison with the original attention module implementation on GPU. We applied this attention module to multiple visual Transformer models and tested the accuracy difference between the balanced multi-head attention module-based model and the original model on CIFAR-10, and obtained a result of 80.46%, which lower from the original version by 0.18%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Comprehensive Study on Drones and Big Data for Supply Chain Optimization Using a Novel Approach 1

A Comprehensive Study on Drones and Big Data for Supply Chai...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dankan Gowda, V. Palanikkumar, D. Suganthi, N. Malleswari, A.S. Thapa, Sanjog Tanguturi, Rama Chaithanya Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Dr Ngp Institute of Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Dayananda Sagar College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Lamar University Department of Information System and Analysis BeaumontTX United States Woxsen University School of Business Hyderabad India

ISBN: (纸本)9798350367720

This research paper aims to provide qualitative and quantitative insights regarding the application of drones and big data techniques in the SCM field through a new scientific method. The proposed method exploits the opportunities of drones for real-time data capturing and big data technologies to enrich decision making in supply chain. The study is pertinent to modern tendencies in supply chain systems and proves the efficiency of the new method through the precise calculations and contrastive analysis. The following are some of the findings that suggest efficiency, cost decrease, and outstanding performance of the supply chain. The studies being offered in this research will prove beneficial for practitioners and researchers who endeavor to advance the concepts and practice of logistics and supply chain management. © 2024 IEEE.

关键词： Big Data Cost Reduction Data Analytics Decision-making Drones Efficiency Improvement Logistics Novel Approach Real-time Data Collection Supply Chain Optimization

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When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework

When Computing Power Network Meets Distributed Machine Learn...

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International Workshop on Quality of Service

作者： Xinjing Yuan Lingjun Pu Lei Jiao Xiaofei Wang Meijuan Yang Jingdong Xu Institute of Systems and Networks College of Computer Science Nankai University China Department of Computer Science University of Oregon USA School of Computer Science and Technology Tianjin University China

In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility with system cost, and formulate a multivariate scheduling problem that maximizes RUE by comprehensively taking client admission, model partition, server selection, routing and bandwidth allocation into account (i.e., mixed-integer fractional programming). We design Refinery, an efficient approach that first linearizes the fractional objective and non-convex constraints, and then solves the transformed problem via a greedy based rounding algorithm in multiple iterations. Extensive evaluations corroborate that CPN-FedSL is superior to the standard and state-of-the-art learning frameworks (e.g., FedAvg and SplitFed), and besides Refinery is lightweight and significantly outperforms its variants and de facto heuristic methods under a variety of settings.

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Analysis of Sleep apnea Considering Electrocardiogram Data Using Deep learning Algorithms 8

Analysis of Sleep apnea Considering Electrocardiogram Data U...

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8th International Virtual Conference on Biosignals, Images, and Instrumentation, ICBSII 2022

作者： Rajabrundha, A. Lakshmisangeetha, A. Balajiganesh, A. Electronic System Design Lab Velammal Engineering College India Department of Computer Science Engineering Velammal Engineering College India Velammal Engineering College Chennai India

Sleep is a vital component of every human being. Adequate restful and restorative sleep reenergizes the body, enhances overall health and psychological well-being. Sleep hygiene, chaotic lifestyles, disorder breathing, stress, and anxiety contribute to poor sleep quality. Obstructive sleep apnea (OSA) sleep respiratory disorder causes temporary lapses of breathing results in gasping, choking, snoring sounds during sleep. The individual does not consciously wake up, but the brain has to start breathing again which disrupts the sleep quality. Polysomnography (PSG) sleep study is employed to diagnose sleep disorders by using either in-home or laboratory-based comprehensive tests. The untreated OSA leads to deterioration in health, performance consequences with severity including daytime sleepiness, motor vehicle accidents, workplace errors, cardiovascular morbidity, and mortality. The pre-processed, interpolated and segmented ECG signal is considered for the examination of OSA. This paper focuses on three types of deep learning classifiers-based prediction models for detection of apnea from the ECG signal. The accuracy value of Long Short Term Memory model (LSTM) is 85 percent and classifier's ability to distinguish between normal and apnea events is *** Gated Recurrent Unit (GRU) classifier and Convolution Neural Network (CNN) model have an f1- score value of 0.80. The proposed LSTM model provides the optimal performance in comparison to other deep learning models used for classification with respect to area under the curve (AUC) and accuracy metrics. © Published under licence by IOP Publishing Ltd.

关键词： Electrocardiograms

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An improvised CNN model for fake image detection

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International Journal of Information Technology (Singapore) 2023年第1期15卷 5-15页

作者： Hamid, Yasir Elyassami, Sanaa Gulzar, Yonis Balasaraswathi, Veeran Ranganathan Habuza, Tetiana Wani, Sharyar Information Security and Engineering Technology Abu Dhabi Polytechnic Abu Dhabi United Arab Emirates Department of Management Information System College of Business Administration King Faisal University Riyadh Saudi Arabia Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chengalpattu India Department of Computer Science and SWE College of Info. Tech UAEU Al Ain United Arab Emirates Department of Computer Science International Islamic University Malaysia Kuala Lumpur Malaysia

The last decade has witnessed a multifold growth of image data courtesy of the emergence of social networking services like Facebook, Instagram, LinkedIn etc. The major menace faced by today’s world is the issue of doctored images, where-in the photographs are altered using a rich set of ways like splicing, copy-move, removal to change their meaning and hence demands serious mitigation mechanisms to be thought of. The problem when seen from the prism of Artificial intelligence is a binary classification one, where-in the characterization must be drawn between the original and the manipulated images. This research work proposes a computer vision model based on Convolution Neural networks for fake image detection. A comparative analysis of 6 popular traditional machine learning models and 6 different CNN architectures to select the best possible model for further experimentation. The proposed model based on ResNet50 employed with powerful preprocessing techniques results in a perfect fake image detector having a total accuracy of 0.99 having an improvement of around 18% performance with other models. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Artificial Intelligence Classification Convolution neural networks Doctored images Splicing

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Intelligent Medical Diagnostic system for Hepatitis B

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computers, Materials & Continua 2022年第12期73卷 6047-6068页

作者： Dalwinder Singh Deepak Prashar Jimmy Singla Arfat Ahmad Khan Mohammed Al-Sarem Neesrin Ali Kurdi Lovely Professional University Punjab144402India College of Computing Khon Kaen UniversityKhon Kaen40000Thailand College of Computer Science and Engineering Taibah UniversityMedina41477Saudi Arabia Information System Department Sheba Region UniversityMarib14400Yemen

The hepatitis B virus is the most deadly virus,which significantly affects the human *** termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage;otherwise,it will become a severe problem and make a human liver suffer from the most dangerous diseases,such as liver *** this paper,two medical diagnostic systems are developed for the diagnosis of this life-threatening *** methodologies used to develop thesemodels are fuzzy logic and the neuro-fuzzy *** diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both *** classification accuracy of a multilayered fuzzy inference system is 94%.The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference system(ANFIS)classifies the result corresponding to the given input is 95.55%.The comparison of both developed models on the basis of their performance parameters has been *** is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic ***,the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B *** other words,the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.

关键词： Artificial intelligence fuzzy logic hepatitis B hybrid system medical diagnostic system neural network neuro-fuzzy technique

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