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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Early Detection of Autism in Children Using Transfer Learning

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Intelligent Automation & Soft Computing 2023年第4期36卷 11-22页

作者： Taher M.Ghazal Sundus Munir Sagheer Abbas Atifa Athar Hamza Alrababah Muhammad Adnan Khan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Lahore Garrison University Lahore54000Pakistan Department of Computer Science COMSATS University Islamabad–Lahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnamGyeonggido13120Korea

Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking *** early detection of ASD is essential for delivering effective,timely *** facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early *** study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)*** model achieved a detection accuracy of 87.7%and performed better than other established ASD detection ***,this model could facilitate the early detection of ASD in clinical practice.

关键词： Autism spectrum disorder convolutional neural network loss rate transfer learning AlexNet deep learning

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A Cost Effective Methodology to Control Robotic Movements Based on Mobile Application and IoT Enabled Controller

A Cost Effective Methodology to Control Robotic Movements Ba...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ramkumar, G. Ahamed, Sk. Fayaz Nanammal, V. Rao, K. Uma Maheswara Shahare, Devendra Y. Al Sherideh, Ala'a Dept of Ece Chennai India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Andhra Pradesh Prakasam District523272 India Jeppiaar Engineering College Departmentt of Ece Chennai India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Andhra Pradesh Prakasam District523272 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

In this study, we present a cost-effective methodology for controlling robotic movements using a mobile application and an IoT-enabled controller. Our system utilizes an ESP8266 microcontroller, various sensors, and a mobile app to communicate commands to the robot via Wi-Fi and Bluetooth. The system's performance was evaluated based on key parameters, including response time, movement accuracy, energy consumption, and wireless communication stability. The response time for Wi-Fi ranged from 50ms at 1 meter to 240ms at 45 meters, while Bluetooth achieved faster response times of 30ms to 240ms over the same distances. Movement accuracy tests showed minimal deviation on smooth surfaces, with deviations as low as 1.0 cm, but higher errors on uneven terrain and grass, reaching up to 5.0 cm during complex movements. Bluetooth showed a power efficiency advantage, consuming 30-40% less power than Wi-Fi. Signal strength for Bluetooth remained stable up to 25 meters, while Wi-Fi was more reliable for longer distances but exhibited degraded performance beyond 30 meters. The total system cost was kept under USD 50, demonstrating its affordability for educational and industrial applications. This project successfully achieved its goal of creating a low-cost, efficient, and responsive robotic control system. © 2024 IEEE.

关键词： Bluetooth

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A Hybrid security Framework for Medical Image Communication

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computers, Materials & Continua 2022年第11期73卷 2713-2730页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Ashraf A.M.Khalaf Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

Authentication of the digital image has much attention for the digital *** image authentication can be verified with image watermarking and image encryption *** schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful *** on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this *** framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using *** the encrypted image is watermarking based on *** combination between watermarking and encryption increases the security and robustness of transmitting an *** performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).

关键词： Walsh hadamard transform watermarking encryption double random phase encoding structural similarity index

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Advancing Stroke Prediction: Explainable Machine Learning Models and Data Augmentation Strategies 25

Advancing Stroke Prediction: Explainable Machine Learning Mo...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Salma, Rahma Abu Al-Qerem, Ahmad Iessa, Abedalhakeam Rajab, Mohammed Alauthman, Mohammad Jeadi, Mohammad Elrashidi, Ali Zarqa University Faculty of Information Technology Computer Science Department Zarqa13110 Jordan University Headquarter University of Anbar Ramadi Iraq University of Petra Department of Information Security Amman11196 Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Stroke, a leading global disability cause, affects 12 million people annually, with 6.5 million dying. Preventing at least half of strokes requires public policy action and increased awareness. Early diagnosis is crucial and enhances patients' opportunities to survive a stroke. This research looks into how well machine learning (ML) models can predict when strokes will happen and how data augmentation affects those predictions.. Five ML models (Logistic Regression, Decision Tree, K-Nearest Neighbors, XGBoost, and Random Forest) are assessed and their performance is compared using an imbalanced and augmented dataset. Data augmentation techniques, including Random Oversampling, SMOTE, and Cluster Centroids, are employed to address class imbalance. The results demonstrate that some models achieve relatively high accuracy without data augmentation (XGBoost: 0.95). However, data augmentation techniques further improve performance across all models. Random Oversampling leads to the highest Accuracy for Random Forest (0.99), while SMOTE achieves the best Precision for Logistic Regression (1.00). The results found that age, occupation type, BMI, blood glucose level, and history of cardiovascular disease are risk factors for stroke. This study highlights the effectiveness of ML models, particularly when combined with data augmentation, for stroke prediction and emphasizes the importance of specific features in identifying stroke risk. © 2024 IEEE.

关键词： Logistic regression

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A binary Bird Swarm Optimization technique for cloud computing task scheduling and load balancing

A binary Bird Swarm Optimization technique for cloud computi...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Magesh Kumar, B. Kumar, M Sathish Shadrach, Finney Daniel Polamuri, Subba Rao Poonkodi, R. Pudi, Vasudeva Naidu Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Sathyamangalam India College of Engineering Department of Cyber Security Karpagam Tamil Nadu Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Kakinada Institute of Engineering and Technology Department of Computer Science and Engineering Korangi Kakinada India Sri Eshwar College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Matrusri Engineering College Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Cloud computing is a new paradigm for highperformance computation that combines a wide collection of autonomous mixed devices with a flexible computational construction to manage and deliver services. High-performance cloud computation is a subset of cloud computation. When it derives to improving the overall performance of cloud computation, task preparation is one of the most difficult things to improve. This can include response time, make span, and the degree of imbalance. When it comes to reducing power usage, processing time and increasing profit for service providers, task scheduling is vital. Heuristic methods like Binary bird swarm optimization (BBSO) were created to address the problem of inefficient procedures. However, if these algorithms are not paired with additional experiential or meta-heuristic procedures, the optimal solution will not be produced. Because of their high temporal complexity, these algorithms are less useful in real-world scenarios. BSO's binary form is being proposed for cloud computing workload scheduling and balancing in the NP-problem. Our goal function determines if heterogeneous VMs have the greatest completion time difference by taking into account the updating and optimization limits discussed in this research. We developed a technique for updating particle positions in conjunction with load balancing. Metaheuristics and heuristics fail to outperform the proposed technique when it comes to job scheduling and load balancing. Achieved this level of success thanks to the deployment of an artificial neural network. In terms of resource allocation, ANN has shown encouraging outcomes. CNNs are more accurate and faster in predicting targets than multilayer perceptron networks. © 2022 IEEE.

关键词： Cloud computing

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Development of a prototype of an intelligent system for predicting the quality of dairy manufacture 11

Development of a prototype of an intelligent system for pred...

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11th IEEE International Conference on Intelligent Systems, IS 2022

作者： Smitiukh, Yaroslav Samoilenko, Yuliia Kostiuk, Yuliia Kryvoruchko, Olena Stepashkina, Kateryna National University of Food Technologies Department of Automation and Computer Technologies of Control Systems Kyiv Ukraine State University of Trade and Economics Department of Software Engineering and Cyber Security Kyiv Ukraine

ISBN: (纸本)9781665456562

The technological process of the churning process in continuous butter manufacture were considered. The qualitative indicator of the water content of butter was modeled on the basis of a set of industrial data using an artificial neural network (ANN). The prototype of an intelligent system for predicting of the water content of butter allows to increase the information support of the operator-technologist. The proposed model predicts the water content of butter with an error of less than 2%. The input variables of the forecast model prototype are cream fat content, cream ripening temperature, cream supply temperature, frequency of revolutions of the stirrer of the whipping device, and consumption of the normalizing component. Further research is aimed at the development of a decision support information system for dairy manufacture and should be integrated into the subsystem of automated control of the technological process to ensure proper functioning in real time. © 2022 IEEE.

关键词： Intelligent systems

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Classification of Brain Tumor Images using Transfer Learning

Classification of Brain Tumor Images using Transfer Learning

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Loga Prakash. T Tharun Kumar. S Harshith. E Elangovan. T Dept of AI & DS Karpagam Academy of Higher Education Department of Computer Science and Engineering(Cyber Security) Karpagam Academy of Higher Education

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Brain tumors provide a substantial risk to human well-being because of their complex nature and the challenges linked to identifying and treating them at an early stage. Precise diagnosis is crucial for efficient intervention, although existing approaches often encounter constraints in terms of both accuracy and efficiency. This paper presents a new and innovative deep learning (DL) method that utilizes the Efficient Net family to enhance the classification and detection of brain tumors. The investigations show that EfficientNetB0 attained accuracy rates of 97.14%, 97.76%, 97.07%, 97.69%, and 97.07% across several criteria. The focal point of our study is the EfficientNetB3 model, which demonstrated outstanding performance, with an accuracy rate of 97.69%. Our method demonstrates superior effectiveness compared to numerous current state-of-the-art procedures. Our highly accurate DL model ensures enhanced diagnostic reliability and speed in clinical settings, facilitating earlier and more successful treatment solutions. The results of our study indicate a substantial opportunity to enhance patient outcomes in the diagnosis of brain tumors

关键词： Deep learning Renewable energy sources Accuracy Automation Computational modeling Transfer learning Brain tumors Brain modeling Complexity theory Reliability

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Encrypted Domain Secret Medical-Image Sharing with Secure Outsourcing Computation in IoT Environment

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IEEE Internet of Things Journal 2024年第2期11卷 2382-2393页

作者： Huang, Jingwang Cui, Qi Zhou, Zhili Yu, Keping Yang, Ching-Nung Choo, Kim-Kwang Raymond Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Nanjing University Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanyang Technological University School of Electrical and Electronic Engineering Jurong West639798 Singapore Guangzhou University Institute of Artificial Intelligence and Blockchain Guangzhou510006 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan The University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

In existing secret medical-image sharing (SMIS) schemes, to protect and manage secret medical-images (SMIs), the sharing and recovery of each SMI are implemented by local servers of medical institutions. However, since a lot of SMIs are produced by personal smart terminal devices in Internet of Things (IoT) environment, directly implementing the sharing and recovery processes will cause excessive communication and computing burden for those local servers, which makes the existing SMIS schemes not suitable for IoT environment. To address the above issue, we propose an encrypted domain SMIS (Enc-SMIS) scheme with secure outsourcing computation for protecting and managing medical images in IoT environment. In the proposed scheme, the medical images are first encrypted using fully homomorphic encryption (FHE) and then outsourced to a cloud server for generating a set of image shares. Subsequently, these shares are stored separately in different local servers of medical institutions. Furthermore, the recovery process is also outsourced to the cloud server when doctors need to observe the patients' medical images. Compared with the existing SMIS schemes, the proposed Enc-SMIS scheme alleviates the computing and communication burden on local servers significantly with secure outsourcing computation in the semi-honest model, and thus supports the storage and management of medical images well in IoT environment. © 2014 IEEE.

关键词： Internet of things

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Detection and Recovery of RFID Harmonic Signal for Wi-Fi Interference

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Journal of Communications and Information Networks 2025年第4期9卷 1-10页

作者： Chao Gong Ping Li Panlong Yang Zhao Lv School of Computer Science and Technology Anhui University Hefei 230000 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Ma'anshan 243032 China School of Computer science School of Cyber Science and Engineering Nanjing University of Information Science and Technology Nanjing 210000 China

In the realm of the Internet of things (IoT), radio frequency identification (RFID) technology is essential for linking physical objects to digital data. The introduction of harmonic technology has expanded RFID's applications by improving sensitivity and enabling communication with wireless fidelity (Wi-Fi) networks. However, this trend faces challenges, notably interference from strong Wi-Fi signals, which impacts RFID-based sensing systems. This paper proposes the WiFID (Wi-Fi and RFID) algorithm, which enables Wi-Fi devices to sense weak RFID harmonic signals. Adaptive subcarrier deallocation minimally reduces Wi-Fi throughput while effectively limiting interference with RFID harmonic sensing. Experimental validation on the universal software radio peripheral (USRP) platform demonstrates that WiFID successfully detects 90% of RFID harmonics at −30 dBm, with only a minimal 4% decrease in Wi-Fi throughput.

关键词： Radiofrequency identification Harmonic analysis Wireless fidelity Power harmonic filters Sensors Interference Throughput Filtering Correlation Vibration measurement

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