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Towards personalized control of things using Arabic voice commands for elderly and with disabilities people

International Journal of Information Technology (Singapore) 2023年 1-22页

作者： Salah, Abdelrahman Adel, Ghada Mohamed, Hussein Baghdady, Youssef Moussa, Sherin M. Department of Software Engineering and Information Technology Faculty of Engineering and Technology Egyptian Chinese University Cairo 11351 Egypt Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo 11566 Egypt Laboratoire Interdisciplinaire de l’Université Française d’Égypte (UFEID Lab) Université Française d’Égypte Cairo 11837 Egypt

Considering recent smart home solutions that automate home appliances through voice commands, elderly people and/or with special needs prefer using their mother-tongue. However, most of current systems adopt the English language, making it harder for non-native English speakers, while personalization is not heavily considered. In this paper, we propose the Arabic Personalized Control of Things (APCoT) system that achieves inhabitant-tailored home automation through Arabic voice commands based on the user’s preferences to control a home device. A new dataset is created for a wider range of Arabic voice commands to interact with home appliances rather than just turning them on/off. Internet of Things, voice and speech recognition are utilized to attain personalization according to the inhabitants' voiceprints, while infrared is adopted to control non-smart devices supporting infrared signals. As a Proof-of-Concept, three home appliances are adopted: TV, Lights and Bluetooth speaker. Machine learning algorithms are customized for Arabic voice recognition, integrating face and hand gestures recognition to provide an additional facility for inhabitant’s identification and sending control commands respectively. The experimental results indicate that the best average accuracies obtained for Arabic voice recognition were 99.63% for a single speaker and 95.4% for multi-speakers, with average response times 0.141s and 14.29s respectively. For speech recognition, Google online API and a customized Vosk offline pretrained model achieved average accuracies of 95% and 90%, average response times 0.7553s and 0.3933s respectively. The face and hand gestures recognition achieved average accuracies of 90% and 98%, average response times 0.5934s and 15.62049s respectively. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Arabic commands Elderly people Home automation People with disabilities Smart home Speech and voice recognition

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Exploring Blockchain Technologies: Insights Into Consensus Mechanisms, Mining Pool Dynamics, and Energy Consumption Patterns

Exploring Blockchain Technologies: Insights Into Consensus M...

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International Conference on Mechatronics (ICOM)

作者： Abdulkadir Tasdelen Mohamed Hadi Habaebi Md Rafiqul Islam Dept. of Software Engineering Faculty of Engineering and Natural Sciences Ankara Yildirim Beyazit University Ankara Türkiye Faculty of Engineering IIUM Malaysia Dept. of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Blockchain technologies and cryptocurrencies have garnered significant attention for their potential to revolutionize various industries. This paper investigates the landscape of blockchain technologies in the context of cryptocurrencies, focusing on performance metrics, trade-offs, trends, and underlying issues. Through comprehensive analysis, the most utilized consensus mechanisms among the top cryptocurrencies are identified. The distribution of top mining pools by hash rate over the last two years reveals dominance by a few major pools, with implications for decentralization and security. In Bitcoin mining pool distribution, trends indicate the dominance of established pools like Foundry USA Pool and AntPool, alongside notable growth from smaller pools like MARA Pool and Luxor. Comparative analysis across different cryptocurrencies highlights variations in hashing rate, block production, and energy consumption, underscoring the complex dynamics of blockchain mining. Moreover, the comparative analysis of energy consumption in blockchain mining hardware reveals insights into energy efficiency and sustainability. Smaller mining pools demonstrate significant growth and momentum, contributing to the diversification and decentralization of mining operations.

关键词： Measurement Industries Energy consumption Mechatronics Production Market research Hardware

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Improving the Performance of Firewalls through Network Traffics Logs' Classification of Firat Dataset Using Decision Tree Algorithm

Improving the Performance of Firewalls through Network Traff...

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International Conference on Web Research (ICWR)

作者： Maryam Talaeepoor Behzad Soleimani Neysiani Mohammad Davarpanah Jezi Department of Computer Engineering Faculty of Computer Engineering Naghshe Jahan Institute of Higher Education Isfahan Iran Department of Software Engineering Oil Design and Construction Company (ODCC) Isfahan Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

ISBN: (数字)9798350394986

ISBN: (纸本)9798350394993

As long as a computer system is connected to the Internet, it is susceptible to attack as a victim. In computer networks, it becomes important to manage the network based on parameters such as network size and network data. Firewalls are devices that help network administrators in this case to establish security in the network, and can be based on the rules that the firewall is based on. It is configured to control incoming and outgoing network traffic. Firewalls can be considered the most vital components of the network in establishing security. Firat University introduced a dataset containing firewall logs with multiple classes for firewall decisions. This study uses data mining techniques to improve the validation performance of classification using various machine learning algorithms like neural networks, deep learning, and kNN. The experimental results show more than 10% improvement according to precision and recall rates among various folding scenarios used in related works with minor improvement in accuracy, too. The decision tree algorithm is fast and explainable versus other algorithms.

关键词： Deep learning Machine learning algorithms Runtime Firewalls (computing) Neural networks Telecommunication traffic Network security

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Using Hypergraph Ansatz on the Hybrid Quantum-Classical Image Compression Scheme

Using Hypergraph Ansatz on the Hybrid Quantum-Classical Im...

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International Conference on Intelligent Systems and Networks, ICISN 2024

作者： Nguyen, Tran Khanh Dong, Nguyen Trinh Hai, Vu Tuan Luan, Pham Hoai Faculty of Computer Science University of Information Technology Ho Chi Minh City Viet Nam Faculty of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Nara Institute of Science and Technology Takayama-cho Nara Ikoma Japan

ISBN: (纸本)9789819755035

Image compression is an important problem in the image processing field and has been applied widely in various applications, from storage optimization and print media to image exchange. Almost all compressed image formats, such as Joint Photographic Experts Group (JPEG) and Portable Network Graphics (PNG), have pros and cons, but there is always a balance between file size and image quality;the larger the file size, the greater the loss on the image. However, the hybrid quantum-classical approach provides us with a new way to compress images more efficiently. In this research, we propose a scheme that turns the image into a unitary operator’s parameter, then we make a new image format via the quantum state preparation algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： hypergraph ansatz image compressing quantum computing

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Communication, Power, and Sla-Aware Virtual Machine Placement in Oversubscribed Cloud Data Center: A Monetary Approach

SSRN

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SSRN 2023年

作者： Kiani, Mohsen Khayyambashi, Mohammad Reza Department of Computer Science Khansar Campus University of Isfahan Isfahan Iran Department of Software Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

In the present study, we propose an algorithm for mapping virtual machines (VMs) to physical machines (PMs) in cloud data centers. The proposed method models a dynamic system where VMs enter and terminate. The goal of the algorithm is to minimize the power consumption of the PMs and the network while preventing service level agreement violation (SLA). Moreover, oversubscription is leveraged to enhance PM utilization. The mapping problem is formulated as an optimization problem and solved using a heuristic and a meta-heuristic algorithm. For the latter, we used chemical reaction optimization. In addition, to convert various important metrics into one optimization goal, first, the raw revenue of executing the VMs is calculated. Then, all other measured parameters, including PM and network power consumption, migration cost, and SLA violation penalty, are converted to monetary measures to obtain a net revenue, which is considered as the optimization goal. The simulation results show that the implemented CRO algorithm outperforms heuristic methods by a significant margin in terms of SLA violation and power consumption. © 2023, The Authors. All rights reserved.

关键词： Electric power utilization

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Classification of Subtypes of Acute Lymphoblastic Leukemia Using Feature Fusion from Blood Smear Images

Classification of Subtypes of Acute Lymphoblastic Leukemia U...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Ghazala Alhadi Eljadi Ahmed Lawgali Salwa Elakeili dept. Computer Sciences Faculty of Information Technology University of Benghazi Beghazi Libya dept. Software Engineering Faculty of Information Technology University of Benghazi Beghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Early detection of Acute Lymphoblastic Leukemia (ALL) is vital for providing appropriate treatment, which requires accurate diagnosis to identify the types of the disease. Therefore, this study focuses on evaluating the performance of deep learning models in classifying different types of leukemia using three pre-trained CNN models: MobileNet V2, DenseNet201, and EfficientNet, to extract deep features. These features are then combined into a single set. An information exchange technique was used to select the best features after merging, and SMOTEENN was employed to address feature imbalance. The results showed that combining features extracted by the pre-trained CNN models achieved high accuracy and reliability, reaching 99.66%, enhancing the performance in accurately and quickly classifying patients. This approach not only improves diagnostic accuracy but also contributes to accelerating the decision-making process for treatment, enabling doctors to better tailor therapies and thereby improve patient outcomes. The findings underscore the importance of ongoing research in this field and provide future recommendations for enhancing performance and increasing classification accuracy in similar medical applications.

关键词： Support vector machines Accuracy Merging Leukemia Medical treatment Feature extraction Convolutional neural networks Reliability Blood Medical diagnostic imaging

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Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

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computer Systems Science & engineering 2023年第10期47卷 791-807页

作者： Sundas Rukhsar Mazhar Javed Awan Usman Naseem Dilovan Asaad Zebari Mazin Abed Mohammed Marwan Ali Albahar Mohammed Thanoon Amena Mahmoud Department of Software Engineering University of Management and TechnologyLahore54770Pakistan School of Computer Science The University of SydneySydneyAustralia Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Computer Science Umm Al Qura UniversityMecca24211Saudi Arabia Computer Science Department Faculty of Computers and InformationKafrelsheikh UniversityKafrelsheikhEgypt

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern ***,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment *** recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or *** or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing *** process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning *** deep learning-based model long-short-term memory(LSTM)performed better than machine learning *** short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.

关键词： COVID-19 artificial intelligence machine learning deep learning sentimental analysis support vector classifier

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HistoDX: Revolutionizing Breast Cancer Diagnosis Through Advanced Imaging Techniques

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IEEE Access 2025年 13卷 94416-94436页

作者： Arshad, Wishal Masood, Tehreem Shahzad, H.M. Ahmed, Hassan A. Ahmed, Syed Hamza Khushi, Hafiz Muhammad Tayyab University of Central Punjab Faculty of Software Engineering and Information Technology Lahore54000 Pakistan Superior University Faculty of Computer Science and Information Technology Lahore54000 Pakistan Superior University Department of Software Engineering Lahore54000 Pakistan Cleveland State University Monte Ahuja College of Business Department of Information Systems ClevelandOH44114 United States

Breast cancer is the second leading cause of mortality among women worldwide, highlighting the need for efficient histopathology-based screening methods for early diagnosis. This study introduces HistoDX, a deep learning framework to classify Invasive Ductal Carcinoma (IDC) using 277,524 histopathology image patches (50x50 pixels) from Paul Mooney's IDC dataset on Kaggle, comprising No Cancer and IDC(+) classes. HistoDX employs a preprocessing pipeline with normalization, data augmentation, and class balancing via oversampling and weighted loss to address the class imbalance. A customized convolutional neural network, built on EfficientNetV2-B3 with additional layers, achieves 97% accuracy and a 0.91 ROC-AUC score on the test set. Validation on BreakHis (97% accuracy) and BACH (90% accuracy) datasets confirm generalizability, though detecting minority IDC(+) cases remains challenging. Low training and test losses underscore reliability. HistoDX empowers pathologists by enhancing diagnostic efficiency and minimizing subjectivity through effective class imbalance mitigation. Its robust performance across diverse datasets like BreakHis and BACH suggests readiness for clinical integration. Future research into advanced augmentation techniques, ensemble models, and whole-slide image analysis could further optimize accuracy, sensitivity, and scalability, paving the way for broader adoption in precision oncology. © 2013 IEEE.

关键词： Diseases

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Robust Feature engineering Techniques for Designing Motor Imagery-Based Bci Systems

SSRN

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SSRN 2025年

作者： Gardezi Ahmed, Syed Saim Khan, Mahnoor Bukhari, Syeda Muneeba Khan, Rizwan Ahmed Jawed, Soyiba Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science Faculty of Information Technology Salim Habib University Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naive Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provide an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Using quadratic complex planar splines in solving local positioning problems 16

Using quadratic complex planar splines in solving local posi...

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16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2022

作者： Strelkovskaya, Irina Solovskaya, Irina Strelkovska, Juliya Grygoryeva, Tetyana Faculty of Cybersecurity Software Engineering and Computer Science International Humanitarian University Odesa Ukraine Institute of Criminal Justice National University Odesa Law Academy Odesa Ukraine

ISBN: (纸本)9781665468619

A solution to the task of local subscriber positioning for a Wi-Fi/Indoor network is presented. The use of the Fingerprinting method is proposed. The operation of the Fingerprinting method, that uses quadratic complex planar splines to evaluate the user's position, is explained. Presented finding a user's local position error on a Wi-Fi/Indoor network. Using the proposed method of finding a subscriber's local position in a Wi-Fi/Indoor network reduces location error. © 2022 IEEE.

关键词： Wi-Fi

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