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24th International Arab Conference on Information Technology, ACIT 2023

作者： Hamdan, Khaled Kazar, Okba College of It Uaeu Dept. of Computer Science and Software Engineering United Arab Emirates College of It Uaeu Dept. of Information Systems & Security United Arab Emirates

ISBN: (纸本)9798350384307

The industrial sector has entered a phase of profound change which sees digital technologies being integrated into the heart of industrial processes. This fourth industrial era gives birth to a new generation of factory called 'Digital Factory', 'Industry 4.0'. This industrial change is the result of the integration of new digital technologies into manufacturing processes and the optimization of energy consumption. The commitment to the environment is also a key and success factor of Industry 4.0 and green in particular the reduction of carbon emissions and pollution. This is how smart green industry try to take in charge the problem of energy consumption and to reduce the Co2 pollution. The goal of this paper is to present the new model of smart green industry using distributed artificial intelligence based on multi-agent system (MAS). The motivation to use the intelligent approach based on MAS is based on the similarity between the fundamental concepts of the cyber-physical system and those contained in the architecture and operation of MAS such as communication, coordination, decision and cooperation. © 2023 IEEE.

关键词： environmental impact Industry 4.0 smart and green Industry 4.0

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Leveraging Ensembles of Pre-trained CNNs for Improved Lung Cancer Detection and Classification

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Procedia computer science 2025年 260卷 151-159页

作者： Dasari Bhulakshmi Dharmendra singh rajput Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore 632014 India

Lung cancer is a serious global health concern, highlighting the importance of early identification to improve patient survival rates. We explore the potential of deep learning(DL) models to improve lung cancer diagnosis through detection and classification models. The performance of pre-trained ResNet50, VGG19, and AlexNet models is evaluated on an augmented lung cancer image dataset to determine their suitability for lung cancer classification. The fine-tuned models are evaluated for their ability to identify and classify lung cancer, achieving high accuracy of 92.88%, 93.06%, and 95.23%. While promising, this approach has limitations. The efficacy of DL models is significantly influenced by both the quality and volume of the training data. Additionally, the ”black box” nature of DL models can make it challenging to understand their decision-making process. However, the results of this study suggest that DL ensembles hold significant potential for lung cancer diagnosis. Further research is necessary to address limitations and explore interpretability techniques for wider clinical acceptance.

关键词： Deep Learning Machine Learning convolutional neural networks Artificial Intelligence

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Prognostic Survival Analysis for AD Diagnosis and Progression Using MRI Data: An AI-Based Approach

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IEEE Access 2025年 13卷 89059-89078页

作者： Deenadayalan, T. Shantharajah, S.P. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore632014 India

Alzheimer’s Disease is a progressive neuro-degenerative disorder and a leading cause of dementia, marked by cognitive decline, memory loss, and behavioral changes. Despite advancements in medical imaging and Artificial Intelligence (AI), early detection and accurate prognostic modeling remain challenging, particularly in distinguishing overlapping disease stages. This study proposes a novel AI framework combining EfficientNetB0, a state-of-the-art deep learning model, with dual attention mechanisms for robust feature extraction and survival analysis using the Alzheimer’s MRI dataset hosted on the Kaggle platform. The model was trained and evaluated on a balanced dataset of 6,400 MRI images, classified into 4 AD stages: Non-Demented (NoD), very Mild-Demented (vMiD), Mild-Demented (MiD), and Moderate-Demented (MoD). The pre-processing steps, including resizing, normalization, and data augmentation, ensured robustness and generalizability. The framework achieved classification accuracies of 99. 93% (training), 93. 60% (validation) and 93. 59% (testing), with a perfect Concordance Index of 1.0 for survival predictions, demonstrating its ability to accurately rank survival times. By integrating survival analysis with deep learning, the framework addresses limitations in previous work, such as reliance on linear assumptions and limited classification capabilities, and provides actionable insights for clinical workflows, supporting personalized treatment and early intervention strategies. Future work will focus on integrating longitudinal data, exploring time-dependent co-variates, and enhancing explainability to promote adoption in diverse clinical settings. This approach represents a significant advancement in AI-driven diagnosis and progression modeling of AD, with the potential to transform patient care and outcomes. © 2013 IEEE.

关键词： Deep learning

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3D Gaze Estimation via Binocular Disparity and Feature Refinement 10

3D Gaze Estimation via Binocular Disparity and Feature Refin...

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10th International Conference on Big Data Computing and Communications, BIGCOM 2024

作者： Wang, Chengxin Chen, Qiuxia Tian, Ying Yang, Jinfeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9798331509538

The eyes are the windows to the soul and are crucial for studying human behavior. Therefore, gaze estimation has attracted much attention in the field of computer vision. In recent years, convolutional neural networks have driven significant progress in gaze estimation research. Despite the remarkable achievements of gaze technology based on full-face images, the inability to obtain full-face images in specific scenarios makes research based on eye images particularly important. This paper proposes a three-dimensional gaze estimation model that takes binocular images as input, using VGG16 as the backbone network for feature extraction. To enhance the feature extraction capability, we have embedded channel attention and spatial attention mechanisms. Meanwhile, to improve the stability of gaze estimation, we classify real gaze angles. First, we perform a preliminary screening of gaze direction through a classification task, and then perform linear regression with the true angle. Finally, we optimize gaze features using binocular vision differences to finetune the gaze estimator. Experiments have shown that our method performs well on three public datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

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Journal of computer science & Technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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A model to measure the spread power of rumors

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13787-13811页

作者： Jahanbakhsh-Nagadeh, Zoleikha Feizi-Derakhshi, Mohammad-Reza Ramezani, Majid Akan, Taymaz Asgari-Chenaghlu, Meysam Nikzad-Khasmakhi, Narjes Feizi-Derakhshi, Ali-Reza Ranjbar-Khadivi, Mehrdad Zafarani-Moattar, Elnaz Balafar, Mohammad-Ali Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Naghadeh Branch Islamic Azad University Naghadeh Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Software Engineering Istanbul Topkapi University Istanbul Turkey Clinical Informatics Louisiana State University Health Sciences Center Shreveport Shreveport United States Department of Computer Engineering Shabestar Branch Islamic Azad University Shabestar Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering University of Tabriz Tabriz Iran

With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not been considered. To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR). For this purpose, the theory of Allport and Postman will be adopted, which it claims that importance and ambiguity are the key variables in rumor-mongering and the power of rumor. Totally 42 content features in two categories "importance" (28 features) and "ambiguity" (14 features) are introduced to compute SPR. The proposed model is evaluated on two datasets, Twitter and Telegram. The results showed that (i) the spread power of False Rumor documents is rarely more than True Rumors. (ii) there is a significant difference between the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a criterion can have a positive impact on distinguishing False Rumors and True Rumors. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cell proliferation

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Estimation of Neurological Diseases and Age Using Brain MRI 4th

Estimation of Neurological Diseases and Age Using Brain MRI

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4th International Conference on Smart systems: Innovations in Computing, SSIC 2023

作者： Ashokkumar, K. Dharshan, K. Ragupathi, T. Pritam, Anem Sagar, Amujuru Gnana Department of Computer Science Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Data Science and Business systems School of Computing SRM Institute of Science and Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819736898

Leveraging D-NN trained on neuroimaging data, we can effectively estimate the chronological ages of normal persons;this projected brain age has potential as a biomarker for identifying age-related disorders. The suggested technique employs a CNN cascade network from DL and the Support Vector Machine (SVM) algorithm from ML (SVM). These techniques have been employed to train MRI scans of the brain, classifying them into regular (unaffected by illness), Alzheimer’s disorder (AD), and neurocognitive disability. That we also have the ability to determine ages thanks to the age classifications on the photos. The brain MRI collection is trained using CNN and SVM, on which the classifications and age estimate will be carried out. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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AI-Driven Intelligent IoT systems for Real-Time Food Quality Monitoring and Analysis 2

AI-Driven Intelligent IoT Systems for Real-Time Food Quality...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Krishnan, Prabhakar Ebenezar, U. Samson Ranitha, R. Purushotham, N. Balakrishnan, T. Suresh Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University Department of Computer Science and Engineering Mr.palliAP India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

The system will monitor and analyse food quality along the supply chain in real time. The system has high performance due to the CNN algorithms. In terms of accuracy, CNN has a high performance (95.2%), precision (92.8%), recall (94.5%) and F1 score (93.6 %). These features enable the model to predict food quality under good and poor conditions, thus demonstrating its effectiveness in assessing quality. The accuracy of real-time monitoring was evaluated across the supply chain to prove system reliability. Accuracy ratings for transport, storage and processing were 96.3%, 94.7% and 92.1%. These results show the robustness of this system within dynamic supply chain environments. Further testing evaluated environmental resilience capabilities. Under normal conditions, the accuracy of this system was 95.2%, while it reduced to 92.6% and rises again up to a level of 94.3%. The adaptability of the system under harsh conditions demonstrates that it can be applied to ensure food quality in various cases. This study reveals how proposed AI-based intelligent Internet of Things system may be used for real application using an appropriate dataset selected properly. This approach can help improve the quality assurance of food in an industry that is complex by uniting advanced algorithms with real-time data monitoring. © 2024 IEEE.

关键词： Artificial Intelligence Food Quality Analysis Intelligent IoT systems Real-Time Monitoring

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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2024 International Conference on Advancement in Renewable Energy and Intelligent systems, AREIS 2024

作者： Raji, V. Maheswari, S. Dharinya, S. Sree Chinniah, P. Kavin, K.S. Raja, Kiruba Thangam S.K.P. Engineering College Dept. of Information Technology Tiruvannamalai India Tamilnadu Erode India School of Computer Science Engineering and Information Systems Vit University Dept. of Software and Systems Engineering Vellore India ST. Joseph College of Engineering Dept. of Ece Tamilnadu Sriperumbudur India Government College of Engineering Dept. of Eee Tamil Nadu Tirunelveli India Vit University Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350387230

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Real-Time Pill Identification for the Visually Impaired Using Deep Learning 6

Real-Time Pill Identification for the Visually Impaired Usin...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Dang, Bo Zhao, Wenchao Li, Yufeng Ma, Danqing Yu, Qixuan Zhu, Elly Yijun San Francisco Bay University Computer Science FremontCA United States School of Software Engineering University of Science and Technology of China Anhui Hefei China School of Electronics and Computer Science University of Southampton Southampton United Kingdom Stevens Institute of Technology Computer Science HobokenNJ United States College of Computing Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798350353174

The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the development and implementation of a deep learning-based mobile application designed to assist blind and visually impaired individuals in real-time pill identification. Utilizing the YOLO framework, the application aims to accurately recognize and differentiate between various pill types through real-time image processing on mobile devices. The system incorporates Text-to-Speech (TTS) to provide immediate auditory feedback, enhancing usability and independence for visually impaired users. Our study evaluates the application's effectiveness in terms of detection accuracy and user experience, highlighting its potential to improve medication management and safety among the visually impaired community. © 2024 IEEE.

关键词： Deep Learning Healthcare Mobile Application Pill Identification Visual Impairment YOLO Framework

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