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6th International Conference on Computing and Informatics, ICCI 2024

作者： Karim, Abdelrhman A. Usama, Mohammed Ibrahim, Manar A. Hatem, Youssef Wael, Waleed Mazrua, Ahmad T. El-Monayer, Ghada K. Elbanhawy, Magy Foad, Khaled Moawad, Ibrahim F. Artificial Intelligence Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Artificial Intelligence Science Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer and System Department Faculty of Engineering Portsaid University Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9798350373875

Natural language processing (NLP) is a branch of artificial intelligence (Al) that enables computers to comprehend, generate, and manipulate human language. Natural language processing can interrogate the data with natural language text or voice. Abstractive Text Summarization is based on the Natural Language Processing technique that tries to provide new and more concise textual summaries for huge texts. Artificial intelligence and deep learning techniques are used in abstractive summarization to examine the text's essential information and create a new summary that conveys the content more concisely and accurately. This type of summary differs from normal extractive summarizing in that it can generate new summaries beyond simply extracting key lines from the source text. This study presents an abstractive Arabic summarization based on the Multilingual TS (MTS) model AASMTS to address these concerns. This technique is based on deep neural networks and models like transformers, which have changed the ability to summarize text. This research has explored the various types of summarizations and highlighted the significance of two prominent techniques: abstractive and extractive summarization. This research describes a complete process for developing an Arabic abstractive summarization model using the MTS architecture. Experiments on different datasets show that this model achieves state-of-the-art results across MTS. © 2024 IEEE.

关键词： Deep neural networks

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Efficiency beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in Data Centers

Efficiency beyond Limits: LSTM and k-NN Synergy for Forecast...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Kumar, C.H.N. Santhosh Devi, V. Chithra Gottumukkala, Prasanthi Satheesh Kumar, D. Sandip University School of Computer Science and Engineering Department of Computer Science & Application Maharashtra Nashik India Sandip University School of Computer Science and Engineering Department of Computer Science & Applications Maharashtra Nashik India Anurag Engineering College Department of CSE Telangana Kodada India PSNA College of Engineering and Technology Tamilnadu Dindigul India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350361537

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R2) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings. © 2024 IEEE.

关键词： k-nearest neighbors

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A Comparative Analysis of CNN and Transfer Learning Algorithms in Predicting Alzheimer's Disease Stages Using Neuroimaging

A Comparative Analysis of CNN and Transfer Learning Algorith...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sharma, Meenakshi Bhagat, Bhavneet Kumari, Vandana Bhumi Sharma, Naman Singh, Dipti Global Group of Institutes Department of Computer Science Engineering Amritsar India Guru Nanak Dev University Department of Computer Science Engineering Amritsar India

ISBN: (纸本)9798350373783

Alzheimer's disease (AD) prediction plays a critical role in early detection and intervention, allowing for better patient care and management. This paper investigates the predictive capabilities and state-of-art of Convolutional Neural Networks (CNN) and transfer learning models, including Inception V3, Resnet-18, and Resnet-50, for the progression of Alzheimer's disease (AD). The Alzheimer's disease Neuroimaging Initiative (ADNI) dataset used in this study, which includes multimodal neuroimaging, clinical, genetic and demographic data, the research aims to provide accurate predictions at various stages of the EA. This dataset enables robust training and evaluation of deep learning algorithms to predict AD progression. The data undergoes preprocessing and feature engineering to prepare a robust data set. Evaluations covering accuracy, precision, recall and F1 score, using k-fold cross-validation, are performed to measure the performance of the chosen algorithms. CNN and Inception V3 demonstrate remarkable performance with an accuracy of 93.75% and 94%. Comparative analyzes include exploration of Inception V3, Resnet-18, and Resnet-50, shedding light on their individual contributions to AD prediction. This paper contributes to the development of reliable and efficient deep learning approaches for early prediction and diagnosis of AD, with the potential to improve patient outcomes. Comparing the predictive capabilities of CNNs and transfer learning models provides valuable insights into their effectiveness in modeling AD progression. © 2024 IEEE.

关键词： Convolutional neural networks

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Bridging the Bandwidth Gap: A Mixed Band Telephonic Urdu ASR Approach with Domain Adaptation for Banking Applications 1

Bridging the Bandwidth Gap: A Mixed Band Telephonic Urdu ASR...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Khalid, Ayesha Adeeba, Farah Sehar, Najm Ul Hussain, Sarmad Center for Language Engineering Al-Khawarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Department of Computer Science University of Engineering and Technology New Campus Pakistan

ISBN: (纸本)9798891762015

The accuracy of Automatic Speech Recognition (ASR) systems is influenced by the quality and context of speech signals, particularly in telephonic environments prone to errors like channel drops and noise, leading to higher Word Error Rates (WER). This paper presents the development of a large vocabulary Urdu ASR system for telephonic speech, based on a corpus of 445 speakers from diverse domains. The corpus, annotated at the sentence level, is used to train and evaluate GMM-HMM and chain Time-Delay Neural Network (TDNN) models on a 10-hour test set. Results show that the TDNN model outperforms GMM-HMM. Mixing narrowband and wideband speech further reduces WER. The test sets are also evaluated for the pre-trained model Whisper for performance comparison. Additionally, system adaptation for the banking domain with a specialized lexicon and language model demonstrates the system’s potential for domain-specific applications. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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Automatic classification of normal/AD brain MRI slices using whale-algorithm optimized hybrid image features

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14237-14248页

作者： Kadry, Seifedine Jessy, V. Elizabeth Rajinikanth, Venkatesan Crespo, Rubén González Department of Applied Data Science Noroff University College Kristiansand4612 Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Division of Research and Innovation Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India Department of Computer Science and Technology Universidad Internacional de La Rioja La Rioja Logroño Spain

In recent years, the prevalence of Age-Related Illnesses (ARL) has been increasing among older individuals, and early recognition and treatment will result in better living conditions. It is well known that Alzheimer's Disease (AD) is among the ARD, and severe cases may result in dementia as well. It is the purpose of this study to propose a technique for distinguishing normal/AD brain MRI slices with improved accuracy utilizing the T2-modality. This scheme consists following phases: (i) Brain MRI collection and preprocessing, (ii) Deep feature extraction with the chosen scheme, (iii) Handcrafted feature extraction, (iv) Whale Algorithm (WA) based feature reduction and serial integration, and (v) binary classification using five-fold cross-validation. A total of 2000 MRI slices (1000 normal and 1000 AD class) are examined during this task using images collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI). This study confirms that the proposed scheme provides a classification accuracy of > 98% when applied with the K-Nearest Classifier. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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Enhancing Traffic Management Using Deep Learning for Realtime Classification 11

Enhancing Traffic Management Using Deep Learning for Realtim...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Jayasakthi Balaji, G. Varunika, S. Grace, R. Kingsy Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350350357

In this modern era, the use of vehicles is increasing tremendously due to the increase in the population. The escalating issue of traffic congestion has emerged as a critical challenge in the real world. We currently face a lack of viable solutions to efficiently manage and alleviate traffic-related issues. In the event of an accident, our ability to swiftly locate the incident and initiate requisite actions is hindered by existing constraints, contributing to delays in addressing the situation effectively. The delay in finding what the incident is and locating the problem results in the increased severity of the situation. In response to these challenges, we have devised a solution through deep learning models. Upon the occurrence of an incident, our system promptly dispatches immediate alerts to authorized personnel, and checks for the manipulation of the traffic signals, facilitating a faster and more efficient response. Therefore, action could be taken at the same time as the incident, which will reduce the impact of the situation. © 2024 IEEE.

关键词： Traffic congestion

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A Predictive and Adaptive Mobile/Web Application for Alzheimers Disease Using Machine Learning 9

A Predictive and Adaptive Mobile/Web Application for Alzheim...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Balamuralikrishnan, G. Boomika, S. Sri Nandhini, R. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

A neurodegenerative condition, Alzheimer's disease (AD) affects millions of individuals globally, that causes memory loss and cognitive impairment. Improving patient outcomes and delaying the course of disease need early detection and continuous treatment. The paper proposes a predictive and adaptive mobile/web application for Alzheimer's disease management, leveraging transfer learning. In this research, we utilize transfer learning techniques, specifically with pre-trained deep learning architectures like ResNet and VGG. These models, originally trained on large datasets, are fine-tuned with Alzheimer's disease-specific data to enhance diagnostic accuracy and efficiency within our mobile/web application. By offering cognitive activities, remote health tracking, and personalized care, the platform improves patient involvement and aids medical professionals in managing patient conditions. Transfer learning increases diagnostic precision while requiring less data. © 2024 IEEE.

关键词： Transfer learning

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Electronic health records protection strategy by using blockchain approach

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Multimedia Tools and Applications 2024年第39期83卷 86883-86894页

作者： Kathole, Atul B. Patil, Sonali D. Kumbhare, Savita Vhatkar, Kapil Kshirsagar, Aniruddha P. Kimbahune, Vinod V. Computer Engineering Dr. D. Y. Patil Institute of Technology Pune India IT Dept. Pimpri Chinchwad College of Engineering Pune India Computer Science and Engineering Department K.B.P College of Engineering Satara Satara India

Electronic health records (EHRs) provide significant challenges for medical organizations due to the digitalization of traditional medical information. When using EHRs, both doctors and patients are accustomed to spending considerable time conducting the necessary queries;nevertheless, the acquired data is not always related to the sought one, and access may be revoked if this is the case. The exchange and maintenance of medical health records are among the most crucial tasks in the healthcare system. The integrity of health records is crucial, as is maintaining patient anonymity, and either would have devastating effects in the event of a breach. Therefore, it is one of the most important reasons to keep electronic health records safe. Modern healthcare infrastructure is notoriously difficult to use, wasteful, and fraught with problems related to data integrity, security, and privacy. Better EHR management and monitoring, however, has the potential to lessen the severity of the problems with security and complexity. The blockchain's potential, with its decentralized and trustworthy nature in the healthcare industry, makes this a reality. Problems with data management and the way information is validated and disseminated are at the heart of the healthcare delivery system's inherent difficulties. Improved access to medication tracking, hospital assets, drug systems, patient information, and so on is the primary benefit of employing blockchain technology in healthcare data management. Because of how important it is to have access to a patient's medical history when prescribing medication, blockchain technology has the potential to significantly enhance the current healthcare delivery system. Therefore, it is critical to develop a blockchain-based system for verifying and protecting medical records. The primary objective of this article is to create a permissioned blockchain-based Ciphertext Policy-Attribute-based encryption system to protect privacy and regulate acces

关键词： Blockchain

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An Integrated Approach Based on Fuzzy Logic and Machine Learning Techniques for Reliable Wine Quality Prediction 4

An Integrated Approach Based on Fuzzy Logic and Machine Lear...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kaur, Narinder Kaur, Gaganjot Aruchamy, Prasanth Chaudhary, Neelu Department of Computer Science & Engineering Chandigarh University Mohali Gharuan India Department of Computer Science & Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science & Engineering Manav Rachna University Haryana Faridabad India

In recent decades, wine quality has been one of the predominant problems in many wine industries. However, the analysis of wine quality is inherently complex owing to its multivariate characteristics and the instigation of several sensory features. Most of the existing prediction methods lagged in providing higher detection accuracy for multi-dimensional datasets. To overcome this, a novel Adaptive Wine Quality Prediction (AWQP) approach has been proposed to assess the quality of the wine in an accurate manner. The proposed AWQP methodology entails the development of a Hybrid detection model that encompasses the fuzzy logic principles with the predictive influence of machine learning techniques. Primarily, the sensory features like aroma, taste, and color are delineated by exemplifying the linguistic variables. The fuzzy rules are then determined to collare the qualitative relationships among these different variables. Subsequently, the finest machine learning algorithm can be carried out to train and test the prediction model. The proposed AWQP methodology ameliorates the comprehensibility of the decision-making process through the hybridization of fuzzy logic and the finest machine learning. This proper hybridization facilitates the proposed method to achieve a superior detection accuracy of 98.75% when compared to the existing prediction methods. © 2025 The Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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Contour Edge Mask Filtered Residual Network based Egg Classification 8

Contour Edge Mask Filtered Residual Network based Egg Classi...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Devi, M. Shyamala Sangeetha, V. Reshmi, R. Saima Nooreen, S. Rajeshwari, C. Sai Pranavi, N. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331505462

In agricultural and food safety, classifying eggs is a crucial activity that requires great precision in distinguishing various kinds of egg. Traditional approaches can be expensive and error-prone since they frequently rely on manual examination or simple image processing techniques. In order to improve classification performance, this research proposes Contour Edge Mask Filtered ResNet (CEM-ResNet), which combines advanced deep learning techniques with edge-detection methods. The Egg Dataset containing 2000 egg images that was used for implementation. The suggested CEM-ResNet model uses contour edge masking to eliminate extraneous background noise and highlights key structural information while utilizing ResNet architecture to capture essential details of eggs. The suggested CEM-ResNet model uses contour edge masking to eliminate extraneous background noise and highlights key structural information while utilizing ResNet architecture to capture essential details of eggs. Initially, the CEM-ResNet model organizes the egg into two groups as Chicken egg and Duck egg. The organized eggs are performed with labeling and then subjected to data augmentation to end up with 42,000 images. The augmented egg images are converted to grayscale images. The grayscale images are processed with edge detection model to finalize the contour filtered egg images and ResNet model. The contour filtered egg images are refined to create the edges and then the mask was created to generate Contour Edge Mask Filtered (CEM) egg images. The ResNet model was refined to process the CEM egg images. With a high accuracy of 99.64%, the implementation reveals that the proposed CEM-ResNet model outperforms towards the classification task. © 2024 IEEE.

关键词： Deep learning

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