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3rd International Conference on Intelligent Systems, Advanced Computing, and communication, ISACC 2025

作者： Sarma, Dubari Dasgupta, Meghna Deka, Vaskar Das, Dipankar Rashed, Golam Gauhati University Department of Information Technology India Rajshahi University Department of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798331523893

In the field of information Retrieval (IR). Question Answering (QA) is considered as a contemporary area for providing relevant answers in response to questions that are proposed in natural language. The QA system consist of three core components and these are question classification, information retrieval and answer extraction. The goal of QA is to develop automated systems that can comprehend the meaning of questions and provide either accurate or relevant answers. Machine learning algorithms plays a vital role in solving automated question answering (QA) problems using natural language processing. These algorithms are designed to enable computers to understand, infer, and respond to human language queries. This work considered deep learning based LLM-BERT for implementing a QA system over the agricultural domain. BERT is used because it comprehends the context of words by observing at both their left and right environment, making it highly operative for complex question and answers. Its ability to adapt to specific domains through fine-tuning ensures accurate and relevant responses in tasks like QA. The dataset used here has been collected, generated and constructed by the authors. © 2025 IEEE.

关键词： Question answering

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Hybrid Deep Learning Methods for Enhancing Parkinson's Disease Early Detection 4

Hybrid Deep Learning Methods for Enhancing Parkinson's Disea...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Devi, S. Vijaya Amala Vijayalakshmi, K. Krishnan, R. Santhana Raj, J. Relin Francis Umesh, R. Soundiraraj, N. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Information Technology Madurai India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India

ISBN: (纸本)9798331523923

Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly impacts motor function. Early detection is critical for effective intervention, yet traditional diagnostic methods often face limitations in accuracy and timeliness. This paper proposes a hybrid deep learning model combining EfficientNet, BiLSTM, and GANs to improve PD detection accuracy using medical images and sequential data. EfficientNet is employed for extracting features from MRI scans, while BiLSTM is used to analyze temporal data, such as gait and speech patterns, to capture the progressive changes associated with PD. Additionally, GANs are utilized to augment the dataset, addressing the challenges of data scarcity and imbalance. The fusion of spatial and temporal features enables a more comprehensive understanding of PD, improving diagnostic performance. Bayesian optimization is applied for hyperparameter tuning, and K-fold cross-validation ensures robust model evaluation. The results demonstrate that the hybrid model outperforms single-modal approaches in both classification and regression tasks, offering enhanced accuracy in detecting PD and predicting disease severity. Furthermore, interpretability techniques like Grad-CAM and attention mechanisms are incorporated to provide insights into the model's decision-making process. This work presents a promising approach for automating PD diagnosis and lays the groundwork for further research in multi-modal deep learning for disease detection. © 2025 IEEE.

关键词： Deep learning

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R3 - Rescale, Rotate, and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyper-Chaotic Systems

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IEEE Access 2023年 11卷 119284-119312页

作者： Gabr, Mohamed Korayem, Yousef Chen, Yen-Lin Yee, Por Lip Ku, Chin Soon Alexan, Wassim Faculty of Media Engineering and Technology Computer Science Department Cairo11835 Egypt National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Faculty of Information Engineering and Technology Communications Department Cairo11835 Egypt New Administrative Capital Mathematics Department Cairo13507 Egypt

This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Chua system and the hyper-chaotic nature of the Chen system, the algorithm carries out rescaling, rotation, and randomization on the target image. The intrinsic unpredictability and sensitivity to initial conditions of these chaotic systems endow the encryption algorithm with an expansive key space of 25208. This feature not only bolsters its resilience against brute-force attacks but also magnifies its overall security profile. The algorithm's efficiency is evident in its rapid computational speed and lean resource consumption, making it suitable for real-time applications. To gauge its robustness, a battery of rigorous tests and analyses, spanning differential attacks, statistical attacks, and brute-force assaults, were employed. The results validate its ability to resist a diverse spectrum of threats. With its expansive key space, exceptional efficiency, and sturdy defenses, the proposed algorithm emerges as a potential cornerstone for safeguarding digital images in arenas like secure communication, data storage, and multimedia transmission. In sum, this research pushes the boundaries of high-security image encryption methodologies, catering to the burgeoning demands of the digital age. © 2013 IEEE.

关键词： Cryptography

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FPGA-Optimized Eight-Term Karatsuba Multiplications for Large Integer Multiplications

FPGA-Optimized Eight-Term Karatsuba Multiplications for Larg...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Singh, Shubham Patel, Saubi Kumar, Sumit Das, Monalisa Jajodia, Babita Indian Institute of Information Technology Department of Electronics and Communication Engineering Guwahati India

ISBN: (纸本)9798350309140

Karatsuba-like multiplication is one of the most efficient multiplication algorithms, but is mostly avoided due to complex sub-multiplications. This paper introduces FPGA-based optimized implementation of eight-term Karatsuba multiplier (ETKM) architecture on FPGAs. Thus, efforts has been made to implement eight-term Karatsuba Multiplication (Method-I), i.e., ETKM-I in terms of speed and hardware utilization. The overall performance of the proposed design method (ETKM-I) are noted by calculating Area-Time-Product (ATP) and compared with conventional eight-term Karatsuba multiplication (CETKM) and existing state-of-the-art. Hardware implementations of the proposed ETKM multiplication architectures are done using Virtex-7 FPGA device in Xilinx ISE platform. Compared with other state-of-the-art designs the performance of the proposed eight-term Karatsuba multiplication (Method-I): ETKM-I based on AT P1 is 6.227%, 76.402% and 33.668% better than Direct Multiplication (DM), Traditional Schoolbook Multiplication (Traditional SBM), and CETKM respectively for 1024-bit inputs. However comparing the proposed ETKM-I, it can be inferred that ETKM-I is 33.668% better than CETKM thus proving its area-time-product efficiency. © 2024 IEEE.

关键词： Integrated circuit design

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Falcon Optimization Algorithm-Based Energy Efficient communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in information technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

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computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of Information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

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Analysis of Indian Electoral Manifestos using Text Mining and Natural Language Processing 15

Analysis of Indian Electoral Manifestos using Text Mining an...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Rana, Rajwardhan Sharma, Sudeep Indian Institute of Information Technology Department of Electronics and Communication Engineering Surat India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370249

This study explores how text mining and NLP tools can help in textual analysis of political documents, particularly the political manifesto of Indian political *** objective is to identify party's biases and inclinations. Here we employed NLP to analyze the sentiments of N-Grams extracted from the party's manifesto. This study revealed that by comprehending sentiment-wise term frequency, voters and political pundits can extract significant insights from the manifesto to understand the political landscape thoroughly. Furthermore, such analysis can help think tanks, businesses, voters, media agencies, and political analysts to better understand any party's priorities and their level of commitment with respect to a particular issue compared to other parties. The objective of this study is only to propose a method hence authors has restrained from providing political opinion on the basis of result generated from the study. © 2024 IEEE.

关键词： Text mining Text analysis Media Natural language processing Object recognition Business

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Click Fraud Detection Using Ensemble Classifier 1

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Purwar, Archana Jain, Art Chawla, Indu Gupta, Ishita Raj, Mayank Jain, Divansh Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (数字)9789819748600

ISBN: (纸本)9789819748594

Online advertising fraud is an emerging issue and has drawn researchers’ mind recently because it now poses a significant danger to an online advertising sector. A content publisher signs an agreement to place advertisements on various websites in exchange for commissions on the basis of the amount of traffic these advertisements generate. It may sometimes motivate fraudulent publishers to produce unauthorized clicks on their websites in order to maximize traffic to the advertisements, a practice known as click fraud. Although click fraud is identified as a huge issue, there are not many algorithms that have been able to effectively solve this problem. Our aim in this paper is to overcome the problem of click fraud and come up with an explicit model for the click fraud detection. In this paper, a model is proposed using recursive feature elimination (RFE) and ensemble classifier with Hellinger distance-based decision tree (HDDT). The proposed model is evaluated over the dataset hosted by a Chinese company TalkingData available freely from Kaggle and has resulted in an accuracy of 99.72% as compared to the traditional decision tree. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Marketing

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Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

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computers, Materials & Continua 2023年第10期77卷 647-664页

作者： Fayaz Muhammad Jahangir Khan Asad Ullah Fasee Ullah Razaullah Khan Inayat Khan Mohammed ElAffendi Gauhar Ali Department of Computer Science and Information Technology Sarhad University of Science and Information TechnologyPeshawar25000Pakistan Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical *** primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune *** use of IIF for detecting autoimmune diseases is widespread in different medical *** 80 different types of autoimmune diseases have existed in various body *** IIF has been used for image classification in both ways,manually and by using the computer-Aided Detection(CAD)*** data scientists conducted various research works using an automatic CAD system with low *** diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)*** baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune *** technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the *** the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of *** augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL *** models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,***,DenseNet-121 shows the highest performance with suitable analysis of autoimmune *** overall performance highlighted that TL is a suitable and enhanced technique compared to its ***,the proposed technique is used

关键词： Indirect immune fluorescence computer-aided diagnosis transfer learning confusion matrix

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On Board Analysis and Evaluation of Battery EIS Data for Second Life Application 5

On Board Analysis and Evaluation of Battery EIS Data for Sec...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Khurana, Archit Dattaterya, Sneha Chatterjee, Gaurav The Lnm Institute of Information Technology Department of Electronics and Communication Engineering Jaipur India

ISBN: (纸本)9798350376753

This paper presents a novel onboard solution for evaluating and testing end-of-life (EoL) batteries, facilitating their efficient reuse and recycling. The proposed framework leverages machine learning (ML) algorithms to predict the State of Health (SoH) from Electrochemical Impedance Spectroscopy (EIS) data and classify batteries for potential second-life applications. To achieve this, we trained models using EIS and other relevant data obtained from testing conducted by various organizations, ensuring accurate SoH predictions and suitability assessments for reuse or recycling. The ML models were implemented on an STM32 platform, enabling real-time, onboard battery evaluation. Additionally, cloud connectivity can be integrated for comprehensive database management, remote monitoring, and accessible classification results. This research demonstrates the feasibility of using ML for onboard battery evaluation and testing, with the potential to significantly transform battery management and sustainability efforts. It has profound implications for resource optimization, waste reduction, and the development of circular economies within the battery industry. © 2024 IEEE.

关键词： Waste management

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