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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Subramanya, S.G. Parkavi, A. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Grape farming is a globally significant agricultural practice, but grapevines frequently encounter viral, fungal, and bacterial infections that compromise crop quality and yield. Conventional disease detection methods are labor-intensive and require significant expertise. This study investigates deep learning techniques, focusing on EfficientNet models, to automate grape leaf disease identification and classification. The transfer learning approach was applied to train and fine-tune EfficientNet models on a publicly available dataset with four classes: black rot, leaf blight, esca, and healthy leaves. A model performance was measured through accuracy and loss metrics. EfficientNetB0 achieved accuracy of 92.45% and 96.73%, and loss of 0.197 and 0.098, on training and validation data, respectively. However, EfficientNetB5 and EfficientNetB7 gave validation accuracies of 96.07% and 96.39%, respectively. The accuracy and loss trends demonstrate that EfficientNetB0 outperforms the other two counterparts. This approach highlights the potential of EfficientNetB0 for accurate, efficient grape disease detection and classification. © 2025 IEEE.

关键词： Plant diseases

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Developing a method to detect driver drowsiness based on a single EEG channel and discriminated features

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International Journal of Intelligent Systems Technologies and Applications 2024年第1期22卷 29-40页

作者： Hussein, Raed Mohammed George, Loay E. Miften, Firas Sabar Iraqi Commission for Computers and Informatics Informatics Institute of Postgraduate Studies Baghdad Iraq Computer Sciences Department University of Information Technology and Communication Baghdad Iraq Computer Sciences Department College of Education for Pure Science University of Thi-Qar Thi-Qar Iraq

Driver drowsiness is one of the leading causes of road deaths and transportation industry dangers. Due to its direct evaluation of neurophysiological brain activity, electroencephalography (EEG) has been regarded as one of the most reliable physiological indicators for identifying driver drowsiness. This study proposes a straightforward, cost-effective method for detecting driver drowsiness using a single channel. The contribution of this research is the discovery of drowsiness using discriminated features [moments features (M1, M2, M3, M4), roughness features (R1, R2, R3, R4), zero crossing rate (ZCR), sample entropy (SE) and median absolute deviation (MAD)] from publicly available datasets. A novel model was introduced in this study, which involved the fusion of wavelet transform Daubechies order 4 (WTDB4) and residue decomposition (RD) techniques for feature extraction. Various classification algorithms, including the least-square support vector machine (LSSVM) and ensemble models were compared in terms of their performance metrics. The algorithm that exhibited superior accuracy with reduced computational time was chosen to classify the driver's status into two groups: awake and drowsy. Notably, the proposed model achieved an impressive accuracy of 97.95%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Electroencephalography

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Micro-expressions: a survey

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Multimedia Tools and Applications 2024年第18期83卷 53165-53200页

作者： Jain, Ankita Bhakta, Dhananjoy Department of Computer Science and Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi834010 India

This paper surveys some of the most important recent works related to micro-expression analysis. It includes discussions on algorithms for spotting and recognizing micro-expressions, their performances, databases, and new feature descriptor frameworks. Here, we use a reverse chronological order to discuss the state-of-the-art in a work-specific fashion. We provide a questionnaire-based comparative study of the literature and discuss future directions of micro-expression analysis. We expect this survey to expose micro-expressions to those interested in pursuing research in this emerging field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Machine learning

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A review on Gujarati language based automatic speech recognition (ASR) systems

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International Journal of Speech technology 2024年第1期27卷 133-156页

作者： Dua, Mohit Bhagat, Bhavesh Dua, Shelza Chakravarty, Nidhi Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Electronics Communication and Engineering Punjab Engineering College Chandigarh Chandigarh India

Automatic speech recognition (ASR) plays a crucial role in facilitating natural and efficient human–computer interaction. This paper offers a comprehensive review of ASR systems tailored specifically for the Gujarati language. Existing literature on Gujarati ASR indicates that most survey papers have focused solely on back-end classification methods. This paper aims to fill this gap by presenting a comprehensive survey that encompasses feature extraction techniques, backend models, speech datasets, and evaluation metrics. This review provides an in-depth analysis of the cutting-edge methodologies employed in the development of Gujarati ASR systems. Firstly, the paper discusses various feature extraction techniques. Secondly, it covers classification models and their impact on ASR performance. Thirdly, the study delves into available speech datasets relevant to the Gujarati language, offering valuable insights for researchers and practitioners. Finally, the paper reviews the evaluation parameters used in ASR systems. Additionally, it gives an overview of online toolkits, resources, and language models pertinent to Gujarati ASR. This review serves as a comprehensive reference for academics and professionals involved in the research and development of ASR systems for the Gujarati language. The parameters selected for comparison include front-end feature extraction methods, back-end classification techniques, speech dataset, and evaluation metrics. Furthermore, the paper discusses both the contributions and limitations encountered by current ASR systems during the review. Finally, it addresses various challenges that still persist and provides directions for future research in this critical field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Large Scopus Data Sets and Its Analysis for Decision Making

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Annals of Data Science 2024年第2期11卷 589-618页

作者： Singh, Prem Kumar Department of Computer Science and Engineering Gandhi Institute of Technology and Management-Vishakhapatnam Andhra Pradesh530045 India

Recently several authors paid attention towards Scopus Data analysis for intellectual measurement of institutes or authors. It is well known that the SCOPUS contains more than 34,346 peer reviewed Journals from different subjects with 3 lakh conferences. It is difficult to measure the performance or expertise of any institute or author in the given domain for admission, job, and ranking or other decision making process. The reason is several manipulations started in document and citation count via strategic authors or institute which can be measured via average author publications, number of funding, collaborations and retracted papers. It is happening due to rogue editor or business strategic of educationalist for profit. However these types of misconduct impacts lot to real researcher which forces brain drain. To resolve this issue, the current paper provides a way to measure the intellectual achievement of an institute or author based on several metrics. The proposed method is illustrated using the SCOPUS data sets and it’s metric for critical understanding. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Decision making

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Comparative Analysis of Machine Learning, Ensemble Learning and Deep Learning Classifiers for Parkinson’s Disease Detection

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SN computer Science 2024年第1期5卷 1-14页

作者： Goyal, Palak Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

A progressive neurodegenerative ailment called Parkinson's disease (PD) is marked by the death of dopamine-producing cells in the substantia nigra area of the brain. The exact etiology of PD remains elusive, but it is believed to involve the presence of Lewy bodies, abnormal protein aggregates, in affected brain regions, leading to the mobile symptoms of PD. Hence, as the management of PD continues to evolve, there is a growing demand for the establishment of a descriptive system that enables the early detection of PD. In this study, we conducted an extensive analysis using machine learning, ensemble learning, and deep learning models with different hyperparameters to develop accurate classification models for PD prediction. To enhance classifier performance and address overfitting, we employed principal component analysis (PCA) for feature selection along with various preprocessing techniques. The dataset used consisted of voice samples, comprising 188 PD patients and 64 normal individuals. Our results demonstrated that the Random Forest (RF) model with accuracy of 82.37% outperformed the other base classifiers Among the ensemble classifiers, the LGBM model exhibited the highest accuracy of 85.90% when compared to both base and ensemble classifiers. Notably, the deep learning model has 91.33% training accuracy and 85.02% testing accuracy, suggesting that deep learning models perform comparably equivalent on small datasets compared to machine learning classifiers. Overall, our findings underscore the effectiveness of machine learning, ensemble techniques and deep learning models in accurately predicting PD. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Hyperparameters LGBM Machine learning Parkinson’s disease Random forest

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Function Scheduling with Data Security in Serverless Computing Systems 17

Function Scheduling with Data Security in Serverless Computi...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Saha, Srestha Pandey, Arvind Addya, Sourav Kanti Brata Nath, Shubha Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India National Institute of Technology Karnataka Department of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798331531195

In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user's QoS. © 2025 IEEE.

关键词： Simulated annealing

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A Novel Simulated Approach to Secure Multi-Tenant Distributed Systems using Anomaly Score based Virtual Machine Live Migration 3

A Novel Simulated Approach to Secure Multi-Tenant Distribute...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Patil, Pravin Kale, Geetanjali Karmarkar, Tanmay Pune Institute of Computer Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350354348

Now a days, cloud computing services are being used exponentially which in turn increases in multi-tenant distributed service(MTDS). Targeted attacks in MTDS propagated through side channel, shared memories, network access, and configuration. These attacks could be discovered after analyzing side channel data. This paper proposes a novel anomaly approach for virtual machine placement (VM) considering anomaly scores of VM and the CPU utilization of the servers. The result reveals that the proposed approach ensures secure VM migrations among the servers during VM placement, and also ensures that the MTDS environment remains secure. © 2024 IEEE.

关键词： Virtual machine

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Biomedical signal processing for health monitoring applications: a review

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International Journal of Applied Systemic Studies 2023年第1期10卷 44-69页

作者： Sonawani, Shilpa Patil, Kailas Natarajan, Prabhu School of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University India Department of Computer Engineering Vishwakarma University India Department of Computer Science DigiPen Institute of Technology Singapore

Biomedical health monitoring systems are evolving rapidly and using non-invasive and cost effective sensors. These systems can monitor physiological parameters of the body to monitor health conditions and provide feedback. These new generation systems can use advance technologies to build intelligent systems for health monitoring and timely diseases detection and diagnosis. Biomedical signal processing and analysis of the patterns in the signals plays important role in building efficient systems, life sciences and research and is rapidly expanding in this domain. This review paper is significant as no existing review paper is giving complete information on biomedical signal processing phases as a whole. It makes four contributions;first, it gives activity flow for developing biomedical signal processing systems. Second, reviews various recent applications researched and kinds of low cost, non-invasive sensors used for biomedical health monitoring. Third, categorisation and enlisting of signal processing techniques like signal sampling, segmentation, filtering, feature extraction, dimensionality reduction and machine learning techniques in healthcare domain. Fourth, it reviews challenges in using signal processing techniques and gives future perspectives which can be helpful for the researchers to use advance techniques to build intelligent systems for reliable decision making. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Cost effectiveness

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MACPGANA: design of a highly efficient multimodal agriculture commodity price prediction model via generative adversarial networks & autoencoders

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International Journal of Information technology (Singapore) 2025年第4期17卷 2355-2368页

作者： kambo, Rubi Pillai, Jyothi Soni, Sunita Urkude, Giridhar Department of Computer Science and Engineering Bhilai Institute Technology Chhattisgarh Durg India Department of Computer Science and Engineering Rungta College of Engineering and Technology Chhattisgarh Bhilai India Symbiosis Institute of Technology Nagpur Campus Symbiosis International(Deemed University) Pune India Department of Computer Applications Bhilai Institute of Technology Durg Chhattisgarh India

This paper presents MACPGANA, a novel multimodal agriculture commodity price prediction model leveraging Generative Adversarial Networks (GAN) and Autoencoders (AEs). The model integrates multimodal data to extract and represent features efficiently, employs GAN for feature selection, and uses predictive autoencoders to estimate inter-day price movements. A Q-Learning-based feedback loop ensures continuous optimization of prediction performance. MACPGANA achieves high accuracy, precision, and recall while maintaining low computational complexity, making it suitable for real-time and large-scale applications. The model’s effectiveness is demonstrated across diverse commodities, highlighting its potential for scalable and efficient agricultural price prediction. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Agriculture commodity price prediction Autoencoders (AE) Generative adversarial networks (GAN) Multimodal feature extraction Q-learning optimization Real-time price forecasting

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