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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Naresh Kumar, U. Madhana Mohan, K. Pragadeeswaran, S. Scaria, Jobin M. Baseera, A. Seenivasan, D. Annamacharya University Department of Artificial intelligence & Data science Rajampet India Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India School of Information Technology Ibs University Mt. Eriama Campus Port Moresby Papua New Guinea School of Computing Science and Engineering Vit Bhopal University Sehore India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (纸本)9798350352931

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. Data preparation involves processing data before storing it machine-readable. SFFS and RFE select features. The model was trained using a hybrid ARIMA-LSTM technique, which blends deep learning and time-series forecasting. The ARIMA-LSTM model outperforms solo ARIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. Data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ARIMA-LSTM can boost agricultural productivity while minimizing environmental impact. © 2024 IEEE.

关键词： crop productivity long short-term memory (lstm) soil management

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Enhancing Security: A Hybrid Encryption Algorithm for Effective Key Generation

Enhancing Security: A Hybrid Encryption Algorithm for Effect...

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Recent Trends in Microelectronics, Automation, computing and Communications Systems (ICMACC), International Conference on

作者： Jeevan John Jacob Kiran P S Jaigovind B Shenoy Sagar Basavaraju Department of Computer Science and Engineering Amrita School of Computing Bengaluru India Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

The abstract Data security is of most importance, in the present day- the introduction of high-level wireless communication channels has led to generation and transfer of massive amounts of data. Data security must keep up with it. Data breach may cause disastrous outcomes. Previously, hardware security modules (HSM) were used to perform cryptographic actions- storing keys and for authentication. This is no longer feasible as HSMs are physical devices and become inconvenient to carry around all the time. The unavailability of HSM poses a serious security threat (for cryptographic keys). This paper aims at a novel software solution that compensates for hardware. As the algorithm focuses on securing keys used for encryption. It may be referred to as key security scheme (KSS). The paper proposes a way to generate keys instead of storing it and assumes performing of cryptographic actions on the available hardware in the system. The proposed method can secure data, both in transit and while at rest.

关键词： Wireless communication Performance evaluation Software algorithms Passwords Market research Software Hybrid power systems Encryption Microelectronics Security

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LOPE: A Low-Overhead Payment Verification Method for Blockchains

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Chinese Journal of Electronics 2021年第2期30卷 349-358页

作者： ZHANG Ying HE Zhengyuan JIA Linpeng DING Donghui ZHANG Hanwen SUN Yi School of Software Engineering Tongji University Blockchain Lab Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences

With its characteristics of decentralization,security, data traceability, and tamper-resistance, the blockchain has been widely used in various *** the difference in the performance of the devices, the light client is proposed so that devices without the ability to store a full blockchain copy can also participate in the blockchain transactions. However,the light client has to communicate with full nodes and verify the authenticity of a transaction which brings in some extent of communication, computation, and storage overheads to the light client. These overheads cannot be ignored for some low-performance devices, such as embedded devices or Io T chips, and therefore the current light client scheme does not work in this situation. We propose LOPE(a Low-overhead payment v Erification method) for poor-capacity nodes in the blockchain *** LOPE, a grouping protocol is designed to partition full nodes into groups to serve the verification requests of the light client. In addition, Practical byzantine fault tolerance(PBFT) is used to ensure the light client to get a credible result in spite of a few dishonest nodes existing in the group. We conduct LOPE and evaluate it in a testbed. The experiment results show that LOPE reduces more than half of the communication overhead, degrades the computation overhead of the light client to a large extent, and avoids the storage overhead of the hash roots of block headers in the light client. We also conduct theoretical analysis to show the performance improvement and security issues of LOPE.

关键词： current light client scheme storage overhead blockchains Low-overhead payment vErification method peer-to-peer computing software fault tolerance electronic money data integrity LOPE low-performance devices cryptographic protocols fault tolerance blockchain transactions

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A Novel Internet of Medical Thing Cryptosystem Based on Jigsaw Transformation and Ikeda Chaotic Map

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computer Systems science & engineering 2023年第9期46卷 3017-3036页

作者： Sultan Almakdi Mohammed SAlshehri Yousef Asiri Mimonah Al Qathrady Anas Ibrar Jawad Ahmad Department of Computer Science Najran UniversityNajran61441Saudi Arabia Department of Information Systems Najran UniversityNajran61441Saudi Arabia Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image *** picture encryption is a crucial step in many cloud-based and healthcare *** this study,a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things(IoMT)and cloud storage.A disorganized three-dimensional map is the foundation of the proposed *** dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding *** suggested cryptosystem enhances the security of the delivered medical images by performing many *** validate the efficiency of the recommended cryptosystem,various medical image kinds are used,each with its unique *** measures are used to evaluate the proposed cryptosystem,which all support its robust *** simulation results confirm the supplied cryptosystem’s ***,it provides strong robustness and suggested protection standards for cloud service applications,healthcare,and *** is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998,which is near its most excellent value of 8,and a typical NPCR value of 99.62%,which is also near its extreme value of 99.60%.Moreover,the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.

关键词： Jigsaw transformation cryptosystem image encryption medical images Ikeda map chaotic system

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Exploring Shopping Opportunities and Elevating Customer Experiences Through AI-Powered E-Commerce Strategies 4th

Exploring Shopping Opportunities and Elevating Customer Expe...

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Maurya, Nitu Singh, Jagendra Singh, Navneet Pratap Chaudhary, Anusha Patil, Sambhajiraje Tiwari, Mohit Department of Management KCC Institute of Legal and Higher Education Greater Noida India Alliance School of Advanced Computing Alliance University Bengaluru India School of Computer Science Engineering and Technology Bennett University Greater Noida India Delhi-NCR Ghaziabad India Dr. D. Y. Patil Vidyapeeth Centre for Online Learning Pimpri Pune India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (纸本)9789819769919

This research explores the efficacy of clustering algorithms in enhancing customer experiences within the e-commerce landscape. Through experiment trials utilizing K-means and DBSCAN clustering techniques, valuable insights have been gleaned. The trials yielded silhouette scores ranging from 0.55 to 0.72, indicating moderate to good clustering quality across different experiments. In K-means clustering, the number of clusters varied from 3 to 6, with inertia values spanning approximately 722.4–1456.8. Conversely, DBSCAN clustering resulted in varying cluster numbers, ranging from 2 to 4, contingent on the combinations of epsilon and min_samples values explored. These findings underscore the significance of judiciously selecting clustering algorithms and parameter settings to achieve meaningful segmentation of e-commerce data. Effective utilization of clustering algorithms empowers businesses to discern valuable insights into customer behavior, preferences, and patterns. Consequently, businesses can tailor their strategies to deliver personalized experiences, targeted marketing campaigns, and optimized product recommendations. This research propels the exploration of additional clustering techniques and parameter refinements for enhanced clustering performance in e-commerce applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Marketplaces

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CNN-based Deep Learning Approach for MRI-based Brain Tumor Detection 4

CNN-based Deep Learning Approach for MRI-based Brain Tumor D...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Narayanasamy, Kanagaraj Elangovan Sathish Kumar, L. Maragatharajan, M. Deepa, D. Karpagam Academy of Higher Education Department of Computer Science TamilNadu Coimbatore India Srm Institute of Science and Technology Department of Dsbs TamilNadu Kattankulathur India School of Computing Science and Engineering Vit Bhopal University MP Kotrikalan India Kongu Engineering College Department of Cse Tamil Nadu Perundurai India

ISBN: (纸本)9798350300888

Conventional brain tumor prediction method is a time-consuming and inaccurate process that involves manually evaluating a large number of MRI (magnetic resonance imaging) scan pictures in search of signs of brain cancer. This might have an effect on the patient's medical care. It may take a long time to analyse a large volume of scan data sets comprising brain pictures. Normal brain tissue and brain cancer tissue seem quite similar under the microscope, making it difficult to distinguish between the two during segmentation. A very accurate automated method for detecting cancer is crucial. This research study has developed a novel method that uses an algorithm to analyse 2D MRI human brain scans and detect brain Tumors. Combining conventional classification algorithms with deep learning methods, the approach makes use of a convolutional neural network (CNN). The model was successfully trained using a dataset of magnetic resonance imaging (MRI) scan images, including Tumors of varied sizes, locations, and intensities. Here, a SVM classifier (Support Vector Machine) and several activation methods are also used to check the results, including Softmax, RMSProp, and sigmoid. Our research showed that a CNN could attain an accuracy of 99.87%, which is higher than the present state-of-the-art findings. The CNN -based approach is meant to aid doctors in the precise identification of brain cancers in MRI scans. This innovation might greatly accelerate the pace at which patients are helped. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Retrieval-Augmented Generation for Multiple-Choice Questions and Answers Generation

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Procedia computer science 2025年 259卷 504-511页

作者： Pradeesh N Remya T MG Thushara K Arun Krishna Pranav V Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Omnex Software Solutions Pvt. Ltd Thoraipakkam Chennai India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Generating a large volume of diverse questions for educational purposes can be a challenging and time-consuming task for educators. Traditional methods like manually writing questions or using templates often fall short when it comes to variety and relevance. In order to cope with these di ffi culties, we have examined a di ff erent approach adopting Retrieval-Augmented Generation ( RAG). RAG is an enhancement technique that allows retrieving independent documents and combines it with artificial intelligence that is able to qualitatively generate contextually adequate questions. In this research, RAG was employed through the Ample LMS platform in which PDF documents were the source of generating MCQ questions. The results of the research indicate that with this method, the process of formulating the questions is relatively faster and the questions generated are of better and diverse in nature. With RAG, the burden of constructing questions is lessened for teachers and the students are provided with better interactive learning opportunities. This approach presents a better alternative that is flexible and suitable for various educational requirements, demonstrating the e ff ectiveness of AI in the improvement of the learning and teaching processes.

关键词： RAG Education Question Generation LLM

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Catalyzing Epilepsy Detection: A Deep Learning Approach with LSTM Networks for EEG Analysis 3

Catalyzing Epilepsy Detection: A Deep Learning Approach with...

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3rd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024

作者： Chandramouli, Akshayata Jain, Garima Victor, Merlyn Halder, Ananya Jain, Sanat VIT Bhopal University School of Computing Science Engineering and Artificial Intelligence Sehore India VIT Bhopal University School of Computer Science and Engineering Sehore India

ISBN: (纸本)9798350391244

Epilepsy is a prevalent neurological disorder, affecting around 50 million people globally. Detecting it accurately and promptly is crucial for effective patient care. However, traditional methods often find this challenging due to the complex and variable nature of EEG patterns in epilepsy. Our study introduces a groundbreaking approach that leverages deep learning, particularly LSTM networks, to address this challenge. LSTM networks excel at capturing long-term dependencies in sequential data, which makes them particularly suitable for analyzing EEG signals, which are essentially time-series data. Our work rigorously trained and evaluated LSTM model to distinguish epileptic form and non-epileptic brain activity. After extensive testing and cross-validation, our model detected epilepsy with 90% accuracy. The findings demonstrate that LSTM networks may accurately diagnose epilepsy and improve patient treatment and management. This epilepsy detection breakthrough could improve the lives of millions suffering by this neurological illness. © 2024 IEEE.

关键词： Electrotherapeutics

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"Socially Assistive Robot Privacy Model": A Multi-model Approach to Evaluating Socially Assistive Robot Privacy Concerns 16th

"Socially Assistive Robot Privacy Model": A Multi-model Appr...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Collins, Sawyer Stanojević, Čedomir Bennett, Casey Henkel, Zachary Henkel, Kenna Baugus Abbott, Nikki M. Bethel, Cindy L. Śabanović, Selma Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States College of Behavioral Social and Health Sciences Clemson University ClemsonSC United States College of Computing and Digital Media DePaul University ChicagoIL United States Department of Computer Science and Engineering Mississippi State University StarkvilleMS United States

ISBN: (纸本)9789819635214

As socially assistive robots (SARs) enter more diverse care settings, including users’ homes, it is critical to identify the shifting privacy risks and dimensions of privacy this technology and its data collection capabilities may affect. We propose a new model of privacy to address the complex nature of SARs as a multimodal technology within the healthcare space. To construct this new model, we combine a previous three-dimensional model from the healthcare literature and synthesize it with a seven-dimension technology-related model. We then use this new combined model to analyze self-report data from several workshops with prospective users of the dog-like robot Therabot to map out the dimensions of privacy identified as future concerns for clinicians and those living with depression. Finally, we suggest this model can be used in future studies to support the in-depth exploration of privacy implications of SARs within healthcare through discussions about privacy among clinicians, those receiving care, and robot designers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Catalyzing Epilepsy Detection: A Deep Learning Approach with LSTM Networks for EEG Analysis

Catalyzing Epilepsy Detection: A Deep Learning Approach with...

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Artificial Intelligence, Computational Electronics and Communication System (AICECS), International Conference on

作者： Akshayata Chandramouli Garima Jain Merlyn Victor Ananya Halder Sanat Jain School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore India School of Computer Science and Engineering VIT Bhopal University Sehore India

ISBN: (数字)9798350391244

ISBN: (纸本)9798350391251

关键词： Deep learning Accuracy Epilepsy Neuroinformatics Brain modeling Electroencephalography Object recognition Long short term memory Testing Synthetic data

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