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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Agarwal, Rupanshi Bhardwaj, Isha Sharma, Ashish Kumar Sanghi, Akash Agarwal, Gaurav Invertis University Department of Computer Science and Engineering Bareilly India Kothiwal Institute of Technology and Professional Studeis Department of Applied Science and Humanities Moradabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Artificial intelligence (AI) is driving a massive shift in the agriculture industry, which is essential to both global food security and economic stability. This review article explores the use of AI technologies in agriculture, outlining their current and potential future applications while charting their evolution. We examine several AI applications such as computer vision, machine learning, robots and the (IoT) Internet of Things, highlighting their contributions to increasing efficiency, streamlining resource allocation, and strengthening decision-making. Machine learning techniques are utilized for predictive analytics in disease identification and crop yield estimation, while computer vision supports plant health monitoring and harvesting automation. Planting, weeding, and harvesting chores are revolutionized by robotics, and data collecting for precision agriculture is made easier by IoT devices in conjunction with sensors. Additionally, by offering indepth analyses of the soil and crops, drones and satellite imagery are essential to precision farming. The technological, financial, and social barriers to the broad adoption of AI are also covered in this study. These barriers include poor data quality, expensive implementation, and moral dilemmas with regard to labour displacement and environmental effects. In this paper, we propose that the future of global food security and sustainability depends on the strategic application of AI in agriculture. In order to fully realize the potential of artificial intelligence in agriculture Strategic implementation will determine the sustain ability and future of global food security. In this order, this study attempts to direct future research and policy development by offering a thorough overview of existing technology, applications, and difficulties. © 2024 IEEE.

关键词： Smart agriculture

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Text Summarization for Kannada Text Documents: A Review 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Veena, R. Ramesh, D. Hanumanthappa, M. Sri Siddhartha Academy of Higher Education Tumkur572105 India Department of Master of Computer Applications Sri Siddhartha Institute of Technology Tumkur572105 India Department of Computer Science and Applications Bangalore University Bangalore560056 India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

In recent times, we have observed the emergence of Automatic Text Summarization (ATS) systems, which are now being developed not only for the English language but also for low-resource languages. The growing quantity of textual information on the Internet, including vast archives of news articles, scientific papers, legal documents, and more, has made ATS increasingly essential. Manual text summarization is known to be time-consuming, costly, and often impractical when dealing with the massive volume of text available. Since the 1980s, research scientists have been dedicated to enhancing ATS techniques. These approaches are typically categorized as extractive, abstractive, or hybrid. The extractive methodology involves selecting the most crucial sentences from the input document(s) and concatenating them to create a summary. In contrast, the abstractive methodology rephrases the content of the input document(s) into an intermediate representation, generating a summary with sentences that differ from the original text. The hybrid methodology integrates extractive and abstractive methodologies. Despite the ongoing development of various methods, it is worth noting that the summaries generated by these systems still fall short of human-generated summaries. Most research efforts have concentrated on the extractive approach, but there is a pressing need to shift more attention towards abstractive and hybrid methodologies. This research endeavour aims to offer a comprehensive survey for researcher scientists by covering various aspects of ATS, including methodologies, techniques, building blocks, methodologies, datasets, evaluation methods, and the specific challenges that need to be addressed in the context of Kannada language text. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Text processing

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A Survey on Energy efficiency in Cloud Computing Frameworks at Different Platforms 5

A Survey on Energy efficiency in Cloud Computing Frameworks ...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Anul Solanki, Kamna Dhankar, Amita Uiet Maharishi Dayanand University Department Of Computer Science And Engineering Rohtak India Maharishi Dayanand University Department Of Computer Science And Applications Rohtak India

ISBN: (纸本)9798350330861

Cloud computing is a paradigm that offers a restricted set of virtualized resources that may be accessed as needed. It is crucial to allocate these resources on target machines in a manner that minimizes both inadequate utilization and excess utilization, as these conditions can result in substantial consumption of energy. The term 'nano data centers' refers to a network of widely dispersed computers that have the capability to both host and distribute material and solicitations in a peer-to-peer manner. The investigation of energy usage in nano servers has not been extensively explored, despite their increasing popularity. Several findings have been derived from our study, which highlight the aspects within the system design that enable nDCs to achieve lower energy consumption compared to their centralized counterparts. This paper presents a comprehensive definition of Cloud Frameworks, encompassing many platforms such as edge computing, green computing, parallel computing, distributed computing, and fog computing. In conclusion, this study highlights a number of obstacles and potential within the realm of cloud computing. It is our intention that this work will attract the interest of the academic community and serve as a catalyst for further investigation in this domain. © 2023 IEEE.

关键词： Fog computing

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An Analytical Model for QoS in Mobile Ad Hoc Network using Artificial Neural Network (ANN) 15

An Analytical Model for QoS in Mobile Ad Hoc Network using A...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kumar, Anil Shukla, R.K. Shukla, R.S. Department of Computer Applications Invertis University Bareilly India Department of Applied Science & Humanities Invertis University Bareilly India Department of Computer Science College of Computing and Informatics Saudi Electronic University Saudi Arabia

ISBN: (纸本)9798331300579

This study examines the challenge of predicting end-to-end packet delay in mobile ad hoc networks for Quality of Service (QoS) routing. Factors like node neighbors, interference, and medium access control impact delay unpredictability. We explore the potential of artificial neural networks to enhance accuracy in forecasting these delays, aiming to improve QoS within the dynamic Mobile Ad Hoc Network (MANET) context. We calculated delay using source-destination path length and average neighbors. Two models were built: Regression Neural Networks (RNN) and Radial Basis Function (RBF) networks. Using a network simulator, we gathered data sets for three routing protocols: AODV, DSDV, and DSR. These sets include delay, path length, and average neighbors. RNN outperformed RBF in predicting delay, as determined by various performance metrics. © Grenze Scientific Society, 2024.

关键词： Radial basis function networks

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Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware Strategies in Wireless Sensor Networks

Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agrawal, Navin Kumar Priya, Naincy Sinha, Priyanka Singh, Pranita Jain, Arpit Kumar, Munish GLA University Department of Computer Engineering & Applications U.P. Mathura India Muzaffarpur Institute of Technology Department of Computer Science & Engineering Bihar Muzaffarpur India Government Engineering College Khagaria Department of Computer Science & Engineering India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram AP India

ISBN: (纸本)9798350394412

In today's surplus world, wi-fi sensor networks are essential for many systems. This network encapsulates sensor nodes powered by irreplaceable batteries, preserving a fixed topology for displaying specific locations. Their efficiency makes it crucial for large data packages. Wireless sensor networks continuously analyze physical factors such as temperature, pressure, ambient soft, sunlight and humidity, dominating the process Energy consumption monitoring is a key task in this area. To address this framework, this paper proposes a different approach: a modified controlled, privacy-controlled approach to record collection. The method aims to reduce redundant sensitivity, thus increasing system lifetime by reducing the frequency of data transfer between nodes and large sinks A comparative study with existing methods shows how the proposed method is effective. Performance findings show that the proposed bundling method (MEBPP) provides high packet transmission values while consuming less power and extending network lifetime. © 2024 IEEE.

关键词： Data aggregation

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IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat Knowledge Delivery 16

IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat ...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Arikkat, Dincy R. Abhinav, M. Binu, Navya Parvathi, M. Biju, Navya Arunima, K.S. Vinod, P. Rehiman Rafidha, K.A. Conti, Mauro Cochin University of Science and Technology Department of Computer Applications Kerala India University of Padua Department of Mathematics Padua Italy

ISBN: (纸本)9798331505264

In the evolving field of cybersecurity, AI-driven chatbots are valuable. Unlike rigid rule-based bots, Large Language Model (LLM) based chatbots can provide flexible, context-aware responses across various domains. This research introduces IntellBot, a sophisticated cybersecurity chatbot built using advanced LLMs, Langchain, and a Retrieval-Augmented Generation model. IntellBot aggregates data from diverse sources, creating a comprehensive knowledge base on vulnerabilities, recent cyber threats, and emerging trends. It offers intelligent responses to enhance threat intelligence, incident response, and overall security awareness. A two-stage evaluation yielded high accuracy, with a BERT score above 0.8 and cosine similarity from 0.8 to 1, while RAGAS assessments scores consistently over 0.77, confirming the system's effectiveness. © 2024 IEEE.

关键词： Chatbots

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Machine and Deep learning in Biometric Authentication: A Review

Machine and Deep learning in Biometric Authentication: A Rev...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Singla, Divya Verma, Neetu Deenbandhu Chhotu Ram University of Science and Technology Department of Computer Science and Applications Murthal India

ISBN: (纸本)9798350396485

Biometric authentication is getting privileged as it allows authentication of a legitimate user without entering a personal identification number or password. Only a glance of any physical characteristics at a camera proves the user's identity making it secure from shoulder suffering attacks and theft. This review will explain the various biometric authentication techniques available for user authentication. Biometric authentication is generally based on two categories 1. Based on physical traits 2. Based on behavioral traits. A comprehensive comparison of all methods is discussed with their merits and demerits. Based on the comparison, we will provide insight into how machine learning algorithms help develop biometric authentication systems. We will also explore the various applications based on biometric authentication. © 2023 IEEE.

关键词： Authentication

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Classification of Skin Cancer Using Deep Learning 8th

Classification of Skin Cancer Using Deep Learning

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8th International Conference on Emerging Research in Computing, Information, Communication and applications, ERCICA 2023

作者： Nagarjun, A. Manju, N. Aradhya, V. N. Manjunath Shruthi, N. Malapriya, S. Department of Information Science and Engineering JSS Science and Technology University Mysuru India Department Computer Applications JSS Science and Technology University Mysuru India

ISBN: (纸本)9789819976324

Skin cancer is one of the most rapidly spreading illnesses in the world and because of the limited resources available, early detection of skin cancer is crucial accurate diagnosis of skin cancer identification for preventive approach in general. One of these models, Convolutional Neural Networks (CNN), has surpassed all others in object detection and classification tests. The dataset is screened from MNIST: HAM10000 which consists of seven different types of skin lesions with the sample size of 10,015. Transfer learning technique is used to train the model to obtain outstanding results. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

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computer Systems science & Engineering 2022年第6期41卷 891-902页

作者： Parul Gandhi Mohammad Zubair Khan Ravi Kumar Sharma Omar H.Alhazmi Surbhi Bhatia Chinmay Chakraborty Department of Computer Applications Manav Rachna International Institute of Research and StudiesFaridabad121006India Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia Department of Computer Applications Chandigarh Group of CollegesLandran140307India College of Computer Sciences and Information Technology King Faisal UniversitySaudi Arabia Department of Electronics&Communication Engineering Birla Institute of TechnologyMesra JharkhandIndia

Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.

关键词： Software quality reliability neural networks fuzzy logic neuro-fuzzy inference system

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Probabilistic Reliability via Subsystem Structures of Arrangement Graph Networks

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IEEE Transactions on Reliability 2024年第1期73卷 279-289页

作者： Huang, Yanze Lin, Limei Xu, Li Hsieh, Sun-Yuan Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou350117 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of (n, k)-arrangement graph A n, k, we focus on the subgraph reliability for A n, k under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in A n, k, and classify all the intersecting modes. Second, we focus on the probability P(q, A n, k n-1, k-1) with which at least one (n-1, k-1)-subarrangement graph is fault-free in A n, k, when given a uniform probability q with which a single vertex is fault-free, and we establish the P(q, A n, kn-1, k-1) by adopting the principle of inclusion-exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the P(q, A n, kn-1, k-1) under both models is very close to the asymptotic value by both theoretical arguments and experimental results. © 1963-2012 IEEE.

关键词： Fault tolerance

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