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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Dhondiyal, Shiv Ashish Kumar, Rohit Verma, Himanshu Dhyani, Ashish Singh, Sumeshwar Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India GNIOT Institute of Professional Studies Greater Noida India Dept. of Hospitality Management Graphic Era Deemed to be University India Dept. of Computer Science and Engineering Jaypee Institute of information technology sector 62 India

ISBN: (纸本)9798350383867

The main goal of this attempt is to assess the quality of wine and determine if it is excellent or terrible. Humans have been engaging in the act of tasting for generations and have consistently made predictions using their sensory organs. A number of industries have recently embraced new technology and used it to a variety of uses. However, human knowledge is required in a number of businesses, such as product quality assurance. With the increasing popularity of the product, modern innovations have increased the cost of the treatment. To ensure the quality of the output, this study looks at a number of machines learning algorithms, including Random Forest Classification, Decision Tree classifiers, and Support Vector Machines (SVM). These methods make use of the product's current attributes to simplify the quality control procedure. © 2024 IEEE.

关键词： Adversarial machine learning

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Exploring Mobility and Scalability of Cloud Computing Servers Using Logical Regression Framework 2

Exploring Mobility and Scalability of Cloud Computing Server...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sriramulugari, Shiva Kumar Gorantla, Venkata Ashok K Gude, Venkataramaiah Gupta, Ketan Yuvaraj, N. Solution Architect United States Software Engineer United States University of the Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Department of Cse Coimbatore India

ISBN: (纸本)9798350371055

This research paper explores the possibilities of using the Logical Regression Framework as a means of controlling the scalability and mobility of cloud computing servers. The aim of this research paper is to identify the key factors that serve as predictors of good scalability and mobility performance in cloud computing servers. In doing so, the research undertakes a comparative analysis of different cloud computing architectures in order to identify which architectures exhibit better scalability and mobility performance. This comparative evaluation is done through the use of a Logical Regression Framework, which is based on two main data sources: (1) the server performance logs and (2) the server utilization rate. Each of these datasets is explored in dept. to find correlations between certain features and scalability/mobility performance. Using this information, an optimal cloud architecture is determined that shows better scalability and mobility characteristics. The hope is that this research can help cloud computing architects and engineers to design and implement better architectures for their cloud-based applications. © 2024 IEEE.

关键词： Automation Cloud Computing Logical Regression Mobility Performance Scalability

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Implementing RDMA over Soft-RoCE with Basic eBPF Monitoring: A Cost-Effective Approach for High-Performance Data Communication

Implementing RDMA over Soft-RoCE with Basic eBPF Monitoring:...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Suresh Kagal M S Narasimha Murthy Tejas Mutalikdesai V Yugal Kishore T V Tejaswini Dept. of Information Science & Engineering BMS Institute of Technology and Management Bengaluru Karnataka India Dept. of Information Science & Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

RDMA is a technology that enables extremely fast communication between different systems by allowing these systems to transfer data without using the CPU and operating system. This results in the rapid transfer of data and reduces latency in data transfer. RDMA, brings more efficiency in moving the data between systems, improving general performance and responsiveness in many applications as CPU is not involved in the data transfer. Traditional RDMA solutions usually require special and costly hardware, such as RoCE adapters. This can make them less accessible for companies with lesser budgets. Soft-RoCE is a software-based solution that brings RDMA capabilities to standard Ethernet hardware. In this way, we can replicate the benefits that RDMA offers without the expensive equipment, hence making it possible in environments where it would not have been feasible. Also, an innovative monitoring framework using eBPF is introduced, which offers comprehensive real-time intelligence to RDMA performance by decreasing the latency and throughput.

关键词： Scalability Ethernet Throughput Data transfer Hardware Real-time systems Resource management Sustainable development Monitoring Standards

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Advancements in Jute Leaf Disease Detection: A Comprehensive Study Utilizing Machine Learning and Deep Learning Techniques 2

Advancements in Jute Leaf Disease Detection: A Comprehensive...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Haque, Rezaul Miah, Md Miraz Sultana, Shayma Fardin, Hasib Noman, Abdullah Al Al-Sakib, Abdullah Hasan, Md Kamrul Rafy, Al Shihabur, Rahman Md Rahman, Shafiur East West University Dept. of Cse Dhaka1212 Bangladesh Brac University Dept. of Cse Dhaka1212 Bangladesh International American University Dept. of Management Information System CA90010 United States Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University Dept. of Information Technology CA92614 United States Touro University Dept. of Cyber Security CA94592 United States Kyungdong University Dept. of Computer Engineering Gangwon-do Korea Republic of Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331517984

Detecting diseases in jute leaves is difficult due to the variability in how the diseases appear. Manually identifying these diseases is challenging because it requires expert knowledge and visual inspections take a lot of time. Machine Learning (ML) offers a promising solution to these challenges by automating the detection process. However, research in this area is limited due to the lack of specific datasets. This study aims to address this by creating a comprehensive dataset of 10,800 high-quality images of jute leaf diseases. The main goal is to develop a robust system for classifying jute leaves into three categories: Yellow Mosaic, Powdery Mildew, and Healthy. Our methodology involved extensive image preprocessing, including resizing and various augmentation techniques, to enhance the dataset's diversity and ensure model robustness. We trained various ML and Deep Learning (DL) models and conducted a comparative analysis of their performance. Additionally, we compared our approach with the state-of-the-art methods. The results showed that DL models, particularly Inception V3, achieved an outstanding accuracy of 99.98%, compared to 89.75% for Random Forest (RF). This highlights the potential of DL techniques in improving the accuracy of jute leaf disease detection. Our findings contribute to better disease management strategies and increased productivity in jute cultivation. © 2024 IEEE.

关键词： Adversarial machine learning

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Block-Hash Signature (BHS) for Transaction Validation in Smart Contracts for Security and Privacy using Blockchain

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Journal of Mobile Multimedia 2023年第4期19卷 935-962页

作者： Bhatnagar, Sonika Dayal, Mohit Singh, Deepti Upreti, Shitiz Upreti, Kamal Kumar, Jitender Dept. of Computer Science & Engineering Government Polytechnic Baheri Bareilly India Dept. of Applied Science Bharati Vidyapeeth’s College of Engineering New Delhi India Dept. of Information Technology ABES Institute of Technology Ghaziabad India Dept.of Information Technology Asian Education Group (AEG) Noida India Dept. of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Dept. of Computer Science & Engineering J.C Bose University of Science & Technology YMCA Haryana Faridabad India

Some of the well-known signature techniques like Winternitz and Lamport are not considered to be very appropriate for the usage of hashing or smart contracts in Blockchains security because of their size O(n²), which is prominently too high. Although in Blockchain, the security concern is on the top priority because of its distributed P2P design still, the security enhancement is required to sign and verify the documents forwarded to the peers, especially in Hyperledger Fabric. Here, this paper presents a new signature technique “Block-Hash” to enhance Blockchain security by using it in smart contracts as well as hashing with size 3Xn bits (n = 256, generally for SHA-256 Hashing) and which can score 112 bits security. The proposed signature can be used appropriately for signing a smart contract by the endorser or committer node. Also, it can be used with a hash algorithm in forming a Merkle tree. Apart from the description and implementation of Block-Hash Signature, this paper has covered the analysis of its security and correctness measures with a table for result comparison. © 2023 River Publishers.

关键词： blockchain security Digital signature hyperledger fabric smart contract

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Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning Models 3

Effective Disease Recognition in Cucumbers: A Web-Based Appl...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahman, Md Shahidur Ahamed, Asif Pranto, Md Nuruzzaman Islam, Md Ariful Al Masum, Abdullah Al-Sakib, Abdullah Rahman, S.M. Arafat Haque, Rezaul Rahman, Shafiur Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University College of Technology and Engineering CA92614 United States International American University Dept. of Business Administration CA90010 United States Westcliff University Dept. of Information Technology CA92614 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health. © 2024 IEEE.

关键词： Feature Selection

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Advancing Speech Therapy for Sinhala-Speaking Children with Autism Spectrum Disorder Through an Intelligent Dialog System

Advancing Speech Therapy for Sinhala-Speaking Children with ...

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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： Aparna Jayawardena Koliya Pulasinghe Samantha Rajapakshe Dept. of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331523268

ISBN: (纸本)9798331523275

This paper presents a dialog system integrated with a NAO socially assistive robot, designed to support Sinhala-speaking children with Autism Spectrum Disorder (ASD). The system leverages a pipeline-based architecture implemented using the RASA framework, consisting of Natural Language Understanding (NLU), Dialog management (DMU), and Natural Language Generation (NLG) units. The NLU unit processes user input by identifying intents, entities, and dialogue acts, incorporating custom tools like the SpokenSinhalaVerbTokenizer for handling spoken Sinhala. The DMU includes a Dialog State Tracker (DST) to maintain conversation context and a Dialog Policy Generator, which employs rule-based, TED, and UnexpecTED policies to adapt conversation flows dynamically. The NLG unit generates natural responses to foster interactive and goal-oriented conversations. Integrated with the NAO robot, the system engages children through meaningful dialogues, such as discussing toy preferences, aiming to enhance social interaction and communication skills. This work highlights the potential of conversational AI and robotics in therapeutic interventions for ASD in low-resource languages.

关键词： Autism Accuracy Natural language generation Toy manufacturing industry Medical treatment Oral communication Systems engineering and theory Solids Reliability Robots

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A Review on the Minimization of Energy Consumption in Blockchain technology 8

A Review on the Minimization of Energy Consumption in Blockc...

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8th International Conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2025

作者： Paul, Aniket Das, Baisakhi Institute of Engineering and Management Kolkata Dept. Information Technology Kolkata India

ISBN: (纸本)9798331520762

Blockchain is considered one of the most innovative technologies and applied across various fields with applications in, for example, education, business, and many more. First introduced as the technology that underlies Bitcoin, blockchain has since evolved to an important means of safekeeping digital information without involving intermediaries. With growing popularity among businesses, there are rising concerns about environmental impacts, particularly energy usage. Blockchain is known for extremely high energy consumption because of the agreement methodologies applied, specifically Proof of Work(PoW). The paper addresses blockchain technology, namely the various algorithms generated and their influence on the environment. It also looks into emerging concepts and strategies in reducing the energy impact of blockchain technology, including moving to Proof of Stake (PoS), hybrid consensus approaches, and exploiting renewable energy sources. © 2025 IEEE.

关键词： Effluents

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Modification of an Ontology to Incorporate Clinical Process of Cardio Vascular Disease Detection with a Case Study of Automated Prediction of Cardiomegaly Using Machine Learning Models

Modification of an Ontology to Incorporate Clinical Process ...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Poly Sil Sen Subhasis Mitra Papia Kar Mondal Dept. of Information Technology Future Institute of Engineering and Management Kolkata India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Healthcare is providing us with an ocean of data and therefore an ocean of opportunities. Electronic health data generation is growing with a maximum growth rate in comparison to other domains of electronic data. Death and physical disability due to Cardio Vascular Diseases are common nowadays irrespective of the obvious medical causes and medical parameters. Many research works have been done in this domain but the data set does not contain the needed medical parameters or the health data signals in most of these works. In this paper, we study general machine learning and deep learning works in this domain and also the research works that deal with actual medical parameters and signal. An existing ontology is enhanced to accommodate clinical process needed including types of investigations for detection of cardiovascular diseases. Finally, Cardiomegaly has been detected from a data set obtained from Chest X-Ray PA View using Neural Network, SVM, XGBoost, Logistic Regression. The objective of this research is to find out ways of improving the existing healthcare services using the power of Artificial Intelligence considering the scarcity of needed healthcare services, infrastructure and experts in many places and at many times.

关键词： Deep learning Support vector machines Logistic regression Oceans Neural networks Medical services Ontologies Predictive models Data models X-ray imaging

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Moving Robots in Unknown Environments Using Potential Field Graphs 2

Moving Robots in Unknown Environments Using Potential Field ...

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2nd International Conference on Advanced Research in Computing, ICARC 2022

作者： Panagoda, Mithila Lokuliyanage, Minuli Senarath, Amadi Nisansala, N.K.V.M. Nethmini Rajapakshe, R.W.A.D.U. Rajapaksha, U.U. Samantha Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dept. of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Deprt. of Computer System and Engineering Malabe Sri Lanka

ISBN: (纸本)9781665407410

The purpose of this research paper is to introduce a new navigation algorithm for Robot Operating System (ROS) based robots which will allow complete autonomous traversal in any given indoor environment. Turtle bot3 burger bot is the sample robot chosen for this project. This will be equipped with a Light Detection and Ranging (LIDAR) scanner with the default settings, ultrasound, Intel Real Sense camera, etc. Environment information gathered via these sensors will be sent into a remote computer for processing since the raspberry pie micro-controller with the turtle bot burger is not enough to carry out a high computation-intensive task. Once the information is received series of tasks will be carried out by the remote personal computer before executing the newly developed navigation algorithm. The data from the LIDAR scanner help to generate a 2-dimensional(2D) potential field graph. Then it will later be used for path planning by navigation algorithm. Furthermore, the Real sense camera is used for object boundary detection to produce more accurate data for the navigation algorithm. Once all the data proceed, data will return to the robot, where the navigation algorithm will start execution in parallel with a recovery behavior algorithm and scan algorithm. The recovery behavior algorithm is responsible for guiding the robot to face away from an obstacle in an event where the predetermined path is obstructed. Furthermore, it is responsible for maintaining accurate data in the cost map. Subsequently, the scanning algorithm will be used to generate maps. © 2022 IEEE.

关键词： Cameras

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