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7th International Conference on Image Information Processing, ICIIP 2023

作者： Madasamy, Sridhar Ramesh, P.S. Roy, Priyanka Arun Raj, S.R. Yadav, Vinay Kumar balaram, Allam Cognizant Technology Solution 500 Frank W. Burr Boulevard TeaneckNJ07666 United States Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamilnadu Chennai India Vellore Institute of Technology Department of Electrical And Electronics Engineering Tamilnadu Vellore India U.B.D.T College of Engineering Department of Electronics & Communication Engineering Karnataka Davanagere577004 India Glocal University Glocal School of Science And Technology Uttar Pradesh India Mlr Institute of Technology Department of Computer Science And Engineering Hyderabad500043 India

ISBN: (纸本)9798350371406

Factories that prioritize digital transformation experience heightened production rates and gain a competitive advantage by enhancing control and operational efficiency. In this study, image processing techniques were employed to achieve digital transformation objectives, and the data obtained was transmitted via 5G communication to a collaborative robot arm, enabling remote control. The robot arm was fitted with a 3D-printed humanoid hand for bin picking, where each finger was controlled by five servo motors. To manipulate the fingers, a user donned a glove equipped with flex sensors, which conveyed the finger positions to the servo motors, facilitating precise pick and place operations. Additionally, image processing was utilized to accurately control the robot arm's position. The research involved the implementation of tuned YOLO V5 (TYOLOV5) algorithms, using Python software, for object detection within the user's gloves. by comparing this approach with other existing methods, the experiment achieved the highest detection accuracy, demonstrating the effectiveness of the proposed approach in achieving superior recognition and estimation compared to traditional and existing deep learning methods. To validate its efficacy, experiments were conducted in a laboratory setting, where a collaborative robot was deployed for a small part assembly task. The results conclusively highlight the potential of this approach to significantly enhance recognition and estimation capabilities, especially in the realm of human-robot collaboration. © 2023 IEEE.

关键词： Image enhancement

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NbSOC framework for team structure to develop blockchain-based applications

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International Journal of Industrial and Systems engineering 2024年第1期46卷 126-149页

作者： Joshi, Nitish Khanna, Tejaswi bali, Vikram bali, Shivani Oracle Tech Hub Marathahalli - Sarjapur Outer Ring Rd Kadubeesanahalli Karnataka Bengaluru560103 India Computer Science and Engineering Department Sharda University Uttar Pradesh Greater Noida India 1404 Shri Radha Sky Garden Sec. 16BWest Greater Noida201308 India IMS Engineering College NH-09 Near Dasna Adhyatmik Nagar Uttar Pradesh Ghaziabad201015 India Jaipuria Institute of Management A-32A Sector-62 Uttar Pradesh Noida201309 India

For the development of blockchain applications, platforms have been implemented by corporates like IbM, Oracle, Amazon, etc. blockchain comprises smart contract development, which gets deployed over a peer-to-peer network. basic skills about blockchain are still lacking amongst the developer community and all the people involved in developing blockchain applications. This paper proposes an NbSOC framework for organising teams to build blockchain-based applications. This framework has been used to create a team structure for implementing a land record management system. The authors have addressed the implementation challenges, cost, roles and responsibilities of an individual in the blockchain development environment. © 2024 Inderscience Enterprises Ltd.

关键词： Smart contract

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A randomized algorithm based approach to fault detection for uncertain systems subject to deception attack

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Journal of the Franklin Institute 2024年第18期361卷

作者： Zhao, Zhen Liu, Peter Xiaoping Gao, Jinfeng School of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China Department of Systems and Computer Engineering Carleton University OttawaON K1S 5B6 Canada

This paper presents a fault detection strategy for discrete uncertain systems subject to deception attack based on the probabilistic framework. Probabilistic parameter models are utilized for characterizing model uncertainties, multiplicative faults and additive faults. An observer-based fault detection filter is constructed to generate fault information under deception attack. A randomized algorithm based approach is developed to determine the observer gain and threshold for attaining a trade-off between the false alarm rate (FAR) and fault detection rate (FDR). Simulation of an unmanned surface vehicle (USV) system and comparison against existing work are performed to illustrate the effectiveness of the proposed scheme. © 2024

关键词： Consensus algorithm

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Adaptable Individualized Investment Recommendation System

Adaptable Individualized Investment Recommendation System

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： N. Sabiyath Fatima N. Noor Alleema C. Mahesh R. Umanesan K. Senthil P. Santhosh Kumar Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

Internetization has played a massive role in the rapid development and growth of the financial investment industry. Due to the rapid development, there is a demand for more financial knowledge services to help make well-informed investments. Accessing and conducting investments through online financial platforms has made the field highly accessible. This has made the investment recommendation system a popular research area. Personalized recommendations will help the user make investments that are safe for their financial situation and also suit their particular needs and interests. This work proposes a method where investment recommendations are given based on three key factors. The user interest sectors, risk appetite-based clustering, and market sentiment of the stock. These factors will be calculated by the use of Lasso regression, SVM, stepwise regression, generalized boosted regression, random forest, ridge regression and fuzzy clustering. based on these three factors, the proposed method results in investment recommendations made for users that are personalized to the specific user. This allows existing investors to gain new opportunities and new investors to start investing with lower amount of risk using the information available.

关键词： Support vector machines Uncertainty Social networking (online) Market research Stock markets Recommender systems Random forests Portfolios Pattern matching Investment

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Autonomous Number Plate Detection System for Vehicle Identification and Tracking

Autonomous Number Plate Detection System for Vehicle Identif...

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

作者： Neha Sri Nareddy Abhinaya Polneni Rekha baithi Pratyusha Kunchamwar Sunil Kumar Suvarna Durgabhavani battu Naveena budda Vikas Agarwal Premchander Munimanda AI IoT and Robotics Lab (AIR Lab) AGVs Group Autonomous Robotics Systems Limited Hyderabad India Department of Information Technology Malla Reddy Engineering College for Women Hyderabad India Ideabytes Software India Private Limited Hyderabad India School of Computer Science Georgia Tech Atlanta USA School of Computer Science and Engineering Malla Reddy Engineering College for Women Hyderabad India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

The autonomous number plate detection system plays a crucial role in modern transportation, law enforcement, and security infrastructure. technical approaches using artificial intelligence, machine learning, and computer vision are actively researched to achieve ease of maintenance through real-time vehicle detection. The recent development has drastically improved the accuracy of object detection and recognition systems in deep neural networks. Using these techniques, vehicle number plates can easily be detected autonomously for vehicle identification and tracking. Here, we have used a custom-trained TensorFlow-based number plate detection method to autonomously detect vehicles using images captured by a smartphone, or surveillance camera. It showed an impressive mAP @ 0.5 of 95%, and demonstrated limitations in long-distance detection, leading us to designate it as the optimal vehicle detection model for accuracy. We designed and developed an autonomous number plate detection system for vehicle identification and tracking, which includes web-based, android-based, and embedded-based applications. In addition, we evaluated the proposed autonomous number plate detection system with real-time images and videos. The proposed system achieved real-time vehicle detection and tracking, boasting a commendable 90% detection accuracy.

关键词： Visualization Accuracy Law enforcement System performance Transportation Artificial neural networks Real-time systems Security License plate recognition Videos

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Cryptocurrency PricePrediction using LSTM

Cryptocurrency PricePrediction using LSTM

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International Conference on Computing and Networking technology (ICCNT)

作者： Swaraj Deo Gaurav Kumar Swayam Soni Gurmeet Singh B.Tech III Year School of Computing Science and Engineering Galgotias University Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

This paper focuses on utilizing the Long Short-Term Memory (LSTM) algorithm to forecast the prices of cryptocurrencies. that is implemented through the model, which will allow us to keep tabs on the fluctuating prices of the various coins that are available. As various cryptocurrencies are available for trading and investment and they are regarded as a type of digital asset. because trading and investing should be done after the knowledge of the market and trends, So, to solve this problem there should be analysis before trading any pair. Our goal is to present accurate prediction based on the dataset of the selected cryptocurrency. Hence, the proposed model is built and it concentrate mostly on technical analysis of the crypto coins which is the major indicator and most suitable for the prediction models. The proposed LSTM model achieves a variance regression score of 0.95 on test data of the bitcoin which serves as a representative cryptocurrency that is used in the model.

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Weather and Climate Forecasting System for Cultivation using Naive's Algorithm 2

Weather and Climate Forecasting System for Cultivation using...

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2nd International Conference on Computing and Information technology, ICCIT 2022

作者： Sheela, M. Sahaya banu, S. Shahar Rajendran, T. Raj, S. Saran Sreeja, b.P. Pavai College of Technology Department of Electronics and Communication Engineering Pachal Namakkal India B S Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Vandalur Chennai India Tamilnadu Coimbatore India Vel Tech Rangarajan Dr. Sagunthala Rd Institute of Science and Technology Department of Computer Science and Engineering Chennai India Karpagam College of Engineering Department of Information Technology Coimbatore India

ISBN: (纸本)9781665436052

Agriculture based advisory services and climatic information are more considerable in providing assistance for pastoralists and small farmers to manage climatic changes and risks factors to be adopted during changes. Moreover, there is some gap in providing climatic information that significantly addresses the requirement of pastoralists and farmers. Huge farmers rely over indigenous knowledge where experiences and indicators are utilized to forecast and observe weather conditions. When this prediction is inbuilt and implemented by many farmers, documentations, coordinators and so on for predicting weather, it should be accurate and reliable manner. This work concentrates on building a weather predictor system for farmers to identify the velocity flow that is needed during farming. This model includes Naives equation for performing this velocity prediction. The outcomes demonstrate that pastoralists and farmers utilize combination of biological and meteorological indications to predict weather conditions. Henceforth, it seems to be more crucial in providing assistance to enhance access to climatic information in various regions of India in vulnerable and poor resource communities. © 2022 IEEE.

关键词： Cultivation

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Deep Learning based Driver Drowsiness Detection in Real Time Environment - An Enhanced Approach

Deep Learning Based Driver Drowsiness Detection in Real Time...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Ashin Ajay Naveen Kumar Ashok Gokul balajiram Abinesh Tamizhselvan Senthil Kumar Thangavel Somasundaram K M. Tech in Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India B. Tech in Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Department of Mathematics School of Physical Sciences Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Effective driver behavior monitoring is necessary due to the increase in traffic accidents caused by distracted driving. In order to identify distractions such as talking, drinking, napping, and using a phone, this research suggests a CNN-based methodology. The model uses facial characteristics such as eyes to detect and analyze these activities. Thousands of photos depicting various distracted driving postures are used to train it on a publicly accessible dataset. The strategy seeks to improve road safety by offering a reliable way to identify distractions.

关键词： Deep learning Sentiment analysis Transfer learning Road safety Real-time systems Reliability Convolutional neural networks Driver behavior Monitoring Vehicles

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Deep Learning for Glioblastoma Subtyping: Leveraging DenseNet-201 in brain Tumor Radiogenomic Classification

Deep Learning for Glioblastoma Subtyping: Leveraging DenseNe...

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Cognitive Robotics and Intelligent Systems (ICC - RObINS), International Conference on

作者： S. Alvin Jesuraj S. V. Evangelin Sonia C. P. Shirley B.Tech Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

This research investigates the application of DenseNet-201, a deep convolutional neural network architecture, in the RSNA-MICCAI brain Tumor Radiogenomic Classification aimed at predicting the genetic subtype of glioblastoma using MRI imaging data. This study demonstrates the effectiveness of DenseNet-201 in accurately classifying glioblastoma cases based on MGMT promoter methylation status, a critical biomarker influencing treatment outcomes. Through comprehensive experimental evaluations, including training, validation, and testing phases, DenseNet-201 exhibits robust performance metrics such as high accuracy, precision, recall, F1-score, and AUC-ROC values. These results highlight the model's ability to effectively distinguish between MGMT promoter methylation-positive and negative glioblastoma cases, offering valuable support for clinical decision-making in treatment planning and prognosis assessment. Leveraging deep learning techniques and MRI imaging data, DenseNet-201 holds promise as a powerful tool for enhancing the understanding of glioblastoma genetics and guiding personalized therapeutic interventions, ultimately contributing to improved patient outcomes in brain cancer management.

关键词： Deep learning Training Magnetic resonance imaging Medical services Genetics Planning Prognostics and health management

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EASYDIAGNOS: A FRAMEWORK FOR ACCURATE FEATURE SELECTION FOR AUTOMATIC DIAGNOSIS IN SMART HEALTHCARE

arXiv

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arXiv 2024年

作者： Maji, Prasenjit Mondal, Amit Kumar Mondal, Hemanta Kumar Mahanty, Saraju P. Computer Science and Design Dr. B. C. Roy Engineering College WB 713206 India Computer Science and Engineering Bengal College of Engg. and Tech. WB 713212 India Electronics and Communication Engg. National Institute of Technology WB Durgapur713209 India Dept. of Computer Science and Engineering University of North Texas United States

The rapid advancements in artificial intelligence (AI) have revolutionized smart healthcare, driving innovations in wearable technologies, continuous monitoring devices, and intelligent diagnostic systems. However, security, explainability, robustness, and performance optimization challenges remain critical barriers to widespread adoption in clinical environments. This research presents an innovative algorithmic method using the Adaptive Feature Evaluator (AFE) algorithm to improve feature selection in healthcare datasets and overcome problems. AFE integrating Genetic Algorithms (GA), Explainable Artificial Intelligence (XAI), and Permutation and Combination techniques (PCT), the algorithm optimizes Clinical Decision Support Systems (CDSS), thereby enhancing predictive accuracy and interpretability. The proposed method is validated across three diverse healthcare datasets using six distinct machine learning algorithms, demonstrating its robustness and superiority over conventional feature selection techniques. The results underscore the transformative potential of AFE in smart healthcare, enabling personalized and transparent patient care. Notably, the AFE algorithm, when combined with a Multi-layer Perceptron (MLP), achieved an accuracy of up to 98.5%, highlighting its capability to improve clinical decision-making processes in real-world healthcare applications. © 2024, CC bY.

关键词： Electronic health record

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