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检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

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Wafer Surface Semiconductor Defect Classification Using Convolution Neural Network Based Improved Faster R-CNN 2

Wafer Surface Semiconductor Defect Classification Using Conv...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Babu, Praveenkumar Habelalmateen, Mohammed I. Srikanteswara, Ramya Reddy, R. Archana Purushotham, N. Srm Institute Of Science And Technology Department Of Electronics And Communication Engineering Chennai Ramapuram India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Sr University School Of Computer Science And Engineering Telangana Warangal India Mohan Babu University School Of Computing Dept. Of Cse Tirupati India

ISBN: (纸本)9798350381665

Defect classification helps ensure the quality and reliability of semiconductor devices. Even a minor defect can lead to significant performance issues or failures in electronic components. Moreover, a recurring issue in this field is the identification of minute fault patterns, frequently measuring just a few μ m in size, and pixels among enormous volumes of high-resolution imaging. In this paper, proposed the improved Faster Region-Based Convolutional Neural Network (R-CNN) by utilizing the Res2Net model can assign the backbone of the proposed model. The model can train and validating on WM811 K dataset. The dataset contains imbalance classes in the presented categories. Initially the data-augmentation technique is employed to create the synthetic data, and it solve the classes imbalance issue. Subsequently Build the Convolutional Neural Network (CNN) foe classify the categories of the defects by using the extracted features. The Res2Net is effectively extract the various features and the feature pyramid network is used to fuse the extracted information. The proposed method is evaluated and compared with various existing methods and metrices such as Accuracy of 97.86 %, precision of 96.50%, Recall of 96.80% and F1-score of 97.85% and the methods are namely Deep Convolutional Neural Network for Wafer Defect Identification (DCNN-WDI), ShuffleNet-v2-CNN and Transfer Learning Using Faster R-CNN. © 2024 IEEE.

关键词： Convolutional neural networks

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AI-Driven Health: A Web App for Enhanced Healthcare Queries and Nutrition Analysis

AI-Driven Health: A Web App for Enhanced Healthcare Queries ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： P Ushashree Archana Naik P Amrutha Sai Sri Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

The AI-Driven Health chat assistant is an innovative healthcare solution that seamlessly integrates technology and care, enabling users to have natural language conversations about symptoms, treatments, and general health inquiries. The chatbot functions as a virtual assistant, enabling users to inquire about medical concerns and obtain clear explanations. The nutrition bot utilizes the advanced capabilities of Google Gemini to analyze food images, offering users comprehensive nutritional information. The proposed system undergoes constant refinement through regular testing and user feedback, guaranteeing a smooth and dependable platform for accessible healthcare interactions. The AI-Driven Health chat assistant strives to enhance the accessibility and comprehensibility of healthcare information, ultimately enhancing a healthier and more informed society.

关键词： Virtual assistants Natural languages Medical services Oral communication Chatbots Internet Testing Biomedical imaging

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Machine Learning-Driven QoS Prediction in 6G Networks Under α-η-μ Fading with Selection Combining and Co-Channel Interference Mitigation 1

Machine Learning-Driven QoS Prediction in 6G Networks Under ...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Upadhyay, Deepak Rallapalli, Sreekanth Hussain, Zakir Sharma, Rashmi Venu, Nookala Ranswal, Aakash Singh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Nitte Meenakshi Institute of Technology Department of MCA Bengaluru India Department of CSE Nagpur Campus Pune India Centre for Internet Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331518578

In this paper, we study machine learning for resource utilization and QoS performance improvement in 6G networks under α-η-μ fading and co-channel interference (CCI). We conduct extensive experiments in simulation comparing our proposal with previous work and showing substantial improvements for both throughput, latency and resource efficiency over traditional as well as deep reinforcement learning based approaches. Their adaptive nature enables real-time optimization, and they run well in both dynamic and complex network conditions. Our experimental results illustrate that machine learned models significantly outperform state-of-the-art baselines on the following dimensions prediction accuracy, resource management and QoS metrics. This work underscores the importance of machine learning as a core enabler for sustainable 6G networks and providing better performance in future wireless communication systems. © 2025 IEEE.

关键词： Cochannel interference

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Automated Early Diagnosis of Lung Tumor Based on Deep Learning Algorithms 1

Automated Early Diagnosis of Lung Tumor Based on Deep Learni...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Maheswari, M. Jothi, B. Devi, P. Gayathri Chitradevi, B. Pradeep, Kumar S SRM Institute of Science and Technology Department of Computational Intelligence Chennai India Panimalar Engineering College Department of ECE Chennai India Department of Computer Science Engineering Perambalur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350323795

The main goals of this study are to identify the early stages of lung cancer and investigate the degree of accuracy of various machine learning algorithms. A thorough review of the literature revealed that certain classifiers have low accuracy while others have higher accuracy but are challenging to approach nearer to 100%. Low accuracy and high implementation costs as a result of inappropriate DICOM image handling Many various types of images are utilised in medical image processing, however CT scans are typically chosen since they have less noise. Deep learning has been shown to be the most effective technique for processing medical images, identifying and classifying lung nodules, extracting features, and predicting the stage of lung cancer. This system's initial stage employed image processing methods to separate lung sections. Using K Means, the segmentation is carried out. Segmented images are used to extract the features, which are then classified using a variety of machine learning algorithms. The accuracy, sensitivity, and specificity of the proposed techniques, as well as their classification time, are used to assess their performance. © 2023 IEEE.

关键词： computerized tomography

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Real-Time Hand Gesture Detection Using Convex Hull and Contour Edge Detection 6th

Real-Time Hand Gesture Detection Using Convex Hull and Conto...

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Amatya, Smriti Ishika Kumar, M. V. Manoj Prashanth, B.S. Sneha, H.R. Thomas, Likewin Yarlagadda, Vishnu Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering PES Institute of Technology and Management Shivamogga India Vellore India

ISBN: (纸本)9789811613418

Hand gesture is a way of communicating through sign languages and presently it has been a hot research topic in the field of Artificial intelligence and Data science. Contemporary methods for detecting the hand gesture uses optical/mechanical sensors, actuator & accelerometer attached with the glove. This paper proposes a novel method for gesture identification for various hand gestures without using additional equipment (such as sensors or gloves). The proposed method captures the image of the hand gesture then segments it. The real-time hand gesture is tracked down by the convex hull. Convolutional Neural Network is used to detect the area of hand gestures in real-time. The results of the experimental evaluation of the proposed method gave 80% accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Palmprint recognition

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MindCare: Machine Learning based Personalized Mental Health Monitor

MindCare: Machine Learning based Personalized Mental Health ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ushashree P Archana Naik Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Mental health challenges are growing in recent years, emphasizing the need for effective monitoring and inter- vention systems. This paper presents a comprehensive approach to detect mental unstabilities by analyzing the text entered by the user. Utilizing a combination of sentiment analysis and machine learning techniques, the proposed system preprocesses text data to extract sentiment scores and other key features, which are then used to train models for accurate mental health assessments. The system also offers personalized recommendations based on the user's mental state. Experimental results demonstrate the effectiveness of the approach, with the model achieving accuracy of 82.31% for negative text classification. These results underscore the potential of the system to serve as a valuable tool in the early detection and management of mental health issues.

关键词： Sentiment analysis Accuracy Navigation Text categorization Personal voice assistants Mental health Machine learning Feature extraction Internet Monitoring

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Improvisation of QoS in SDN-Frame Work for UAV Networks Using Dijkstra Shortest Path Routing Algorithm 1

Improvisation of QoS in SDN-Frame Work for UAV Networks Usin...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Mathapati, Mahantesh Nandihal, Priya Mishra, Priti Kotagi, Veeranna Dept.of Cse Bengaluru India Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India Information Science and Engineering Atria Institute of Technology Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India

ISBN: (纸本)9798350316469

This work presents a unique SDN-based Dijkstra shortest path routing algorithm for QoS improvisation in UAV Networks. It discusses the various problems pertaining to the networking of unmanned aerial vehicles in disaster rescue operations and unique networking algorithmic solutions to the problems. Involving the SDN technology in networks provides faster service delivery than the traditional technology, as it isolates the control plane and data plane. This SDN technology has the centralised governing management service on the distributed network that affords improved security. But, SDN with Wireless Sensor Network (WSN) does not provide enhanced Quality of Services (QoS) in sophisticated cloud computing (CC) applications. Across the research community, end-users seek to attain a QoS while accessing the cloud-based services depending on their knowledge in using today's technology. Therefore, this research study is aimed to prioritize the cloud application data without any delay from the normal data and also to provide the fastest communication using the SDN framework applying shortest path routing. The traditional networking infrastructure's speed, scalability, and agility are increased by the key functionality of SDN, which is used to intellectual the network information from the applications. Moreover, massive size data are managed and customized by using OpenContrail, which is a more responsive and scalable network. The QoS provided by the SDN with cloud-based services using shortest path, stimulates this research to apply the integrated technology in UAV. UAV can transfer the data through cloud service using SDN, in order to maintain a communication with remote experts. The proposed Shortest Path (SP) direction-finding algorithm provides appropriate deployment & connectivity to the modules of group of UAVs, also decreases time delay within the network. The simulation results reveal that the proposed technique is suitable and more effective to set up an emergency

关键词： Cloud Computing OpenStack Quality of Service Shortest Path Algorithm Software-Defined Networking UAV

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Assessment of Enterobacteriaceae Bacteria and Antibiotic Resistance in Spring Water of Sikkim Using OPTICS Clustering Algorithm

Assessment of Enterobacteriaceae Bacteria and Antibiotic Res...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Shobha Prabha Niranjan Dept. of Computer Science and Engineering Nitte Meenakshi Institute of Technology Benagaluru India Department of Electronics and Communication Engineering NMAM Institute of Technology Nitte (Deemed to be University) Nitte Karnataka India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Waterborne diseases pose meaningful public health risks, especially in regions reliant on natural spring water like Sikkim, India. This study aims to evaluate water quality in four districts of Sikkim, focusing on Enterobacteriaceae bacteria, indicators of fecal contamination and potential pathogens. Using selective culture techniques, Enterobacteriaceae were isolated and taxonomically characterized. Multiple species were identified, indicating a risk of waterborne diseases. Antibiotic exposure testing proven that Escherichia coli isolates were resistant to commonly used antibiotics and carried both virulence and resistance genes. The proposed study introduces OPTICS (Ordering Points to Identify the Clustering Structure) for bacterial strain clustering analysis The results highlight the critical need for robust water quality management strategies in Sikkim to reduce health risks linked to antibiotic-resistant bacteria and fecal contamination. This proposed study specifies critical insights into community health risks and offers a novel approach for clustering bacterial strains in water quality assessment. The escalating issue of antibiotic resistance in bacterial infections, a critical public health concern, is discussed in the proposed work. The study's primary objective is to analyze the correlation between antibiotic usage patterns and the development of resistance across various bacterial strains. By employing a combination of data analysis, laboratory experiments, and statistical modeling, the research seeks to identify key factors that drive the emergence of antibiotic-resistant bacteria. The findings reveal a strong association between the misuse and overuse of antibiotics and the rapid evolution of resistance in several common bacterial pathogens. The study highlights the role of specific antibiotics that contribute disproportionately to resistance, emphasizing the need for stricter regulation and more judicious use of these drugs. Moreover, the research ide

关键词： Antibiotics Water quality Optics Water pollution Public healthcare Springs Contamination Water resources Immune system Strain

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Intrusion Detection in Industrial Internet of Things Based on Recurrent Rule-Based Feature Selection 2

Intrusion Detection in Industrial Internet of Things Based o...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Devarajan, Mohanarangan Veerappermal Aluvala, Srinivas Armoogum, Vinaye Sureshkumar, S. Manohara, H.T. Sacramento United States Sr University School of Computer Science and Engineering Warangal India University of Technology School of Innovative Technologies and Engineering Mauritius Kalasalingam Academy of Research and Education Department of Cse Krishnankoil India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350372892

The Industrial Internet of Things (IIoT) is experiencing rapid growth, and robust cyber security measures to protect against from the cyber-attacks with the help of Anomaly Detection System (ADS) and signature-based detection system. The sensors collect vast amount data from the environment, presenting functionality challenges for device. To overcome this problem, many Network Intrusion Detection Systems (NIDS) had developed for secure to IIoT systems. But NIDS faces challenges due to complexity of information collection required for threat detection. So, propose study work introduces a Recurrent rule-based Feature Selection (RFS) for IIoT system. NSL-KDD and UNSW-NB15 dataset are used for performing operation and relevant features are selected by using hybrid rule-based algorithm. Then RFS model classifies the data and predict the attacks. the proposed method performance is superior than existing method and better results in terms of accuracy rates of 99.0% and 98.9%, detection rates of 99.0% and 99.9%, and low false positive rates of 1.0 % and 1.1 % respectively. © 2024 IEEE.

关键词： Network intrusion

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Heart Disease Prediction Using Artificial Intelligence Ensemble Network 2

Heart Disease Prediction Using Artificial Intelligence Ensem...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Nirmala, S. Veena, K. Indu, B. Kalshetty, Jagadevi N. Amc Engineering College Dept. of Cse Bengaluru India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Heart disease has climbed its way to the top of the list of the primary causes of death all over the world. In the past, individuals also referred to heart disease as cardiovascular disease when talking about it. Heart disease and stroke are the primary causes of death in India, together accounting for one death in every four that occur there. The use of machine learning to the process of forming judgements and creating predictions based on the large quantities of data created by the healthcare business is highly valuable. This is because machine learning can analyse patterns in the data to make more accurate predictions. According to the information that was provided by the WHO, cardiovascular disease is the primary cause of around 24 percent of deaths in India that are attributed to non-communicable illnesses. These deaths are mostly caused by coronary artery disease (CVD). In addition, coronary heart disease is the main cause of death in industrialised nations like the United States of America and other rich countries. Around 17 million people each year lose their lives to cardiovascular disease, making it the leading cause of death on a global scale;the incidence of cardiovascular disease mortality is greatest in Asia. The Cleveland heart disease dataset's primary objective was to provide information that could be used to conduct an analysis of the system. The implementation of the prediction model takes use of a broad range of feature integrations, in addition to numerous techniques of categorization that are already widely known to the general public.. © 2022 IEEE.

关键词： Diseases

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