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检索条件"机构=Dept. of Computer Engineering and Application"

共 253 条记录，以下是91-100 订阅

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Revolutionizing Hops Plant Disease Classification: Harnessing the Power of Transfer Learning

Revolutionizing Hops Plant Disease Classification: Harnessin...

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2023 International Conference on Sustainable Communication Networks and application, ICSCNA 2023

作者： Degadwala, Sheshang Vyas, Dhairya Panesar, Sonia Ebenezer, Divya Pandya, Darshanaben D. Shah, Vandita Dipak Sigma University Dept. of Comm. Engg. Gujarat India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India Drs.Kiran and Pallavi Patel Global University Dept. of Comp. Sci & Engg. Kset Gujarat Vadodara India Pes University Department of Computer Science and Engineering EC Campus Karnataka Bengaluru India Scjccs Sankalchand Patel University Dept. of Comp. Sci. Gujarat Visnagar India Shreyarth University Department of Computer Application Gujarat Ahmedabad India

ISBN: (纸本)9798350313987

This research work introduces an innovative approach for the classification of hops plant diseases through the utilization of transfer learning with state-of-the-art Convolutional Neural Networks (CNNs). By leveraging pretrained models on extensive image datasets, this study has developed a customized CNN architecture, which is specifically designed to address the unique challenges posed by hop plant diseases. Their method demonstrates remarkable accuracy in classifying multiple hop plant diseases, offering significant potential for early disease detection and precise management strategies. The paper also provides an insightful comparison of the effectiveness of each CNN architecture within the context of hops plant disease classification. This research has the potential to automate and enhance hops plant disease management, ensuring the sustainability and quality of hop crops for the brewing industry while providing a valuable framework for addressing similar agricultural challenges. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Hops Plant Disease Multi-Class Classification Natural Farming Transfer Learning

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Utilizing Anomaly Detection Methods for Identifying Fraudulent Activities in Credit Card Transactions

Utilizing Anomaly Detection Methods for Identifying Fraudule...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： N. Siva Rama Krishna Prasad Amit Gangopadhyay R. Prabakaran Nilamadhab Mishra D. Shyam Prasad Sneha Kapoor Dept. of CSE Saveetha School of Engineering Chennai India Dept. of ECE Mohan Babu University Tirupati India Dept. of Computer Application Arignar Anna Institute of Science and Technology India Dept. of CSE VIT Bhopal University Bhopal India Dept. of Electronics & Instrumentation Engineering CVR College of Engineering Hyderabad India Dept of CSE Swami Vivekanand Subharti University Meerut India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Anomaly detection involves identifying unusual occurrences that could signal potential issues, such as security breaches, system failures, financial fraud, structural defects, or medical errors. In the context of digital finance, credit card fraud has become a significant concern, with fraudulent transactions often designed to appear legitimate. To address this, we employ the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset during sampling, and use the ARIMA model for time series forecasting to ensure accurate results. This research explores various fraud detection techniques and models, including K-Nearest Neighbors (KNN), Random Forest, Support Vector Classifier (SVC), and Decision Trees. We evaluate and compare the performance of these models to determine which offers the highest accuracy in detecting fraud.

关键词： Machine learning algorithms Time series analysis Static VAr compensators Nearest neighbor methods Credit cards Vectors Fraud Decision trees Security Random forests

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ResNet-50: The Deep Networks for Automated Breast Cancer Classification using MR Images

ResNet-50: The Deep Networks for Automated Breast Cancer Cla...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Tejaswini Das Debasish Swapnesh Kumar Nayak Anindita Kar Lambodar Jena Tripti Swarnkar Dept. of Computer Application FET-ITER Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Dept. of Computer Science and Engineering FET-ITER Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Dept. of Data Science (CSE) FET-ITER Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India

One in every four cancer cases in women is breast cancer, which is the most prevalent malignancy in this group globally. In 2020, breast cancer will account for one in every eight new instances of cancer, according to projections of 2.3 million new cases. Breast cancer identification, both early and accurate, is critical to improve patient outcomes and survival rates. Magnetic resonance imaging (MRI) has developed as a valuable method for diagnosing breast cancer. Deep learning algorithms, notably convolutional neural networks (CNNs), have demonstrated exceptional effectiveness in a variety of medical image processing applications, including breast cancer classification. The ResNet-50 architecture has received a lot of interest in this context because of its excellent performance in image recognition tasks. ResNet-50 is a deep residual network that introduces skip connections and residual learning to solve the difficulty of training very deep neural networks. A total number of 1480 cancer MRI samples are collected from the Kaggle database, having two classes, which include healthy and malignant scans. It is observed that our proposed model with the data pre-processing techniques has achieved a classification accuracy of 92.01%.

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Next Generation HR: Transforming Talent Management with AI and ML Innovations

Next Generation HR: Transforming Talent Management with AI a...

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Disruptive Technologies (ICDT), International Conference on

作者： Niharika Singh Pooja Singh Sudeshna Chakraborty Dept. of Master of Computer Application GL BAJAJ Institute of Technology & Management Greater Noida U.P. Department of CSE School of Computing Science & Engineering Galgotias University Greater Noida (U.P.) Department of Computer Application School of Computing Science and Engineering Galgotias University Greater Noida

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-dept. analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success.

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Analysis of Energy Quality Issues in the Solar Energy System

Analysis of Energy Quality Issues in the Solar Energy System

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International Conference on Electrical Power and Energy Systems (ICEPES)

作者： Ishita Uniyal Shweta Tiwari Vishal Shiv Ashish Dhondiyal Department of Electrical Engineering Graphic Era Hill University Dehradun Department of Computer Science & Engineering University Institute of Engineering Chandigarh University Mohali Punjab School of Computer Application Lovely Professional University Jalandhar Punjab Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Photovoltaic systems are being used more and more in the production of electricity because of the high cost of fossil fuel-based energy. The main issue that happens between grid and end-user transmission lines is power quality. DSTATCOM is a power quality compensating device that corrects voltage sag and swell are examples of power quality concerns. In high voltage line for power transmission this can be happened. The study was carried out with MATLAB or Simulink software to resolve the voltage sag and swell, and the model was created. The simulation results show the problem of voltage sag and swell is minimize to very large extent.

关键词： Power transmission lines Voltage fluctuations Software packages Simulation Power quality Energy resolution Power transmission

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Securing IoT-Edge Networks: Federated Deep Learning for Botnet Detection 7

Securing IoT-Edge Networks: Federated Deep Learning for Botn...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Nagasundaram, S. Sindhuja, R. Rajesh Kanna, B. Rajalakshmi, S. Shobana, G. Srivastava, Aviral Vels Institute of Science Technology and Advanced Studies Department of Computer Application Chennai Pallavaram India Saveetha Engineering College Department of Medical Electronics India Alagappa Chettiar College of Engineering and Technology Dept. of Electronics and Communication Engineering Karaikudi India RMK College of Engineering and Technology Department of Electronics and Communication Engineering India College of Science and Humanities Department of Computer Applications SRM Institute of Science and Technology Kattankulathur India Amity University Department of CSE Rajasthan Jaipur300202 India

ISBN: (纸本)9798350340600

Governments throughout the world are encouraging the use of 'smart city' technologies to improve urban residents' day-to-day experiences. In order to improve services like healthcare, electricity distribution, water purification, traffic management, etc., smart cities implement internet-connected technologies. The proliferation of connected gadgets has led to an increase in botnet assaults based on the IoT. The term IoT is used to describe a system of computers that are linked together that may be used for a wide variety of tasks, from environmental monitoring to on-demand power switching and beyond. Many Internets of Things gadgets are inherently disparate, update at irregular intervals, and hide in plain sight on a private or company network. The safety and confidentiality issues surrounding the Internet of Things that need to be addressed in both academic and practical settings. This research study proposes a federated-based solution to botnet attack detection utilizing on-device decentralized traffic data and a deep learning (DL) model. The proposed federated method addresses privacy concerns by preventing data from leaving the network's edge on the device. Instead, the edge layer is used to do the DL calculation, which has the extra benefit of being closer to the source of the data. Many tests are run on newly made public test data sets for deep learning models. Additionally, the sets of data are presented for examination and understanding. The recommended DL model achieved better results than the ML models. Finally, this research shows that the suggested model can detect anomalies with a precision of up to 98%. © 2023 IEEE.

关键词： Internet of things

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Breast Cancer Prediction Using Machine Learning

Breast Cancer Prediction Using Machine Learning

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Shiv Ashish Dhondiyal Pooja Luthra Bhupender Sharma Rajni Rajni Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Chandigarh University Mohali Punjab Department of Computer Science and Application Coer University Roorkee Uttarakhand Department Of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer.

关键词： Image segmentation Accuracy Machine learning algorithms Sensitivity Databases Nearest neighbor methods Prediction algorithms Breast cancer Classification algorithms Random forests

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An Effective Prediction of Rainfall Using Machine Learning Technique

An Effective Prediction of Rainfall Using Machine Learning T...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： Nisha Rana Guru Prasad M S Ashutosh Sharma Pratap M S M Anand Kumar Aditya Pai H Dept. of Computer Science and Engineering Doon Institute of Engineering And Technology (DIET) Rishikesh India Dept. Computer Science and Engineering Graphic Era Deemed University Dehradun India Dept. of Applied Science (Chemistry) Doon Institute of Engineering And Technology (DIET) Rishikesh India School of Computing and Information Technology Reva University Bengaluru India Dept. of Computer Application Graphic Era (Deemed to be University) Dehradun India Dept. of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India

Climatic information such as temperature, humidity, and precipitation are useful for agriculturists, businesses, researchers, and the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. In this study, factual approaches and machine learning methods are used to estimate and forecast meteorological parameters for the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. Approaches are used to forecast and estimate precipitation. Daily observations were used as part of the experiment. Validation of results using real-world data is used to check the accuracy of forecasting model experimentation. The experimental results show that distribution, correlation, line-plots, and neural networks perform well for forecasting meteorological characteristics and have the best classification accuracy when compared to other types of machine learning methods for precipitation forecasting.

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Mathematical Models of Heterogeneous Machine Learning Techniques for Ransomware Protection in Cyber-Physical Systems

Mathematical Models of Heterogeneous Machine Learning Techni...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Subir Gupta Sudipta Hazra Simanta Hazra Sourav Gayen Sayanti Mukherjee Anwesa Naskar Dept. of CSE (AIML) Haldia Institute Of Technology Haldia WB India Dept. of C.S.E. Asansol Engineering College Asansol WB India Dept. of C.S.E. NSHM Knowledge Campus Durgapur WB India Dept. of Computer Application Asansol Engineering College Asansol WB India Dept. of I.T. College of Engineering and Management Kolaghat Kolaghat WB India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Ransomware attacks are a growing threat to cyber-physical systems (CPS), capable of causing financial losses, downtime, and even physical harm. Using machine learning to detect and prevent these attacks is a promising approach. A study compared four machine learning techniques—support vector machines (SVM), decision trees, random forests, and neural networks—to detect ransomware attacks on CPS. The models were trained and evaluated using a dataset of real ransomware incidents. Results showed that all four techniques effectively detected ransomware attacks. Neural networks achieved the highest F1 score, while SVMs excelled in precision, recall, and accuracy. However, the differences in performance among the methods were not significant. The models detected ransomware outbreaks within an average of 12 hours, allowing businesses to mitigate the attack's effects promptly. The study concluded that machine learning could effectively detect and prevent ransomware attacks on CPS. However, it is important to note that the study used a limited dataset, highlighting the need for further research with larger datasets to confirm these findings.

关键词： Support vector machines Neural networks Machine learning Organizations Cyber-physical systems Data models Ransomware Decision trees Protection Random forests

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VGG-16 Technique to Reduce the Global Food Crises and Enhance the Crop Yields: Deep Learning Approaches

VGG-16 Technique to Reduce the Global Food Crises and Enhanc...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Sivakumar Depuru K Amala Pera Supriya A Basi Reddy Ravala.Sai Gireesh Dept of CSE School of Computing Mohan Babu University Tirupati India Dept.of. ECE Sri Venkateswara College of Engineering (Autonomous) Tirupati A.P India Department of Computer Application Mohan babu University Tirupati Andhra Pradesh India Dept. of. Computer Application Mohan babu University Tirupati A.P India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

In the digital era, enhancing agriculture plays a critical role in addressing food crises by improving crop yield and ensuring food security. Early detection and management of crop diseases are paramount for agricultural sustainability, significantly affecting food security and farmer livelihoods. Farmers often struggle to identify crop diseases at early stages, leading to reduced yields and potential food shortages. This research aims to assist farmers in early disease identification, thereby increasing crop yield and preventing food crises. This study introduces a machine learning approach using Convolutional Neural Networks (CNNs), specifically the VGG-16 model, to accurately identify crop diseases. VGG-16’s architecture is particularly effective in recognizing patterns in images, making it a superior choice for diagnosing plant diseases compared to existing models. By leveraging VGG-16, the research demonstrates improved accuracy in detecting crop diseases at early stages, providing a valuable tool for farmers to manage crop health proactively. The findings of this research highlight the potential of advanced machine learning techniques to enhance agricultural practices and contribute to global food security.

关键词： Support vector machines Technological innovation Accuracy Machine learning algorithms Food security Agriculture Convolutional neural networks

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