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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是71-80 订阅

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Forecasting the Rate of Crimes Committed Against Women Using machine learning Approach and Data Mining Techniques 7

Forecasting the Rate of Crimes Committed Against Women Using...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kumar, Sarthak Parhad, Om Pathak, Dhruv Irlewad, Shivani Parira, Payel Pawar, Rajendra Mit World Peace University Department of Computer Engineering and Technology Pune India

ISBN: (纸本)9798350304268

Prediction of the probability of crime in any region is a serious problem, especially in developing countries. Several factors can influence the crime rate at any location. there have been no effective solutions or dedicated research studies on the increasing crime cases, especially among women. thus, we have trained several machine learning algorithms and a neural network to come up with the best probability prediction model for different crime types. the data used was gathered from 50 districts in Madhya Pradesh, India. the trained models were evaluated based on their accuracy and f1-score in predicting the probability of different types of crimes. © 2023 IEEE.

关键词： Crime Prediction Demographic Features Neural Network Random Forest

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A Statistical machine learning Approach to Optimize Workload in Cloud Data Centre 7

A Statistical Machine Learning Approach to Optimize Workload...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Upadhyay, Pranjal Sharma, Krishna Kumar Dwivedi, Rishu Jha, Pradeep Rashtriya Raksha University Gujrat Gandhinagar India Learn and Build by TechiNest Pvt. Ltd. Rajasthan Jaipur India Department of CSE Global Institute of Technology Rajasthan Jaipur India

ISBN: (纸本)9781665464086

the modern data centre (DC) is a perplexing amalgamation of various mechanical, electrical, and control frameworks. the increasing number of possible working configurations and nonlinear interdependencies make it difficult to comprehend. To improve Data Center performance and execution, the neural network scheme can benefit from the genuinely active data. machine learning (ML) is emerging as the most suitable method of demonstrating DC execution and improving productivity by utilizing the existing sensor data. the data centre infrastructure, the tasks to be completed, and the desired profits are framed as a mathematical programming model, which can then be overcome by using alternative methods of searching for smart task programming. this paper proposes a novel method for maximizing the load on cloud infrastructure. this article has utilized the machine learning techniques to enhance the load balancing. the new proposed algorithm is evaluated and then compared against existing algorithms for analyzing the make span time, time taken to execute tasks, overall loadon the virtual machines and total cluster utilization. © 2023 IEEE.

关键词： machine learning

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Proceedings of the 7th international conference on Intelligent computing and Control Systems, ICICCS 2023

Proceedings of the 7th International Conference on Intellige...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

ISBN: (纸本)9798350397253

the proceedings contain 305 papers. the topics discussed include: a novel approach to predict the severity of peripheral neuropathy using machine learning;harnessing deep neural networks for accurate offline signature forgery detection;advanced skin category prediction system for cosmetic suggestion using deep convolution neural network;a lightweight neuromorphic CNN for human activity recognition on edge device;mood detection and emoji classification using tokenization and convolutional neural network;exploring explainability and transparency in deep neural networks: a comparative approach;comparative analysis of artificial intelligence techniques for sentiment analysis and its challenges;cloud and IoT based framework for air and noise pollution monitoring system via extreme learning machine;weighted mask recurrent-convolutional neural network based plant disease detection using leaf images;vehicle counting based on convolution neural network;cost optimization on cloud by adoption of rust programming;and implementation of cloud computing in higher education: a bibliographic analysis.

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An Improved System for Brain Pathology Classification using Hybrid Deep learning Algorithm 7

An Improved System for Brain Pathology Classification using ...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Sathya, V. Dhanabal, S. Trichy India

ISBN: (纸本)9781665464086

Speech, recognizing, learning, programming, and problem-solving are all meant to be included in artificial intelligence computing activities. Artificial intelligence (AI), a subfield of computer engineering, is concerned with developing sophisticated software or technologies that act and function like human. Deep learning, a category of algorithms used in deep learning, is based on learning data representations and is one of many machine learning techniques. Deep learning is used to develop brain tumour screening and classification models for rapid and uncomplicated tumour identification using Magnetic Resonance Images (MRI) imaging. this paper shows how the pretrained model Alexnet with Transfer learning (TL) may be trained on tiny and innovative fresh data for classification problems using the stochastic-gradient descent with momentum-SGDM optimizer. Numerous hybrid models were presented with the aim of classifying a brain tumour MR image as a benign as well as malignant tumour. the accuracy, error rate, and confusion matrix parameter of the suggested models are evaluated in order to show how the works have improve. © 2023 IEEE.

关键词： Stochastic systems

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Identification of Melanoma Lesions using Deep learning Networks and Cloud computing 7

Identification of Melanoma Lesions using Deep Learning Netwo...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Shiney, Dasari Elsie Gnana Sri, Palanki Nithya Kornepati, Purna Anjaneya Kumar Divya, Talasilla Sri Manoj, Motepalli Sasi Sai Seshadri Rao Gudlavalleru Engineering College Department of Information Technology Andhra Pradesh India

ISBN: (纸本)9798350397253

An integration of computer vision algorithms, machine learning, and deep learning to extract information from digital photos is particularly important in dermatology for the early identification and treatment of diseases like melanoma. Two very important issues in the field of melanoma detection research are discussed in this article. the first argument highlights the importance of performing regular training and testing cycles, as well as the risks associated with transfer learning when even little changes to dataset parameters might affect classifier performance. To address the second point's call for a system architecture that can adapt to shifting training datasets, proposed a Cloud-Fog-Edge computing hybrid architecture for a Melanoma Detection service. the distributed technique considerably shortens the running time of continuous retrain, as shown by studies done on both a single computer and a variety of distribution systems. © 2023 IEEE.

关键词： Deep learning

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Approach to Predict Early Stage of Breast Cancer using machine learning 7

Approach to Predict Early Stage of Breast Cancer using Machi...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Sekar, R.Raja Kumar, Midigesi Rakesh Vardhan, Mandala Vishnu Gopal, Kuruba Venu Jayakkam Allan Tilak, C. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Srivilliputhur India

ISBN: (纸本)9798350397253

In the world, breast cancer is regarded as one of the main factors that cause death for females between the ages of 20 and 59. Early detection and treatment can enable patients to receive appropriate care, hence lowering the rate of breast cancer morbidity. In line with research, most experienced doctors can correctly identify cancer with 79% accuracy, whereas deep learning algorithms can do so with 91% accuracy. this research study analyzes the most recent deep learning-based breast cancer models detection and classification and present them through a comparative study. Additionally, to make it easier for any future experiments and comparisons, the datasets that are used and available to usage are listed in this research work. Models based on decision trees, random forests, logistic regression, and support vector machines are the most up-to-date, most accurate models used in machine learning techniques © 2023 IEEE.

关键词： Diseases

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Web Application-based Diabetes Prediction using machine learning 7

Web Application-based Diabetes Prediction using Machine Lear...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Gurunathan, P.T. Siva Raghuraj, S. Roahit, S. Nithishkumar, S. Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering Karur India

ISBN: (纸本)9781665464086

Diabetes is a crucial illness, and a lot of people are struggling with it. People with diabetic issues are at high risk of illnesses like cardiovascular disease, kidney illness, stroke, eye issues, tissue damage, and so on. the present exercise in a medical facility is to gather needed information for diabetic issues through medical diagnosis with different examinations, and suitable therapy is offered based upon the medical diagnosis, which takes more effort and resources. However, by using machine learning, there is a solution to this serious problem. In the proposed work, the machine learning algorithms K-Nearest Neighbor (KNN) and Random Forest (RF) were used to predict the risk of developing diabetes-related diseases. After pre-processing the data, features are selected based on their influence on the prediction of the diseases. the prediction accuracy using Random Forest (RF) on the preprocessed data after feature selection and applying the ensemble classifier is 83%, which is significantly higher than the K-Nearest Neighbor (KNN) with an accuracy of 75%. then, these machine learning models were used to create a web application for diabetes prediction. the objective is to use machine learning techniques to predict the risk of developing diabetes-related diseases and create a web application to support this prediction. © 2023 IEEE.

关键词： Forecasting

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Energy Consumption in Smart Buildings using machine learning 7

Energy Consumption in Smart Buildings using Machine Learning

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Kumar, Katti Bharath Abhay Gopireddy, Gangireddy Ranjana, P. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

Energy is the indispensable factor of modern societies. From last few decades, there is rapid increase in world's energy consumption due to raise of population and their comfort demands. Buildings are accountable for 40% of global energy hence Building Energy Management has become crucial topic nowadays for reducing energy wastage the ability to forecast the amount of energy used in SB and plan for that use is essential for the implementation of energy-efficient management systems. the proposed method for estimating the amount of energy that is used in buildings, as well as offering suggestions for cutting down on energy use and lowering expenses. the systems use the information like temperature humidity air quality etc. this information is analyzed by machine learning forecast the amount of energy that is used by smart buildings. © 2023 IEEE.

关键词： Internet of things

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A Hybrid Gradient Boost Model for Intrusion Detection 7

A Hybrid Gradient Boost Model for Intrusion Detection

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Vaishali, R. Dept. of. Physical Science University of Vavuniya Vavuniya Sri Lanka

ISBN: (纸本)9781665464086

Due to the advancement of network threats at present, it is crucial to conduct research on identifying and preventing network anomalies. machine learning (ML) is one strategy for Intrusion Detection System (IDS). Finding a reliable system to act as a networking shield is still difficult despite the fact that various IDS are suggested utilizing ML. this paper suggests a hybrid model combined with the gradient boost methods XGBoost and Lightgbm to forecast the various attacks that are urging in the network. To obtain the higher precision, hyperparameters of the algorithms are tuned. the proposed system is trained using the UNSW-NB15 dataset, which contains attacks for Generic, Exploits, Denial of Service (DoS), Shellcode, Fuzzer, and Reconnaissance. the system has an average accuracy of 99.89%. Because of the recent dataset training, the proposed system is relevant to modern Intrusion Detection Systems used in current network systems. © 2023 IEEE.

关键词： machine learning

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Comparative Analysis of Deep learning Pre- Trained Models and machine learning Classifiers for Accurate Cervical Cancer Diagnosis using Colposcopy Images 7

Comparative Analysis of Deep Learning Pre- Trained Models an...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kalbhor, Madhura Malhari Shinde, Swati Vijay Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Cervical cancer is a significant public health issue worldwide, and early detection is essential for successful treatment. Colposcopy is a widely used diagnostic tool for cervical cancer, but its accuracy depends on the examiner's experience. Computer-aided diagnosis (CAD) systems can potentially improve diagnostic accuracy and reduce variability among examiners. the proposed system includes feature extraction using different deep-learning pre-trained models and classification into cancerous and non-cancerous images using different machine-learning classifiers. the efficacy of the suggested system is assessed utilizing a conventional dataset comprising colposcopy images. the results of study demonstrate a potential of CAD systems for improving accuracy of cervical cancer diagnosis and reducing inter-observer variability using deep learning. the paper presents that the Googlenet and ResNet-50 pre-trained models features from transformation zone extracted colposcopy images have the highest classification accuracy of 76.64%. © 2023 IEEE.

关键词： Cervical Cancer colposcopy images deep learning Pre-trained models

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