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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Gowriswari, s. Fathima, s shafla Joseph Rajan, D. Antony Mohankumar, N. sasirekha, V. Rajmohan, M. Sri Sairam Engineering College Department of Instrumentation and Communication Engineering Tamil Nadu Chennai India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350357

This paper proposes an approach for user-centric drink customization by combining Raspberry Pi technology with cloud-powered collaborative filtering. by using collaborative filtering algorithms housed in cloud architecture, the proposed solution seeks to improve the beverage customization experience. A specialized Raspberry Pi interface allows users to input their preferences and comments, which are automatically included in the collaborative filtering mechanism. With the help of this cloud-b.s.d system, user data can be processed and analyzed in real time, enabling personalized drink suggestions. Adding Raspberry Pi devices to a system makes it more responsive and makes data processing more localized, which is great for users. The combination of Raspberry Pi with cloud-powered collaborative filtering shows that it can provide a highly customized and adaptable drink experience. This study adds to the growing body of knowledge in cloud-b.s.d collaborative filtering and Internet of Things (IoT) applications, and it provides a potential way to improve customer happiness in the beverage b.s.ness. © 2024 IEEE.

关键词： Collaborative filtering

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Methodology for Classifying Objects in High-Resolution Optical Images Using Deep Learning Techniques 7th

Methodology for Classifying Objects in High-Resolution Optic...

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7th International Conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2022

作者： Lalitha Kumari, P. Das, santanu Kannadasan, b. sampathila, Niranjana saravanakumar, C. Anand, Rohit Gupta, Ankur Department of Computer Science and Engineering Malla Reddy Institute of Technology Andhra Pradesh Secunderabad Maisammaguda India Department of Biotechnology Seshadripuram First Grade College Yelahanka New Town Bangalore India Department of Civil Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai India Department of Biomedical Engineering Manipal Institute of Technology Karnataka Manipal India Department of Electronics and Communication Engineering SRM Valliammai Engineering College Tamilnadu Kattankulathur India DSEU G.B.Pant Okhla-1 Campus New Delhi India Department of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India

ISBN: (纸本)9789811988646

The classification of objects that are present in the images or in the videos is being developed progressively to obtain good results because of the use of convolutional networks. In this work, we have used the convolutional networks for the detection of objects that are present in high-resolution satellite images. Tests were carried out on ships that are on the high seas and in the ports. This classification is useful for monitoring the coasts, as well as for analyzing the dynamics of the ships which can be applied in the search of ships. To cover this task of classifying ships in the spectral images, the use of high-resolution satellite images of coastal areas and with a large number of ships is used in order to build a set of images, containing images of the ships. In order to be used for training setting and testing of the convolutional network, a very particular configuration of the convolutional network caused by the particularity of high-resolution satellite images is presented. The methodology developed indicating the procedures performed is also presented in which a set of images containing 300 was built images of ships that are in the sea or are anchored in the ports. The results obtained in the classification using the convolutional networks are acceptable to be able to be used in different applications. © 2023, The Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： ships

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Triboost and beyond: Advanced Machine Learning Approaches for Diabetes Risk Prediction

TriBoost and Beyond: Advanced Machine Learning Approaches fo...

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Electronics and Renewable systems (ICEARs), International Conference on

作者： soham biren Katlariwala Vaibhav C. Gandhi Nirav Patel Devendra Parmar Ayushi Desai B.S. Data Science and Applications Indian Institute of Technology Madras Chennai India Department of Computer Engineering MBIT The Charutar Vidya Mandal (CVM) University Anand India Parul Institute of Technology Parul University Vadodara India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Diabetes is a tremendous world health issue and, in its management, and complications prevention, proper screening is vital. This paper examines the improvement of accurate diabetes risk prediction by some advanced machine learning methods. The study compares various models such as; Random Forest, Adaboost, support Vector Machine, Catboost, Decision Tree, Ls-sVM, Logistic Regression, Naïve bayes, k-Nearest Neighbors, LightGbM, XGboost, and neural networks. Most emphasis is put on Triboost that is a novel ensemble model which integrates XGboost, Catboost, and LightGbM. Lastly, the effectiveness of a model is determined using measures such as ROC AUC and accuracy. Evaluation of the results has shown that the proposed Triboost had the highest accuracy at 94% and ROC AUC of 91% thereby outcompeting the use of individual classifiers as well as other ensemble methods. The results of the study confirm adequacy and possibilities of ensemble learning to enhance predictive precision and stability and their applicability for early detection of diabetes in clinics. Further work shall seek to increase the variability of the dataset, integrate fresh clinical data, and increase computational effectiveness for practical use.

关键词： support vector machines Renewable energy sources Accuracy sensitivity Predictive models stability analysis Diabetes Ensemble learning Decision trees Random forests

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Retraction Note: Optoelectronic-aided machine learning-b.s.d stable routing protocol for MANET and beyond massive MIMO systems in 5G networks

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Gnanasekaran, P. Varunkumar, K. A. Rajendran, N. Priyadarshini, R. Macherla, sivudu Department of Information Technology B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur District India

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An efficient scene content-b.s.d indexing and retrieval on video lectures 6th

An efficient scene content-based indexing and retrieval on v...

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6th International Conference on Information system Design and Intelligent Applications, INDIA 2019

作者： Ashok Kumar, P.M. Ambati, Rami Reddy Arun Raj, L. Department of Computer Science Engineering K.L. University Vaddeswaram522502 India Department of Computer Science Engineering RVR & JC College of Engineering Guntur522019 India Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai600048 India

ISBN: (纸本)9789811553998

Recently, the popularity for massive online open course (MOOC) learning among student community is increasing day by day. In most of the MOOC learning Web.s.tes, indexing and retrieval techniques are b.s.d on the keywords like names, titles and Web addresses of the videos. As a result, the response for content-b.s.d queries is not good in most of the scenarios. In this paper, we propose an efficient scene content-b.s.d indexing and retrieval (EsCIR) framework for lecture videos. The proposed EsCIR framework consists of two phases: offline phase and online phase. In offline phase, we apply novel block-level key frame extraction (bLKFE) technique to segment video frames into shots and choose the right frame. The optical character recognition (OCR) tool is applied to the generated key frames in the videos for extracting the text. The jaccard similarity coefficient measure is used to eliminate the duplicate text frames, and then use of stop word removal and stemming algorithms are applied to get meaningful keywords from the scene. We used single-pass in-memory indexing (sPIMI) technique for building the indexing with the help of extracted keywords. In online phase, search and matching algorithms will map input queries given by user to the corresponding videos. We applied the okapi b.s.Match25 (bM25) ranking function to rank those matched videos for b.s. relevance results. The proposed EsCIR framework has been validated on standard video lectures and found to give better results than the existing state of art algorithms in terms of precision and accuracy. We compared our results with existing methods of lecture video techniques reflects improvising rob.s. performance on lecture videos. © springer Nature singapore Pte Ltd 2021.

关键词： Extraction

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An Efficient Feature selection in Diabetes Prediction Using Clustering Greedy stepwise search Algorithm 15

An Efficient Feature Selection in Diabetes Prediction Using ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rajeswarappa, G. Vignesh, R. Ratha, Ashis Kumar Devi, s. Nachiya, R.A.M. Jainaf selvakumar, M. G Pulla Reddy Engineering College Department of Cse Kurnool518007 India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Karunya Nagar Coimbatore India Iilm University School of Computer Science and Engineering Department of Computing and Security Uttar Pradesh Greater Noida India Nandha Engineering College Department of Mca Tamilnadu Erode India B.S. Abdur Rahman Crescent Institute of Science and Technology Crescent School of Pharmacy Chennai Vandalur600048 India Bannari Amman Institute of Technology Department of Information Technology India

ISBN: (纸本)9798350370249

Diabetes is a metabolic disease that is common across the world and has a high yearly risk of complications that can be life-threatening. These problems have the potential to kill several important organs if left unchecked. Preventing severe consequences can be achieved by guaranteeing prompt treatment, contingent upon early identification. Refining datasets for efficient data mining operations necessitates data pretreatment, which frequently entails eliminating extra functions that may jeopardize predicted precision. In statistical machine learning, feature selection is a well-established topic that uses methods like K-means clustering and the greedy stepwise search algorithm. Even with limited capabilities, empirical testing of the sVM classifier on the Pima Indian Diabetes (PID) dataset repeatedly shows that it is more accurate than other approaches. The PID dataset is a popular benchmark for evaluating data mining techniques, demonstrating the usefulness of this dataset for research and development projects. © 2024 IEEE.

关键词： Diabetes Feature selection Glucose Greedy stepwise sVM Classifier

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Analysis of Network Technologies and Cyber security Assessment for Enhancing Machine Learning, Grid Computing and Cyber-Physical Connectivity Internetwork Effectiveness 2

Analysis of Network Technologies and Cyber security Assessme...

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2nd International Conference on Innovative Practices in Technology and Management, ICIPTM 2022

作者： Dhanalakshmi, b. singh, Chaitanya surya, s. Hedage, Atharav Kuchhal, shikha Tongkachok, Korakod B.S. Abdur Rahman Crescent Institute Of Science And Technology Computer Science And Engineering Tamilnadu Vandalur India Swarrnim Startup Innovation University Computer Engineering Department Gujarat Gandhinagar India School Of Computing Kalasalingam University Computer Science And Engineering Tamilnadu Sirivelliputhur India Vishwakarma Institute Of Information Technology Electronics And Telecommunications Pune Belgaum Karnataka India Spitm Dcrust Department Of Ece Murthal India Thaksin University Faculty Of Law Thailand

ISBN: (纸本)9781665466431

The fast improvement of the Internet has incredibly worked with individuals spreading information worldwide, prompting the Internet turning into a pivotal apparatus in many fields like science, trade, schooling, etc. With the Internet common in such countless parts of day to day existence, network security has become b.s.c and requests consistent consideration. For this situation, an IDs's motivation is to recognize between real organization associations and possibly hurtful ones. The recommended method in this examination centers around an interruption identification and avoidance system that comprises of Cloudlet Controller (CC), Trust Authority (TA), and Virtual Machine Manager to recognize questionable practices in a cloud climate. Cloud clients are migrated to different regions in the climate, as indicated by our proposed approach. Whenever information bundles from different clients are gathered, network traffic happens. To resolve this issue, we assembled the Cloudlet Controller, which gets bundles from different clients through switch. CC gets parcels until they arrive at their edge level;if the cloudlet is over-burden, the bundles are shipped off inactive cloudlets. In this philosophy, network traffic is decreased, and it is significantly helpful in identifying interruptions in a straightforward way. In this review, a cross breed arrangement b.s.d interruption location and counteraction framework on the cloud is worked to distinguish gatecrasher exercises in the framework. A cross breed characterization b.s.d interruption location framework is created with a few cloudlets, a cloudlet regulator, a confided in power, and a virtual machine supervisor to distinguish interloper assaults in the framework, as well as a deterrent instrument to safeguard bundles from gatecrashers. © 2022 IEEE.

关键词： Machine learning

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IoT b.s.d smart Cradle for Neonatal Monitoring 6

IoT based Smart Cradle for Neonatal Monitoring

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： sonia, R. Jayadeva, sujay Mugaloremutt Ragavendiran, s.D. Prabu Revathi, N. Arumugam, Jeevanantham Kumar, s sandeep B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Tamil Nadu Vandalur600048 India Jss Academy of Higher Education and Research Department of Health System Management Studies Karnataka Mysuru570004 India Rvs Technical Campus Department of Computer Science and Engineering Tamil Nadu Coimbatore641402 India Panimalar Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai600123 India Kongu Engineering College Department of Information Technology Tamil Nadu Perundurai638060 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9781665482714

India has the highest percentage of working women and mothers. It also has a record for the highest number of babies being born every single day. These two statements highly contradict each other. When a woman gives birth to a baby, she needs to spend almost a year or two taking care of the necessities of the baby. but when a need to leave the baby for a certain time arises regularly, it becomes tougher for them to do so. This study provides a solution to this problem. This research aims at the construction of a smart cradle. This cradle is des igned using various input elements, output elements, a controller module, and a mobile application. The input elements consist of a temperature sensor, a sound sensor or a microphone,a moisture sensor, and a music player. The output elements include the motor and The controller used in this study is the NodeMCU module. Cloud storage is also used and it is provided by the Arduino loT software. The sensors in the smart cradle begin recording relevant parameters such as temperature, humidity, and sound as soon as it is turned on. The information is then uploaded to the cloud The data is then processed in the cloud for analysis. If indeed the child's temperatures is over the safe range, the user receives a warning. Moisture levels above normal suggest that the infant has urinated The sound of the baby crying will be recorded in the microphone. The output elements are energized as a result of the sensor's data. If the baby has a high fever or has to go to the bathroom, the app will notify the parent. © 2022 IEEE.

关键词： Microphones

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IoT b.s.d Optical sensor Network For Precision Agriculture

IoT Based Optical Sensor Network For Precision Agriculture

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： R s Vishnu Durai R. Vijayakumar s. Lakshmisridevi shaik Thasleem bhanu U. Arunkumar Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamilnadu India Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai Tamilnadu India Department of Electrical and Electronics Engineering Faculty of Engineering Karpagam Academy of Higher Education Deemed to be University Coimbatore Tamil Nadu India

Precision agriculture is a cutting-edge farming strategy that maximizes harvests by using cutting-edge technology and data-driven decision-making. Optical sensors and other Internet of Things (IoT) devices have great promise to revolutionize farming operations in this setting. sensor networks and Machine Learning (ML) b.s.d tracking devices are in great demand because of the precise data extraction and analysis they give. This research was undertaken with the goal of reducing agricultural hazards and promoting smart farming practices. Diseases caused by insects and other diseases may reduce crop yields if not addressed quickly. Thus, in this study, we provide a unique artificial swarm fish optimized naive bayes (AsFONb) method for keeping an eye on the health of the soil and preventing diseases from manifesting in cotton plants' leaves. In this research, numerous important indicators of crop growth and health were monitored using Internet of Things (IoT) devices equipped with optical sensors. The environmental factors like as temperature, humidity, light intensity, and chlorophyll content are recorded by these sensors. The proposed method involves sending the collected data to a central server for processing and analysis via wireless transmission. Once the disease has been detected, the information will be sent to the farmers via Android app. The Android app can show the chemical concentration in a container with soil factors like humidity, temperature, and wetness. Using an Android app, you may control the relay and hence the power supply and chemical sprinkler system. The experimental findings demonstrate that the proposed solution outperforms the status quo in disease identification.

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Deep Learning-b.s.d Diagnosis system for Liver Cancer Through Multimodal Determination for Early Treatment

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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Artificial Intelligence and Emerging Technology (Global AI summit), International Conference on

作者： N. sabiyath Fatima Ramendra singh Akuthota swathi Amar Choudhary Azhar Ahmed Chhaya sharma Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science & Technology Chennai India Department of Computer Science and Engineering (IoT) Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science and Engineering CVR College of Engineering Mangalpalli Rangareddy India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Computer Science and Engineering KCC Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The present research provides an analysis of the development and implementation of a deep learning-b.s.d diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, VGG 16, VGG 19, and Inception V3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that VGG 19 provides the highest diagnostic accuracy of 98.76 % , with VGG 16, Inception V3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted VGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results b.s.d on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms b.s.d on clinical case studies.

关键词： Deep learning Training Liver cancer Analytical models Accuracy Computational modeling Confusion matrices computer architecture Feature extraction Testing

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