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2023 IEEE International Conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Haarika, Raye Babu, Tina Nair, Rekha R. Rajesh, T.M. Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Applications Bengaluru India

ISBN: (纸本)9798350396195

As a result of high population growth, medical research and early sickness identification has become a responsibility. As the inhabitants grows, the likelihood of succumbing to breast cancer rises. A illness detection system that's automated reduces the danger of mortality, provides dependable, efficient, and rapid reaction, and assists doctors in disease diagnosis. to reinforce the predictive efficacy of current models and To estimate the risk of breast cancer, proposed model used Extreme Gradient Boosting (XGBoost) and compared it to Logistic Regression (LR), The Random Forest (RF), and Support Vector Machines (SVM). In this work, target picking the most weighted features using the XGBoost method and training the model with those features was performed. This approach yielded the identical accuracy as training the model with all features, however training the model with all features takes longer. The suggested approach has produced findings that are comparable and may help radiologists provide opinions © 2023 IEEE.

关键词： Population statistics

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Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

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Predicting Future Automobile Ownership Trends in Uzbekistan Using Linear Regression 2nd

Predicting Future Automobile Ownership Trends in Uzbekistan ...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Ather, Danish Singh, Malikhan Arora, Kavita Pathak, Sonal Qurashi, Suhail Javed Singh, Anupam Amity University Tashkent Uzbekistan Computer Science and Engineering College of Engineering & Technology Mathura India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819735938

The rapid urbanization and economic growth of Uzbekistan over the past decades have engendered significant changes in automobile ownership trends. Understanding these trends is crucial for infrastructure planning, sustainable transportation strategies, and environmental considerations. This Paper seeks to employ Machine Learning (ML) techniques to forecast the number of cars per 100 households in Uzbekistan by 2025, based on historical data from 2010, 2015, and 2020. The dataset, derived from official statistics provided by the State Committee of the Republic of Uzbekistan on Statistics, shows a remarkable increase from 21 cars per 100 households in 2010 to 48 by 2020. Leveraging regression models, we aim to predict the expected growth for 2025. Preliminary analyses hint at a continuing upward trend, impacted potentially by factors such as Population growth, GDP growth, urban migration, and increasing global connectivity. The findings from this research will not only serve as a prediction tool but will also shed light on the socio-economic factors influencing car ownership, providing valuable insights to policymakers and urban planners. While the limited data points present a challenge for complex model utilization, the study underscores the potential of ML in forecasting and the need for more comprehensive data collection in future studies. The predictive insights drawn from this research can significantly aid in shaping transportation policies, mitigating potential environmental impacts, and planning urban infrastructures in Uzbekistan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Preserve the Medical Data using Secure Partially Homomorphic Encryption with Blockchain Technology in Smart Healthcare 2

Preserve the Medical Data using Secure Partially Homomorphic...

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

作者： Kavitha, S. Pavithra, B.S. Shashikala, A.B. Jagadeesh, B.N. Mageswari, P. Uma Marichi, Dileep Nitte Meenakshi Institute Of Technology Department Of Mca Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Rns Institute Of Technology Department Of Information Science And Engineering Bengaluru India Presidency College Department Of Computer Applications Bangalore India

ISBN: (纸本)9798350381665

Healthcare information is vital for both individuals and service providers, necessitating secure sharing and maintenance of electronic healthcare records (EHR). Traditional EHR systems have relied on centralized architectures, such as the cloud, which may expose sensitive information to undisclosed intelligence. However, these systems have struggled to address the diverse needs of stakeholders while ensuring separation, security, and compliance with legal requirements. Blockchain technology offers a decentralized and distributed ledger system, providing secure, verifiable, and immutable data transfer capabilities. By employing cryptographic techniques like hashes, blockchain eliminates the need for a central authority, making data exploitation challenging and evident. This study proposes a blockchainbased architecture utilizing the Proof of Stake (POS) consensus mechanism to authenticate user identities and facilitate secure EHR exchange across multiple healthcare systems. Additionally, to enhance data security, Secure Partially Homomorphic Encryption (SPHE) is recommended for outsourced data, enabling users to perform operations on encrypted data. The proposed technique surpasses alternatives in terms of energy efficiency, authenticity, and protection of medical data. © 2024 IEEE.

关键词： Blockchain

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Performance Evaluation of Drones in FANETs using Queueing Model

Performance Evaluation of Drones in FANETs using Queueing Mo...

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

作者： Ghosh, Akash Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Pandey, Trilok Nath Pattanayak, Binod Kumar De, Utpal Chandra School of Computer Applications Kiit Deemed to Be University Bhubaneswar India School of Computer Science and Engineering Vit Chennai India Iter Soa Deemed to Be University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350313987

When conventional communication routes are unavailable or malfunctioning, mobile adhoc networks (MANETs) are essential for establishing communication. These networks can be easily and cheaply built whenever necessary because they don't require any pre-existing infrastructure. In some extreme situations (such as floods, battle zones, rescue operations, etc.), MANETs cannot be used. Lacking a fixed infrastructure in some situations, flying ad hoc networks (FANETs) can be quite useful for establishing communication. In FANETs, many drones are connected one to another directly or through middle nodes. In this research work, the arrival rate of events during the catastrophe, the drone's service rate, the waiting buffer of the system is considered and various performance measures such as the waiting time of the requests in the waiting buffer, the waiting time of the requests in the system, average queue length and the drones' idle time have all been considered. The ideal number of drones should be used to handle catastrophic occurrences and reduce casualties by adopting the queuing model. © 2023 IEEE.

关键词： Catastrophe Drones Flying ad-hoc networks Performance Measures Queueing Model

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Blockchain based Secure Erlang Server for Request based Group Communication over XMPP

Blockchain based Secure Erlang Server for Request based Grou...

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2023 International Conference on Advances in Intelligent Computing and applications, AICAPS 2023

作者： Johnpaul, C.I. Vivekanandan, Manojkumar Premkamal, Praveen Kumar Ramya, R. St. Thomas College Department of Computer Science and Applications Kerala Thrissur India Srm University-AP School of Engineering and Applied Sciences Department of Computer Science and Engineering Andhra Pradesh Amaravati India Kamaraj College of Engineering and Technology Department of Computer Science and Engineering Virudhunagar India

ISBN: (纸本)9798350333817

Many real world activities in computer science scenarios are linked with concurrency and security related issues and have to handle large number of processes to be executed in parallel with false safe security solutions. There are many traditional methods in programming languages to handle concurrency. Concurrency is one of the major issues that need to be addressed by most of the servers when dealing with the group communication operations. Security of the data as well as the credibility of the users are the other aspects when a group of users involve in real-time communication. Many light-weighted servers are designed to carryout elementary operations of request handling, file sharing etc. In design of such servers having large number of clients, the request service handling will be based on the individual server programs. Keeping track of individual credibility and establishing concurrency solutions in server design is challenging. The whole work describes the significance and implementation of an Erlang based XMPP server in comparison with a Python based XMPP server with a view to service the client request handling operations for sending messages, group chatting, buddy-list creation, presence identification integrated with XML messaging pattern as per the XMPP protocol. We also accomplish the security and credibility of the users using a blockchain based interface that keep track of user activities during group communication. The security analysis is also performed for blockchain based interface. © 2023 IEEE.

关键词： Python

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Blood Cell Classification and Counting in Hematology through Deep Learning 4

Blood Cell Classification and Counting in Hematology through...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ananthi, P. Devi, K. Nirmala Ramya, T.E. Shanmugapriya, P. Aradhya, T. Kongu Engineering College Department of Computer Technology Perundurai Tamil Nadu Erode India Madanapalle Institute of Technology & Science Department of Computer Applications India Kongu Engineering College Perundurai Tamil Nadu Erode India

ISBN: (纸本)9798331540364

Medical diagnosis is one of the areas that has been greatly influenced by the progress of computer vision technologies. This study presents a unique approach for the detection and counting of blood cells in hematology: You Only Look One algorithm version 7 (YOLOv7). YOLO is designed for real-time object detection, making it ideal for applications such as blood vessel detection where speed is critical. The architecture allows the model to process images faster than other recognition models, such as R-CNN or SSD, which is very important for situations that require fast results, such as analysis automatic blood at clinical sites. The purpose of the proposed method is to overcome the shortcomings of conventional methods and to accurately and quickly identify blood cells in mechanical images. The YOLOv7 model has been utilizedbecause it can detect objects in real-time with highprecision and speed. The methodology for bloodcell counting involves the use of YOLOv7 togenerate bounding boxes around each detectedblood cell in an image, where the count of these bounding boxes directly corresponds to the number of cells. This advancement in hematology not onlyimproves blood cell analysis efficiency but also expedites the process of diagnosing and planningtreatment in various medical situations. This paper details how YOLOv7 was modified and adjusted to meet the specific requirements of hematologicalimage analysis, including training models, preparing datasets, and evaluating performance. The study also addresses the potential impact of this technology on clinical workflows, highlightinghow it might help medical practitioners make decisions more quickly and intelligently. Hematology analysis systems including YOLOv7 are able to improve laboratory diagnostics and help patients by providing better care and results. This workconcludes by demonstrating the revolutionary potential of YOLOv7 in blood cell identificationand counting in hematology, paving the way for accurate and better

关键词： Diagnosis

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Precaution Chatbot for Epilepsy Patients using Natural Language Processing and Deep Learning Sequential Model 5

Precaution Chatbot for Epilepsy Patients using Natural Langu...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Kasthuri, E. Subbulakshmi, S. Sreedharan, Rajasree Department of Computer Science and Engineering Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amritapuri India Division of Nephrology Department of Pediatrics Medical College of Wisconsin MilwaukeeWI United States

Epilepsy is a brain disorder that can affect people of all ages which is one of the most common neurological disorders affecting around 50 million individuals. Healthcare Chatbots schedule appointments, respond to patient inquiries, and offer relevant information, freeing medical staff to handle life crises. This paper implements a chatbot for epilepsy patients to increase awareness about epilepsy. Patients may easily access this AI-based healthcare chatbot whenever they have queries regarding precautions to be taken by epilepsy patients. Chatbot uses a rich set of epilepsy-related datasets collected from trusted websites which are preprocessed with NLP techniques to prepare the data for model ***, LSTM, and RNN models are created and evaluated to produce answers to user queries related to possible precautions to avoid seizures, safety measures to be taken by patients with seizure history, etc. Evaluation of models shows that the RNN model generates more accurate answers with 94% accuracy. Finally, the epilepsy question answering Chatbot for precaution is concluded with the RNN model. © 2024 The Authors. Published by Elsevier B.V.

关键词： Deep neural networks

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Energy-Efficient Task Offloading for Edge Computing-Based Smart Grid Networks Using Human Urbanization

Energy-Efficient Task Offloading for Edge Computing-Based Sm...

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

作者： Patra, Sudhansu Shekhar Dash, Bibhuti Bhusan Pandey, Trilok Nath Pattanayak, Binod Kumar Tripathy, Abinash Chandrade, Utpal School of Computer Applications Kiit Deemed to Be University Bhubaneswar India School of Computer Science and Engineering Vit Chennai India Iter Soa Deemed to Be University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350313987

A microgrid is a generating source used to decentralise the centralised power supply in a small community. It produces, distributes, and regulates the community's power. It can balance the load demands of clients with fluctuating power needs since it is adaptable, dependable, and able to deliver uninterrupted electricity. The utility grid is connected to smart Microgrids, which assist the powering industries' requirement for electricity. They rely heavily on contemporary, integrated information and communication technologies (such as edge computing, cloud computing, fog computing, IoT sensors, etc.). Despite the low cost of the services offered by cloud computing, response, processing, and resource management times are issues. As a supplement to cloud computing, edge computing develops, offering services and tackling issues like latency, security, and traffic. The edge servers are used by smart meters to offload their computational tasks. The energy usage is rising as more data is processed in the edge servers. In order to reduce energy usage, this article suggests optimized task scheduling to edge servers using human urbanization optimization algorithm (HUOA). © 2023 IEEE.

关键词： Edge computing Energy optimization Human urbanization optimization Smart grid

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Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

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Big Data Mining and Analytics 2023年第1期6卷 32-43页

作者： Kalyan Kumar Jena Sourav Kumar Bhoi Soumya Ranjan Nayak Ranjit Panigrahi Akash Kumar Bhoi Department of Computer Science and Engineering Parala Maharaja Engineering CollegeBerhampur 761003India Amity School of Engineering and Technology Amity UniversityUttar Pradesh 201303India Department of Computer Applications Sikkim Manipal Institute of TechnologySikkim Manipal UniversitySikkim 737102India Directorate of Research Sikkim Manipal UniversityGangtokSikkim 737102India

As a huge number of satellites revolve around the earth,a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time ***,classifying satellite images plays strong assistance in remote sensing communities for predicting tropical *** this article,a classification approach is proposed using Deep Convolutional Neural Network(DCNN),comprising numerous layers,which extract the features through a downsampling process for classifying satellite cloud *** is trained marvelously on cloud images with an impressive amount of prediction *** time decreases for testing images,whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset *** satellite images are taken from the Meteorological&Oceanographic Satellite Data Archival Centre,the organization is responsible for availing satellite cloud images of India and its *** proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.

关键词： satellite images satellite image classification cyclone prediction Deep Convolutional Neural Network(DCNN) features layers down-sampling process

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