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Optimizing Chronic Kidney Disease Diagnosis: A Comprehensive Review of Metaheuristic Algorithms and Their Applications in Detection and Classification 8

Optimizing Chronic Kidney Disease Diagnosis: A Comprehensive...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Manjula, S. Hema Rajini, N. Chokkanathan, K. Annamalai University Feat Department of Computer Science and Engineering and Technology Tamilnadu Chidambaram India Alagappa Chettiar Government College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Karaikudi630003 India Madanapalle Institute of Technology & Science Department of Artificial Intelligence Andhra Pradesh 517325 India

ISBN: (纸本)9798350367904

The usage of metaheuristic algorithms for the diagnosis and classification of chronic kidney disease (CKD) is investigated in this work. To improve feature selection and classification accuracy, this study proposes several optimization algorithms including Genetic Algorithm (GA), Firefly Optimization Algorithm (FOA) and Particle Swarm Optimization (PSO). These methods are combined in the proposed method with machine learning models including support vector machine (SVM) and random forest (RF). With some methods reaching up to 9 9. 5 6 % accuracy, this proposed method obtains greater performance in terms of accuracy, precision, recall and F1-score by means of empirical evaluation employing CKD datasets. This study indicated the efficient handling of metaheuristic approaches managing issues in healthcare applications including noisy, unbalanced, and high dimensional datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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An overview of thalassemia: A review work

An overview of thalassemia: A review work

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Begum, Ruqqaiya Suryanarayana, G. Rama, B. V. Saketha Swapna, N. Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Tamil Nadu Chennai India Department of Computer Science & Engineering Vijay Rural Engineering College Telangana Nizamabad India

ISBN: (纸本)9781032669663

Thalassemia is a Disease that passes from parents to children through genes. It is a red blood cell disorder caused when the body doesn’t make enough of a protein in the blood called hemoglobin. Three classifications of Thalassemia are Alpha(a), Beta(b), DeltaBeta(db) And Beta thalassemia is the most effective disease compared to other types. Out of 5% of patients 3% comes in Beta Thalassemia. It is first identified in the early 19’s with the symptoms like poor growth, severe anemia, and huge abdominal organs. It is a disease of which most people are deceased due to unawareness, and not getting proper Medication, especially in rural areas as they cannot find symptoms because other disease symptoms differ from thalassemia. Out of 10000 newborns, approximately an average of 4.4 will be affected by thalassemia. Out of 32 districts In India, 10 to 12 thousand children were born with this disease. To date, no research work has shown the exact death rates of thalassemia worldwide. Blood transfusion is the major method used for the prevention of thalassemia-affected patients. Apart from that Bone marrow is another prevention method used for Beta Carriers. In this work, we gathered detailed Information about thalassemia and its types so that people will get aware of this disease. This work provides details of how this disease is getting inherited from parents to children, the depth of the characteristics of every subtype, and the statistics about causes and death rates from this disease. © 2024 The Author(s).

关键词： Blood

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AI-Based Vehicle Detection and Its Emission Impact on AQI 1

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19th Control Instrumentation System Conference, CISCON 2022

作者： Bhat, Suhas Sudhir Pai, M. D. Varun Holla, B. Ashutosh Pai, M. M. Manohar Department of Computer Science and Engineering Manipal Institute of Technology Manipal576104 India Department of Computer Science and Engineering Government Polytechnic Karkala Karkala574104 India Department of Information and Communication Technology Manipal Institute of Technology Manipal576104 India

ISBN: (数字)9789819995547

ISBN: (纸本)9789819995530

Vehicle traffic has a significant impact on urban quality of life. One major impact is on the air quality of the surrounding area. With increasing vehicle traffic, air quality gets drastically affected in urban cities. A dense accumulation of vehicles is commonly observed across a network of roads during peak hours, drastically affecting the air quality index of the surrounding environment. Adverse measures and prevention are required to control air quality to maintain a healthier environment. Hence, determining vehicle emission details is necessary to address the overall impact in the surrounding areas. For any organization/gated campus, it is required to minimize the air pollution caused by vehicles that regularly visit the campus. By identifying the vehicles appearing on the campus premises, their emission impact on the surrounding can be determined using the PUC certificate of the vehicle. This study performs a vehicle emission impact on a gated campus using a deep learning approach. Real-time surveillance footage is processed with a deep learning model to detect vehicle and its license plate. Furthermore, air quality sensors deployed at strategical locations provide real-time data on pollutant concentration. This combined information is further utilized by a Web application to provide statistics of AQI in real time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Air quality

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Review of Efficient Load Balancing Technique to Improve QoS Parameters Fog Computing 2nd

Review of Efficient Load Balancing Technique to Improve QoS ...

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2nd International Conference on Innovations in Data Analytics, ICIDA 2023

作者： Sharma, Arti Mahapatra, Rajendra Prasad Sharma, Vineet Kumar Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad201204 India Department of Computer Science and Engineering KIET Group of Institutions Ghaziabad201206 India

ISBN: (纸本)9789819734658

Fog networking is an aspect of the IoT (Internet of Things) idea, which sees most of the products used by humans on a daily basis connected to one another. Smart phones, smart health monitoring equipment, as well as other similar technologies are examples. The goal of fog computing (FC) is to lessen the burden on CC. Fog computing advances processing of data, communication, storing and analysis nearer to edge nodes and applications. To alleviate some of the drawbacks of cloud computing, a significant number of apps and services are shifting to fog computing. The main problem in fog computing is how to improve service quality. It is clear to observe that the SNNC and EBS have improved QoS to a great extent. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fog computing

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ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees

ConU: Conformal Uncertainty in Large Language Models with Co...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Wang, Zhiyuan Duan, Jinhao Cheng, Lu Zhang, Yue Wang, Qingni Shi, Xiaoshuang Xu, Kaidi Shen, Hengtao Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Drexel University United States Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761681

Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the closed-source nature of the latest large language models (LLMs). This study investigates applying conformal prediction (CP), which can transform any heuristic uncertainty notion into rigorous prediction sets, to black-box LLMs in open-ended NLG tasks. We introduce a novel uncertainty measure based on self-consistency theory, and then develop a conformal uncertainty criterion by integrating the uncertainty condition aligned with correctness into the CP algorithm. Empirical evaluations indicate that our uncertainty measure outperforms prior state-of-the-art methods. Furthermore, we achieve strict control over the correctness coverage rate utilizing 7 popular LLMs on 4 free-form NLG datasets, spanning general-purpose and medical scenarios. Additionally, the calibrated prediction sets with small size further highlights the efficiency of our method in providing trustworthy guarantees for practical open-ended NLG applications. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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An efficient extraction of the maximum power in a photovoltaic system based on enhanced P&O-Fuzzy logic controller

An efficient extraction of the maximum power in a photovolta...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Elzein, I.M. Kurdi, M. Department of Telecommunication and Networking Engineering University of Doha for Science and Technology Doha Qatar Department of Computer Science City University Lebanon

ISBN: (纸本)9781032803722

The concept of solar cell is changing the world in obtaining a clean energy that is an environment user friendly source of energy. Solar arrays process and convert the irradiance which is representing the quantity of light that comes from sun to a usable energy. The maximum power point which is denoted by Maximum power (Pmax) can be efficiently extracted from solar panels through the implementation of a tracking system denoted by the maximum power point tracking algorithm (MPPT). Such a tracking system is well known and utilized in detecting the ultimate maximum power that can be taken from the PV solar arrays under the circumstances of the various changes in both the temperature and irradiance. Through this analysis study, the Perturb and Observe (P&O) algorithm will be proposed to adjust the perturbation steps that will be controlled by an enhanced fuzzy logic controller. It will utilize the fuzzy concepts to extract the maximum power. In this research paper an analysis will be conducted to find the unknown parameters of the PV module along in selecting the appropriate set of fuzzy rules/fuzzy table as well as introducing a high gain dc-dc boost converter to improve the efficiency of power, response time, and reducing the oscillation around the Pmax in the proposed photovoltaic system. © 2025 The Author(s).

关键词： Solar irradiance

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Service Recovery in NFV-Enabled Networks: Algorithm Design and Analysis

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IEEE Transactions on Cloud Computing 2024年第2期12卷 800-813页

作者： Nguyen, Dung H. P. Lin, Chih-Chieh Nguyen, Tu N. Chu, Shao-I Liu, Bing-Hong Pham Van Dong University Faculty of Engineering and Technology Quang Ngai570000 Viet Nam National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung80778 Taiwan Kennesaw State University Department of Computer Science MariettaGA30060 United States

Network function virtualization (NFV), a novel network architecture, promises to offer a lot of convenience in network design, deployment, and management. This paradigm, although flexible, suffers from many risks engendering interruption of services, such as node and link failures. Thus, resiliency is one of the requirements in NFV-enabled network design for recovering network services once occurring failures. Therefore, in addition to a primary chain of virtual network functions (VNFs) for a service, one typically allocates the corresponding backup VNFs to satisfy the resiliency requirement. Nevertheless, this approach consumes network resources that can be inherently employed to deploy more services. Moreover, one can hardly recover all interrupted services due to the limitation of network backup resources. In this context, the importance of the services is one of the factors employed to judge the recovery priority. In this article, we first assign each service a weight expressing its importance, then seek to retrieve interrupted services such that the total weight of the recovered services is maximum. Hence, we also call this issue the VNF restoration for recovering weighted services (VRRWS) problem. We next demonstrate the difficulty of the VRRWS problem is NP-hard and propose an effective technique, termed online recovery algorithm (ORA), to address the problem without necessitating the backup resources. Eventually, we conduct extensive simulations to evaluate the performance of the proposed algorithm as well as the factors affecting the recovery. The experiment shows that the available VNFs should be migrated to appropriate nodes during the recovery process to achieve better results. © 2013 IEEE.

关键词： Recovery

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Deep Learning for Predictive Modeling of Cardiovascular Disease: Optimizing Neural Networks 8th

Deep Learning for Predictive Modeling of Cardiovascular Dise...

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Hossain, Mohammad Alamgir Amin, Sherif Tawfik Eishah, Hassan Abu Hamdan, Alfadil Ahmed Metwally, Abdelnasser Abdelwakil Limkar, Suresh Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Artificial Intelligence and Data Science AISSMS Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789819765805

Cardiovascular diseases (CVD) are a prominent contributor to illness and death on a global scale, underscoring the need for precise predictive models to facilitate timely intervention. The present study investigates the utilization of deep learning methodologies, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), in the context of predictive modeling of cardiovascular diseases. This study examines the efficacy of three well-known optimization techniques, namely Adam Optimization, RMSprop, and Stochastic Gradient Descent (SGD), within the framework of these neural network architectures. Among the various models based on Convolutional Neural Networks (CNNs), Stochastic Gradient Descent (SGD) has been identified as the optimizer that produces the most favorable outcomes for predicting CVD. The utilization of this optimization technique demonstrated exceptional efficacy in the training of the deep neural network, resulting in superior levels of accuracy, sensitivity, and specificity. On the other hand, it was observed that LSTM-based models exhibited the greatest improvement when utilizing RMSprop optimization. The utilization of RMSprop has been found to have a positive impact on the effectiveness of sequence modeling, resulting in enhanced predictive capabilities for assessing the risk of cardiovascular disease. The efficacy of this technique was demonstrated in its ability to capture temporal dependencies within the dataset, consequently enhancing the predictive capability of the model. The results of this study emphasize the importance of carefully choosing neural network architectures and optimization techniques when constructing predictive models for cardiovascular disease. Customizing the selection of neural network architecture and optimization algorithm according to the unique attributes of the dataset can substantially augment the precision and dependability of CVD risk evaluations. This, in turn, can ultimately lead t

关键词： Risk assessment

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Adaptive solar power generation forecasting using enhanced neural network with weather modulation

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10955-10968页

作者： Sujeeth, T. Ramesh, C. Palwe, Sushila Ramu, Gandikota Basha, Shaik Johny Upadhyay, Deepak Chanthirasekaran, K. Sivasankari, K. Rajaram, A. Department of CSE Siddartha Educational Academy Group of Institutions Andhra Pradesh Tirupati India Department of Mechanical Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Engineering and Technology MIT-WPU Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Graphic Era hill univeristy Dehradun India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. © 2024 – IOS Press.

关键词： Solar power generation

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Data Dissemination Framework for Optimizing Overhead in IoT-Enabled Systems Using Tabu-RPL

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SN computer science 2024年第4期5卷 343页

作者： Prajapati, Vishnu Kumar Sharma, T.P. Awasthi, Lalit Kumar Department of Computer Science and Engineering Nationtal Institute of Technology H.P. Hamirpur 177005 India

The IoT devices vary in terms of processing power, storage capacity, memory, and energy capabilities. These device heterogeneities pose unique challenges for efficient data dissemination. The authors propose a data dissemination framework designed to address IoT system challenges. The framework focuses on reducing network congestion and traffic, minimising data transmission, improving overall system performance, and alleviating the burden on IoT devices with limited resources. The RPL protocol dealt with the constraints of low-power, lossy networks and objective function influences the behaviour of routing protocols. The RPL objective functions are not based on different criteria and affect the network quality of the services. Furthermore, the framework incorporates Tabu Search Routing (TSR), an adaptive routing algorithm that dynamically adjusts data dissemination paths based on network conditions and device capabilities. The algorithm adapts to network topology and traffic load changes to optimise the routing of data packets. The TSR algorithm aims to find efficient paths for data dissemination by exploring search space and evaluating various routing options. Tabu-RPL objective function is simulated cooja-3.0 simulator for a large-scale IoT network to achieve a 30% reduction of network overhead, 20% less energy consumption, 25% more packet delivery ratio, and 20% reduction of end-to-end delay compared to EPC-RPL, Enhancement of RPL and EEOPS-RPL and finally achieve the quality of services of the network. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Data aggregation Data dissemination Internet of Things Objective function Tabu search routing algorithm

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