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Markov Model Based Risk Prediction of Patients with Kidney Diseases

Markov Model Based Risk Prediction of Patients with Kidney D...

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chaki, Shambhab Rakshit, Pratyusha Chowdhury, Archana Jadavpur University Department of Electronics and Telecommunication Engineering India Christian College of Engineering and Technology Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798350373783

The paper proposes a novel machine learning approach for early prediction of risk of a patient suffering from severe kidney-related diseases (KD). The training phase consists of two steps. First, the records of the already existing patients with KD are divided into groups based on the similarity of the sequences of their clinical activities, including diagnoses, lab tests and medications. Next, a Markov chain model is employed to mathematically represent the clinical activity-sequences of patients in each group. The prediction phase also consists of two steps. First, the support of each group to the clinical-activities of the unknown patient P is evaluated. P is then assigned with the group with the highest support. Next, the Markov chain model of the winning group is used to predict the possible finite sequence of the clinical activities of P in future. Comparative study shows better performance of the proposed algorithm over other state-of-the-art algorithms. © 2024 IEEE.

关键词： Electronic health record

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Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

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computer Systems Science & engineering 2023年第5期45卷 1545-1559页

作者： P.Sumathi V.V.Karthikeyan M.S.Kavitha S.Karthik Department of Information Technology SNS College of EngineeringCoimbatoreTamilnaduIndia Department of Electrical and Electronics Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all ***,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper *** present decade,the application of deep learning models in many fields of research has created greater *** increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil *** that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil ***,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)***,soil nutrient data has been given as second input to the DNNR *** utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been *** training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the *** results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification *** results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.

关键词： Soil quality classification accuracy deep learning neural network soil features training and testing

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A Review on Traditional and Deep Learning based Object Detection Methods 5

A Review on Traditional and Deep Learning based Object Detec...

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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Solunke, Babruvan R. Gengaje, Sachin R. Walchand Institute of Technology Solapur Computer Science and Engineering Solapur India Walchand Institute of Technology Solapur Electronics and Computer Engineering Solapur India

ISBN: (纸本)9781665475242

Fast and accurate object detection systems are in high demand due to the advent of autonomous vehicles, smart video surveillance, facial detection, and numerous people counting applications. These systems not only detect and classify every object in an image or video, but also locate each one by creating a bounding box around it. This paper analyses the traditional and recent deep learning-based object detection methods from different perspectives, incorporating features recognition on many scales, data expansion, training approach, and perspective detection, in order to make it easier to deeply understand object detection. Some commonly used standard datasets for object detection are discussed. It also addressed the challenges and possible research scope in the future from the perspective of evolving object detection datasets and the framework for object detection tasks. From the analysis, it is observed that the performance of the methods in use for object detection is moderate and requires improvement, especially in difficult environments such as large object scale variance, obstructed object view, and horrific mild prerequisites. Therefore, the possible research scope for inventions and implementation of more novel deep learning methods to enhance object detection and classification accuracy is discussed. © 2023 IEEE.

关键词： Object detection

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Black Hole Attack Detection Using Dynamic Threshold Values and Residual Energy in VANETs

Black Hole Attack Detection Using Dynamic Threshold Values a...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Shalom Priscilla, I. Senthil Kumar, C. Buvaneswari, B. Shiva Kumar, M. Thiagarajar College of Engineering Department of Computer Science and Engineering Madurai India SRM Madurai College for Engineering and Technology Department of Electronics and Communication Engineering Madurai India

ISBN: (纸本)9798331512965

In black hole attack, a hostile vehicle or node within the VANET pretends to have the optimal route to the destination, convincing other nodes to route their data packets through it as they head towards the destination, trailing behind the malicious node, receives the data, it either discards the packets completely or forwards them selectively, causing disruptions in communication. To overcome this dilemma the research discusses a novel approach for categorizing nodes in AODV (Adhoc On demand vector Routing Protocol) networks according to their residual energy (RE) and forward rate (FR). With the goal to identify nodes that are considered energy-constrained, the proposed approach defines a residual energy threshold (RET) and includes dynamic threshold values (MinDTV and MaxDTV) for the forward rate. Three categories are applied to nodes: 1. (Trusted), 2. (Intermediate), and 3. (Untrusted or Energy-Constrained). This methodology results in a more trustworthy and energy-efficient network by dynamically updating node selection based on present forwarding behavior and levels of energy. The proposed approach boosts the overall strength, longevity as well, and integrity of RPL networks through the elimination of energy-depleted and unstable nodes;hence, it is especially suitable for applications in scenarios with scarce resources and malicious activity. © 2025 IEEE.

关键词： Black holes

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Coevolution with Bee Colony: Paradigm Shift in Many Objective Optimization

Coevolution with Bee Colony: Paradigm Shift in Many Objectiv...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Khoirom, Selina Pramanik, Shreya Rakshit, Pratyusha Chowdhury, Archana Jadavpur University Department of Electronics and Telecommunication Engineering India Christian College of Engineering and Technology Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798350373783

The research offers a unique coevolution-based many-objective optimization (MaOO) approach to benefit from the underlying parallelism of the evolutionary process. The proposed MaOO handles individual objectives in parallel using artificial bee colony (ABC) algorithm. After achieving convergence, a group of good-quality solutions is carefully chosen from each ABC population, dealing with a specific objective. Next, the groups are combined to form a union set. Finally, a fuzzy membership-induced rank measure is developed to discover the top-ranked equally good members of the union set, showing the approximate Pareto optimal solutions to the given MaOO problem. The proposed MaOO algorithm, referred to as fuzzy-bee colony (FBC) is compared with three state-of-the-art techniques. Experiments undertaken reveal that FBC outperforms its contenders with respect to the performance metrics. © 2024 IEEE.

关键词： Optimization algorithms

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Blockchain and IoT integration for secure short-term and long-term air quality monitoring system using optimized neural network

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Environmental Science and Pollution Research 2024年第27期31卷 39372-39387页

作者： Chinnappan, Balasubramanian Hakim, Kareemullah Kumar, Neelam Sanjeev Elumalai, Vijayalakshmi Electronics and Instrumentation Engineering B.S.A Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai600048 India Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus Tamil Nadu Chennai600026 India

Accurate air pollution prediction is vital for residents’ well-being. This research introduces a secure air quality monitoring system using neural networks and blockchain for robust analysis, precise predictions, and early pollution detection. Blockchain guarantees data integrity, security, and transparency. Goals include real-time air quality data, secure blockchain recording, and enhanced safety through informed decisions. The research integrates blockchain and IoT for short- and long-term air quality monitoring, utilizing an optimized neural network. IoT sensors collect PM2.5, PM10, CO, NO2, and SO2, processed through noise removal and normalization, with feature extraction using N-tuple contrastive learning. Predictions utilize Graph attention-based deep Residual shrinkage Network and Bidirectional long short Term Memory (GRNBTM) categorized into five levels. An adaptive bowerbird algorithm optimizes parameters, reducing computational complexity. Blockchain integration ensures secure, tamper-proof data storage with a lightweight consensus-based algorithm. The GRNBTM model’s air quality monitoring performance is extensively simulated and analyzed at 30-min, 2-h, 1-day, and 1-month intervals, demonstrating superior performance over existing techniques. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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Energy Measurement by Smart Metering using Blockchain technology 10

Energy Measurement by Smart Metering using Blockchain Techno...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Aishwarya, R. Chandrakala, D. Jeyanthi Yegammai, S. Varrshini, V. Kanaga Suba Raja, S. Vishnu Prasad, V. Easwari Engineering College Electrical & Electronics Engineering Chennai India SRM Institute of Science and Technology Computer Science & Engineering Tiruchirappalli India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353778

The development of demand-side management with controlled loads has received a lot of attention as a result of the smart grid's ongoing growth and the energy market's volatility. The large number of household loads in the residential sector has generated a great deal of interest in managing the energy consumption of individual residences. This paper suggests simulating the monitoring of several SHEMS (smart home energy management system) characteristics. We have concentrated on the functionalities of the suggested design that gather data and analyse it, and we'll apply them to common home loads like electric water heaters and charging stations for electric cars. Overall, we discovered that the modelling of the loads results in considerable financial savings for consumers when a SHEMS is put into place. Here, we'll use blockchain technology to transfer data securely. Every piece of energy data has a timestamp and a hash value. Since blockchain technology operates through code and encrypts all data, it is becoming more and more difficult for hackers to have a field day. Additionally, since all records are linked to one another via time stamps and hash codes, any change would have a significant impact on the entire database. © 2024 IEEE.

关键词： Energy utilization

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Techlab: Revolutionizing Lighting Automation Through IOT and Node MCU 4th

Techlab: Revolutionizing Lighting Automation Through IOT and...

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4th International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2023

作者： Mahendra, Vibhute Pritish Gopinath, Vanhere Payal Gopinath, Vanhere Harshal Bapu, Rajpure Akash Tukaram, Palve Vinaya Department of Electronics and Computer Sanjivani College of Engineering Maharashtra Kopargaon India Department Computer Engineering G.V. Acharya Institute of Engineering and Technology Maharashtra Shelu India

ISBN: (纸本)9789819752300

In today’s еvеr-еvolving еducational landscape, technological advancements play a pivotal rolе in еnhancing lеarning еnvironmеnts. This papеr prеsеnts "TеchLab," an innovativе automation systеm that rеvolutionizеs lighting control. By utilizing the potential of Internet of Things (IoT) and NodeMCU, TechLab provides an innovative solution for precise lighting control. TеchLab's main advantagе liеs in its ability to save electricity while providing a user-friendly еxpеriеncе. This automation systеm еmpowеrs usеrs to еxеrcisе prеcisе control ovеr lighting infrastructurе, allowing thеm to optimizе illumination conditions for various applications. In addition to its usеr-cеntric dеsign, TеchLab aims to rеducе еnеrgy consumption significantly. Thе nеcеssity for systеms likе TеchLab is еvidеnt in today's quеst for еnеrgy еfficiеncy, usеr convеniеncе, and adaptablе tеchnology. TеchLab addrеssеs this nеcеssity by introducing a sophisticatеd yеt еasy-to-usе systеm for lighting automation. During thе implеmеntation phasе, TеchLab dеmonstratеd an imprеssivе 20% rеduction in еlеctricity usagе, indicating its potеntial for significant еnеrgy savings. Additionally, a usеr satisfaction survеy rеvеalеd еxcеptionally high lеvеls of contеntmеnt, with 96.3% of usеrs еxprеssing satisfaction with thе systеm's pеrformancе. Usеr bеhaviour has bееn positivеly influеncеd by TеchLab, with usеrs activеly participating in еnеrgy-saving practicеs. Thеy arе еnjoying opеrating thе systеm and havе adoptеd thе habit of turning off lights whеn lеaving a room or lab. Thеsе bеhavioral changеs furthеr еmphasizе thе systеm's positivе impact on еnеrgy consеrvation. Thеsе quantitativе rеsults undеrscorе TеchLab's potеntial for еnеrgy consеrvation and еnhancеd usеr еxpеriеncеs. The system’s practicality and impact in еducational institutions arе еvidеnt, making it a promising solution for lighting automation in various sеttings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lighting

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental Science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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