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Design of a Multi-Stage Ensemble Model for Thyroid Prediction Using Learning Approaches

Intelligent Automation & Soft Computing 2024年第1期39卷 1-13页

作者： M.L.Maruthi Prasad R.Santhosh Department of Computer Science and Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatoreTamil NaduIndia

This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women *** major research problem is the lack of automated model to attain earlier *** existing model fails to give better prediction ***,a novel clinical decision support system is framed to make the proper decision during a time of *** stages are followed in the proposed framework,which plays a substantial role in thyroid *** steps include i)data acquisition,ii)outlier prediction,and iii)multi-stage weight-based ensemble learning process(MS-WEL).The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier *** classifiers aremerged to handle the issues identified in others and intend to enhance the prediction *** proposed model provides superior outcomes and gives good quality prediction *** simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing *** model gives a prediction accuracy of 97.28%accuracy compared to other models and shows a better trade than others.

关键词： Thyroid machine learning pre-processing classification prediction rate

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Secure authentication scheme with Archimedes optimization algorithm for load balancing technique in fog computing

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3861-3869页

作者： Premkumar, N. Santhosh, R. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

In this paper, to develop an efficient secure authentication scheme and load balancing technique in fog computing. To achieve an efficient secure authentical scheme in addition load balancing method in fog computing, Hybrid Edge DataCenters (HEDC) is developed in this paper. Normally, the EDC can be utilized to set up as a distributed system in addition it is located among the data source and cloud datacentre. This EDC network is also placed in the intermediate layer in the fog hierarchy among cloud datacentres in addition Internet of Things (IoT). This EDC is operated as an intermediate layer in fog computing which is responsible for load balancers and secure authentication in distributing the workload. This EDC fails to achieve efficient secure authentication and manage the workload in fog computing. So, the proposed HEDC is utilized to enable efficient authentication and workload management (load balancing). The proposed method is a combination of EDC and Archimedes Optimization Algorithm (AOA). Based on resource utilization and response time, efficient load balancing is achieved. Additionally, the secure authentication scheme is also attained with the assistance of the HEDC. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Archimedes optimization algorithm Data access control EDC Fog computing Internet of things Security enhancement

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Harnessing the Power of CNN-Transformer Encoders in Stress Speech Analysis

Harnessing the Power of CNN-Transformer Encoders in Stress S...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Shabiyya, Syifa'Hukma Prasetio, Barlian Henryranu Widasari, Edita Rosana Brawijaya University Master of Computer Science Faculty of Computer Science East Java Malang Indonesia Brawijaya University Computer Engineering Faculty of Computer Science East Java Malang Indonesia Brawijaya University Faculty of Computer Science East Java Malang Indonesia

ISBN: (纸本)9798350359633

Stress is the physiological response to mental, emotional, or physical stress, which varies between individuals. A survey by Ipsos Global showed that around 30% of respondents identified stress as a significant health issue. Some countries in Southeast Asia, such as Cambodia, have much higher rates of depression than the world average. In Indonesia, the stress rate reached 9.8% in 2018. This research focuses on Speech Stress Recognition (SSR), an automated method that recognizes stress levels through speech characteristic analysis. We use Mel-Frequency Cepstral Coefficients (MFCC) feature extraction and the CNN-Transformer Encoder model. Evaluation results on the SUSAS dataset showed an overall accuracy of 73.76%. When the classification results are viewed by gender, male data appears better at classifying stress levels than female data. To improve performance, we implemented the Voice Activity Detection method, which resulted in an accuracy of 81% for male and 69.23% for female. The findings of this research have potential applications in various fields, including mental health and emotion analysis in human communication. © 2023 IEEE.

关键词： Speech recognition

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Water level prediction using deep learning models:A case study of the Kien Giang River,Quang Binh Province

River

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River 2023年第4期2卷 468-479页

作者： Trieu T.Hieu Ta Q.Chieu Dinh N.Quang Nguyen D.Hieu Departments of Computer Science and Statistical and Actuarial Sciences Western UniversityLondonOntarioCanada VCREME Center of Research HanoiVietnam Faculty of Computer Science and Engineering Thuyloi UniversityHanoiVietnam Faculty of Civil Engineering Thuyloi UniversityHanoiVietnam

Time-series water level prediction during natural disasters,for example,typhoons and storms,is crucial for both flood control and *** data-driven models that harness deep learning(DL)techniques has emerged as an attractive and effective approach to water level *** paper proposed an innovative data-driven methodology using DL network architectures of Gated Recurrent Unit(GRU),Long Short-Term Memory(LSTM),and Bidirectional Long-Short Term Memory(Bi-LSTM)to predict the water level at the Le Thuy station in the Kien Giang *** models were implemented and validated based on hourly rainfall and water level observations at meteo-hydrological *** combinations of input variables with different time leads and time lags were established to evaluate the forecast capability of three proposed models by using five metrics,that is,R2,MAE,RMSE,Max Error Value,and Max Error *** results revealed that the LSTM model outperformed the Bi-LSTM and GRU models,when water level and rainfall observations for one-time lag at three stations were used to predict the water level at the Le Thuy station with 1-h time lead,with the five metrics registering at 0.999;3.6 cm;2.6 cm;12.9 cm;and−1 h,respectively.

关键词： Bi-LSTM deep learning GRU Kien Giang River LSTM water level prediction

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Enhancing Railway Safety with an IoT Based System for Real Time Fault Detection and Crack Monitoring 9

Enhancing Railway Safety with an IoT Based System for Real T...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Nalluri, Madhusudana Rao Chukka, Santhaiah Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

Safety of railway is the major problem worldwide. It has problems like cracks or any fault in the railway tracks. These problems can cause severe accidents if it is not detected regularly and early. In traditional fault finding and crack detecting method, the manual method is involved which is very slow and consist of many errors. By considering all these challenges and issues in track detection method, we have implemented an advanced IoT based railway crack and fault-finding system. It uses advanced sensors, microcontroller, GPS and GSM module which can detect and report faults of the railway track. The system uses real time mechanism. The system is implemented and tested on a prototype railway track to detect crack or any fault. The internet of things technology added modern and accurate fault-finding feature in the system. The IoT plays an important role in implementing advance technologies, as it uses sensors and advance microcontroller. The accuracy rate of fault and crack detection is around 97%, which is for 2 mm crack. Along with this GPS module accurately track fault location. GSM module successfully send the alert message quickly after detecting the fault in the track. Hence the response time is very less and accurate. This system is implemented in order to ensure safety for the railway track and prevent the accidents. Hence the system is useful for improving overall transport safety. © 2024 IEEE.

关键词： Microcontrollers

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Advancing Sustainable IoT Appliance Load Monitoring Through Edge-Enabled Federated Transfer Learning

Advancing Sustainable IoT Appliance Load Monitoring Through ...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Sri Preethaa, K.R. Sundaram, Karthic Rama Abirami, K. Kpr Institute of Engineering and Technology Computer Science and Engineering Coimbatore641407 India Dresden01062 Germany Curtin University Faculty of Engineering and Science Department of Electrical and Computer Engineering Malaysia

ISBN: (纸本)9798350357905

Non-intrusive Appliance Load Monitoring (NALM) is essential for efficient electricity consumption tracking in households, promoting eco-friendly practices, and cost reduction. Privacy concerns in real-world NALM implementations can be addressed using federated learning. However, challenges persist, such as limited training data, edge model customization, and resource constraints. We introduce a practical federated learning framework for NALM, leveraging cloud-based model compression, collaborative data collection, and personalized edge and multi-task learning models through unsupervised transfer learning. Our advanced load monitoring model offers precise energy disaggregation through real-world experimentation, making it a leading solution for non-intrusive appliance load monitoring on edge devices. It maintains excellent learning performance and user privacy. Future research should aim to enhance federated learning efficiency and tackle remaining challenges in practical NALM implementations © 2024 IEEE.

关键词： Cost reduction

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An efficient cloud based face recognition system for e-health secured login using steerable pyramid transform and local directional pattern

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1017-1027页

作者： Dosaj, Ayush Satapathy, Suresh Chandra Soundrapandiyan, Rajkumar Kaur, Mannat Hannoon, Naeem School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Kalinga Institute of Industrial Technology Deemed to be University Odisha Bhubaneswar India Faculty of Electrical Engineering Universiti Teknologi MARA Shah Alam Malaysia

This paper proposes a face recognition system based on steerable pyramid transform (SPT) and local directional pattern (LDP) for e-health secured login in cloud domain. In an e-health login, patients periodically forget their self-made login username and password. This face recognition system can replace the traditional login system. In the proposed system, SPT can decompose a face image into several sub-bands of different scales and orientations, and LDP can encode the sub-bands in binary texture pattern. The LDP binary pattern was obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Therefore, SPT–LDP design represents a face image in a robust way which includes multiple information sources from different scales and orientations. The proposed system is tested on standard benchmark facial recognition technology (FERET) and extended Yale-B databases. Further, the efficiency of the proposed system is proved by comparing recognition rates with other existing descriptor based methods. From the experimental results it is observed that the proposed system achieves 99.79% recognition in fb set, 90.72% in fc set, 84.97% in dup I set, and 87.69% in dup II set for FERET database and 100% in sub2 set, 100% in sub3 set, 96% in sub4 set and 95% in sub5 set for extended Yale-B database which is better than the existing methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Face recognition

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Prediction of student performance using machine learning techniques 5

Prediction of student performance using machine learning tec...

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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Elsaid Khoudier, Mohamed Mohsen Mahmoud Abdelnaby, Raneem Hany Eldamnhoury, Zeyad Mohamed Ali Abouzeid, Salma Raafat El-Monayer, Ghada K. Enan, Nada M. El-Ghamry, Amir Fouad, Khaled Moawad, Ibrahim New Mansoura University Faculty of Computer Science and Engineering Egypt Mansoura University Faculty of Computer Science and Engineering New Mansoura University School of Engineering and Computer Science University of Hertfordshire Hosted Global Academic Foundation Faculty of Computers and Information Egypt Benha University Faculty of Computer Science and Engineering New Mansoura University Faculty of Computers and Artificial Intelligence Egypt Ain Shams University Faculty of Computer Science and Engineering New Mansoura University Faculty of Computer and Information Sciences Egypt

ISBN: (纸本)9798350381030

The ever-increasing importance of education has driven researchers and educators to seek innovative methods for enhancing student performance and understanding the factors that contribute to academic success. This paper presents a methodology for predicting student performance (SPP) that leverages machine learning techniques to forecast students' academic achievements based on a variety of features, such as demographic information, academic history, and behavioral patterns. The proposed SPP method utilizes a real-world collected dataset from multiple educational institutions to ensure an accurate and comprehensive *** proposed methodology starts with a data preparation stage, where the data is cleansed and organized for analysis. This process encompasses tasks such as handling missing values, scaling the data, and transforming variables if necessary. The RFE selection technique was used to select the most important features for the SPP model. A number of machine learning classifiers were tested, and the linear regression algorithm was found to be the best performer. The evaluation results showed that the linear regression algorithm can be used to predict the student performance with a mean absolute error (MAE) of 0.23 and a root mean square error (RMSE) of *** results of this study demonstrate the potential of machine learning algorithms in predicting student performance and identifying key factors that influence academic success. This information can be leveraged by educators and academic institutions to develop targeted intervention strategies, tailored learning experiences, and personalized recommendations for students, ultimately fostering a more effective learning environment and improving overall educational outcomes. © 2023 IEEE.

关键词： Forecasting

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Advanced Hydro-Informatic Modeling through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the computer Society 2025年第1期6卷 726-738页

作者： Mahir, Shahariar Hossain Tashrif, Md Tanjum An Karim, Md Ahsan Kundu, Dipanjali Rahman, Anichur Hamza, Md. Amir Farid, Fahim Al Miah, Abu Saleh Musa Mansor, Sarina Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Multimedia University Faculty of Engineering Cyberjaya63100 Malaysia Department of CSE Nilphamari Bangladesh

Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylhet, Bangladesh where transboundary water flows and climate change have increased the risk of disasters. Accurate flood detection plays a vital role in mitigating these impacts by allowing timely early warnings and strategic planning. Recent advancements in flood prediction research include the development of robust, accurate, and low-cost flood models designed for urban deployment. By applying and utilizing powerful deep learning models show promise in improving the accuracy of prediction and prevention. But those models faced significant issues related to scalability, data privacy concerns and limitations of cross-border data sharing including the inaccuracies in prediction models due to changing climate patterns. To address this, our research adopts the Federated Learning (FL) framework in an effort to train state-of-the-art deep learning models like Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Feed-Forward Neural Network (FNN) and Temporal Fusion Transformer-Convolutional Neural Network (TFT -CNN) on a 78-year dataset of rainfall, river flow, and meteorological variables from Sylhet and its upstream regions in Meghalaya and Assam, India. This approach promotes data privacy and allows collaborative learning while working under cross-border data-sharing constraints, therefore improving the accuracy of prediction. The results showed that the best-performing FNN model achieved an R-squared value of 0.96, a Mean Absolute Error (MAE) value of 0.02, Percent bias (PBIAS) value of 0.4185 and lower Root Mean Square Error (RMSE) in the FL environment. Explainable AI techniques, such as SHAP, sheds light on the most significant role played by upstream rainfall and river dynamics, particularly from Cherrapunji and the Surma-Kushiyara river system, in d

关键词： Convolutional neural networks

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Network Bandwidth Optimization Using Software Defined Networking (SDN) 5

Network Bandwidth Optimization Using Software Defined Networ...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Haidar, Gaby Abou Zangana, Ali Noaman Khasro Owayjan, Michel Daou, Roy Abi Zeid Achkar, Roger American University of Science and Technology Faculty of Engineering and Computer Science Beriut Lebanon France La Sagesse University Faculty of Engineering Beirut Lebanon

ISBN: (纸本)9798331519957

This paper provides a detailed comparison of traditional networking architectures and Software Defined Networking (SDN) approaches, with a focus on bandwidth optimization and traffic management. Simulations were conducted under various networking scenarios, utilizing Cisco Packet Tracer for traditional networks and Mininet with the OpenDaylight (ODL) controller for SDN. The evaluated scenarios included single, dual, and triple PC setups, as well as high-Traffic conditions. Traditional networks demonstrated packet loss rates of up to 5%, limited bandwidth utilization (~2.443%), and increasing latency as more devices were added. In contrast, SDN-based setups showed no packet loss and significantly higher bandwidth utilization, reaching up to 94.478%. This study highlights the potential of SDN to enhance network efficiency and scalability, addressing a gap in the literature through scenario-based comparative analysis. Future research directions include exploring large-scale topologies, security enhancements, and advanced optimization techniques. © 2025 IEEE.

关键词： Packet loss

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