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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Ozdemir, Resul Ozturk, Celal Tasyurek, Murat Aslantas, Veysel Erciyes University Department of Computer Engineering Kayseri Turkey Erciyes University Department of Software Engineering Kayseri Turkey Kayseri University Department of Computer Engineering Kayseri Turkey

ISBN: (纸本)9798350306590

Diabetes mellitus is a chronic disease caused by several factors, including age, obesity, physical inactivity, inheritance, and unhealthy nutrition. Therefore, early stage detection of chronic diseases is of utmost importance for individuals. In this light, intelligent approaches are needed to predict the risk of diabetes mellitus. In this study, a new deep learning model was proposed to estimate the risk of diabetes disease. The performance of the new model was compared to the popular machine learning algorithms. In order to improve model performance, hyperparameter optimization was handled by the Grid Search algorithm. The proposed model achieved 98.71% accuracy, 98.74% precision, 98.71% recall, and 98.71% F1-score performance on the UCI diabetes data set. The proposed model outperformed compared machine learning models. © 2023 IEEE.

关键词： Forecasting

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Enhancing the Parkinson's Disease Detection Through Machine Learning and Feature engineering 15

Enhancing the Parkinson's Disease Detection Through Machine ...

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

作者： Singh, Rosevir Ahuja, Sachin Kumar, Abhishek Chandigarh University Department of Computer Science and Engineering Gharuan India

ISBN: (纸本)9798350370249

The main objective of this study is to generate a common approach which links machine learning models for prediction of Parkinson's Disease (PD) having complications with the movement and other non-motor features. We considered PD diagnostic accuracy, as the early and precise identification play a vital role in management therefore, we explored different traditional machine learning algorithms;Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Gaussian Naïve Bayes, Decision Tree, and Random Forest Classifiers in order to determine how they could be used for predicting the disease. Addressing data imbalance, the Balanced Random Forest was an approach implemented to guarantee a fair data class representation. Our approach consists of three major stages starting with data selection and cleaning then processing, model training, and validation that allows us to comprehensively uncover and utilize data-driven insights to improve the precision and predictive power of our models. This paper illustrates step by step method including the application of feature engineering, performance optimization, and the strategic implementation of ensembles to overcome the challenges of PD diagnosing. It is suggested that we could be able to really make progress in early detection of Parkinson's Disease cases with the help of machine learning methods, which may represent novel research perspectives in the future and could be used in clinical practice. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Illicit Events Evaluation on the Dark Web Using NSGA-2 Algorithms Based on Energy Consumption

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Informatica (Slovenia) 2024年第18期48卷 77-96页

作者： Rawat, Romil Rajavat, Anand Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India

The proposed work objective is to adopt the non-dominated sorting genetic algorithm II (NSGA-II), a type of MOEA (multi-objective evolutionary algorithms), to reduce the dimensionality and identify the most relevant features. The objectives and hypotheses tested using NSGA-II and MOEA are to develop predictive models that best fit the problem of traffic analysis, analyse the set of features associated with the dataset, and apply NSGA techniques to reduce the data dimensionality. The work used the datasets and considered the Transnational Terrorist Hostage Event (TTHE) and Counter Trafficking Data Collaborative (CTDC) datasets, which relate to global human trafficking (HTr). The method used is focused on analysing the energy consumption (Econ) with the carbon footprint (CFP) of the proposed algorithm for different values of the main parameters on different hardware platforms, where the quality of each individual is computed based on ML algorithms. The result represents the "F-measure" metric obtained from support vector machines (SVM) and the number of features in order to determine the dimensionality, the execution time, the Econ, and the CFP during the feature engineering process. To estimate performance, the parameter values are computed using *** proposed work concludes that cybercrime employs and represents susceptible behaviour on the OSN Channels network. We identified the threat patterns by using NSGA-II and Econ analysis to establish connections between the variables in the dataset. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Support vector machines

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Predictive Insights for Enhancing Customer Retention in Banking: Integrating Power BI and Random Forest 3rd

Predictive Insights for Enhancing Customer Retention in Bank...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Sumanasri, A. Vyshnavi, M. Manogna, R. Nikhila Keerthi, Kelam Kesavarshini, A. Pande, Sagar Dhanraj School of Computer Science Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Maharashtra Pune India

ISBN: (纸本)9789819743582

The main goal of the study is to solve the problem of losing customers or clients in various financial organizations, especially in the banking sector. It investigates the use of Random Forest algorithm and the Power BI dashboard which helps to forecast churn and improve retention strategies. Historical data—transactions, demographics, and usage patterns—is collected and a thorough analyzation is done with the help of Power BI to provide a dynamic dashboard that provides insights in real-time. Combining data analytics technologies and predictive skills with their retention initiatives can help banks increase their efficacy and efficiency. Economic loss and operational difficulties are brought on by customer attrition. The outcome of this study suggests a method for forecasting customer attrition in banks that combines Power BI’s amazing data visualization techniques with Random Forest’s highly accurate predictive skills. The Random Forest algorithm integrates multiple decision trees to make predictions in a precise and expandable way. Moreover, it has the capability of handling large data sets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Gen-Power: Anomaly Detection in IoT Devices Utilizing Generated Power Waveforms

Gen-Power: Anomaly Detection in IoT Devices Utilizing Genera...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Hisafuru, Kota Togawa, Nozomu Waseda University Department of Computer Science and Communications Engineering Japan

ISBN: (纸本)9798350324136

Recently, IoT (Internet-of-Things) devices are very widely used in consumer electronics areas and their design and manufacturing are often outsourced to third parties to make them at a low cost. Meanwhile, malfunctions may be inserted into them intentionally by malicious third parties. Utilizing power waveforms measured from IoT devices is one of the effective ways to detect its anomalous behaviors. Most IoT devices regularly consume steady-state power due to the operating system and/or hardware components. However, the existing methods manually or semi-manually find out the steady-state power in the IoT device and extract pre-determined features from the application power waveform. Hence, they cannot well detect its anomalous behaviors automatically. In this paper, we propose a method, called Gen-Power, to detect anomalous behaviors in IoT devices utilizing the generative machine-learning model. The proposed method generates application power waveforms by inferring the steady-state power by machine learning from the observed total power waveform. Then, the anomalous application behaviors are detected by automatically extracting the latent features from the generative application power waveform. Experimental evaluations show that Gen-Power detects anomalous application behaviors successfully, while the recent state-of-The-Art method cannot detect them. © 2024 IEEE.

关键词： anomaly detection generated power waveform generative machine learning IoT device power analysis side-channel analysis

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A Color Histogram-Based Approach for Scene Segmentation in a Video 1

A Color Histogram-Based Approach for Scene Segmentation in a...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Majumdar, Soumya Rao, Krothapalli Sreenivasa Iit Kharagpur Department of Computer Science & Engineering Kharagpur India

ISBN: (纸本)9798350377002

A scene in a video consists of several shots and difference between shot and scene is that shot is captured from different camera locations whereas scene consists of shots that are captured at same location but from different camera angles. Simi-larity measurements between shots for detecting scene boundary are done in existing work using a sliding window of variable length. This method can lead to poor result if the sliding window size is too large or too small because success of this method depends upon video content. Our proposed method for video scene boundary detection and segmentation does clustering of keyframes instead of applying sliding window. Proposed method has four stages: shot segmentation with the help of tool, selection of keyframes with highest entropy from each shot, clustering of consecutive frames with most similarity, generation of scenes from clustered keyframes. Dataset used for experiment is four episodes of the first season of an American sitcom named 'The Bing Bang Theory' and the result is compared with existing state-of-the-art literature. Proposed method is time-consuming because of lesser number of computations. This method also has less time complexity because entropy differences of keyframes are computed instead of all frames. However, the presence of ads in the datasets used in the experiment affects the result in some cases. © 2024 IEEE.

关键词： Image segmentation

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Amazon Web Services Cloud Compliance Automation with Open Policy Agent 4

Amazon Web Services Cloud Compliance Automation with Open Po...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Paul, Alen Manoj, Rishi Udhayakumar, S. Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350385793

The security challenges posed by Infrastructure as code (IaC) are outgrowing established security procedures in an era of rapidly adopting cloud computing and DevOps methodologies. Using security principles to be integrated into the CI/CD pipeline for continuous validation and remediation, this research work proposes a DevSecOps approach to enable cloud environment IaC security. The proposed architecture evaluates CloudFormation Template file to the specified security policy by utilizing Open Policy Agent (OPA). Any violations are flagged by OPA, which may force the deployment process to stop. By incorporating security and compliance considerations into the development pipeline, this method guarantees that vulnerabilities are kept out of production systems. Organizations can improve the overall security posture of their cloud systems by proactively identifying and remediating security problems by implementing a DevSecOps approach. By ensuring that IaC deployments adhere to specified security policies based on the compliance requirements, OPA's continuous security validation reduces the possibility of misconfigurations and security flaws. © 2024 IEEE.

关键词： Cloud security

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Design and Optimization of Adaptive Network Coding Algorithms for Wireless Networks 15

Design and Optimization of Adaptive Network Coding Algorithm...

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

作者： Nadeem, Syed Mohammed Rao, Deepak Dasaratha Arora, Arpit Dongre, Yashwant V. Giri, Rakesh Kumar Jaison, B. G Pulla Reddy Engineering College Department of Computer Science and Engineering India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India College Sunrise University Department Computer Science & Engineering Alwar301028 India R.M.K.Engineering College Department Computer Science and Engineering Kavarapettai601206 India

ISBN: (纸本)9798350370249

This paper specializes in the layout and optimization of adaptive community coding algorithms for WiFi networks. The goal of those algorithms is to maximize the performance of network transmissions at the same time as heading off collisions and losses. We analyze the performance of numerous current network coding algorithms, examine their strengths and weaknesses and advocate techniques to optimize their performance. We also increase and compare new algorithms based on the adaptive network coding method. Those algorithms are designed if you want to mechanically adapt to the environment and take full benefit of the available assets to maximize the overall network's performance. The results of our simulations endorse that the proposed adaptive network coding algorithms outperform conventional network coding algorithms in phrases of both throughput and latency. We further propose an optimized model of our adaptive network coding algorithms and evaluate its performance to that of the unique algorithms. Our results demonstrate significant performance profits in terms of both throughput and latency. © 2024 IEEE.

关键词： Algorithms Latency Optimization Throughput Transmissions

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IoT-Enabled Personalized ICU Nutrition Plans: An Intelligent Approach Through Decision Tree Algorithm

IoT-Enabled Personalized ICU Nutrition Plans: An Intelligent...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Solainayagi, P. Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

Personalized care and continuous patient monitoring are signs of critical care units (ICUs), where the IoT may significantly enhance healthcare delivery. Optimal nutrition plans for ICU patients, taking into account their unique requirements, preferences, and medical issues, is a difficulty in IoT-enabled healthcare. Using Decision Tree (DT) algorithms, it provides a smart method for creating individualized ICU nutrition plans in this research. In the ICU, users take readings of vital signs using internet-connected equipment, including temperature, heart rate, blood oxygen saturation, and body mass index. After that, it sorts the patients into several groups according to their nutritional needs and potential dangers using DT algorithms that are used with the collected data. The system for patients is created using the categorization findings;these plans consider the patient's calorie, protein, fluid, and micronutrient consumption. By comparing the technique to the current ones and utilizing a simulated dataset of ICU patients, it assesses how well it performs. Better health outcomes and lower healthcare costs may be achieved by using the technique to create more precise and efficient diet plans for ICU patients. © 2024 IEEE.

关键词： Nutrition

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Speech Emotion Recognition System based on Auto Encoder 5

Speech Emotion Recognition System based on Auto Encoder

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Katyal, Rohit Sharma, Neeshu Chandigarh University Computer Science Engineering Department Mohali India

ISBN: (纸本)9798350376753

The most recent field of study to develop computer interface systems is emotion recognition through speech. In the proposed work two phases are designed, first phase is based upon feature extraction and second phase is based upon classification approach for a voice-signalled emotion recognition system. Initially, Mel-frequency cepstrum coefficients features of speech signals, pitch, zero crossing rate, amplitude and phase of a signal are extracted and introduced to auto encoder for feature selection. In the second, we suggest selecting relevant parameters from the previously retrieved parameters by using the Auto-Encoder approach. Then as a classifier technique, we employ Support Vector Machines (SVM). The Ryerson Multimedia Laboratory is used for experiments (RML) and tests the system using several categories, such as angry, disgust, fear, happy and surprise. The choice of characteristics that are extracted and the kind of classifier that is employed determine how accurate this system is. © 2024 IEEE.

关键词： Speech recognition

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