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Leveraging Scalable Classifier Mining for Improved Heart Disease Diagnosis

Leveraging Scalable Classifier Mining for Improved Heart Dis...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Swetha, K.R. Ushasree, Linginedi Reddy, K. Satish Nisha, R. Shariff Parameshachari, B.D. Bgs Institute of Technology Adichunchanagiri University Dept of Cse Karnataka India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Hyderabad Institute of Technology and Management Department of Electronics and Communication Engineering Hyderabad India J.J College of Engineering and Technology Tamilnadu India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350301595

The scalable classifier mining technique proposed in this article aims to improve the accuracy and scalability of heart disease prediction models. This method employs a wide range of methodologies, including data preparation, feature selection, ensemble learning, and distributed computing, among others, to handle complicated patterns in large datasets quickly. By merging decision trees, random forests, and XGBoost classifiers with the Apache Spark distributed computing platform, the proposed method outperforms both traditional machine learning techniques and cutting-edge deep learning algorithms. The proposed method achieved an accuracy of 93.5%, which is higher than any of the conventional machine learning methods and comparable to or better than the most cutting-edge deep learning methods. By encompassing all of the essential processes, such as preprocessing, feature selection, ensemble learning, model merging, and distributed computing, this technique assures that the model is accurate, efficient, and easy to comprehend. As a result, it is an especially well-suited approach for clinical applications. The proposed technique has effectively shown its potential to increase the accuracy and scalability of heart disease prediction models, resulting in high accuracy, scalability, and interpretability. © 2023 IEEE.

关键词： Learning systems

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IOT Based Smart Agriculture Using LIFI

IOT Based Smart Agriculture Using LIFI

引用

International Conference on Multimedia, Signal Processing and Communication Technologies, (IMPACT)

作者： A Vijayakrishna Gopichand G Mohd Dilshad Ansari G Suryanarayana Department of Computer Science and Technology G Narayanamma Institute of Technology … Science India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Dept. of Computer Science … Engineering Guru Nanak University Hyderabad India Dept. of Computer Science … Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9781665476485

Modernization of the conventional farming procedure is one of the major concerns for a nation like India that needs to import agro items from various nations to cater to the needs of around 1.2 billion people. One of the main challenges of agriculture is that most of the conventional methods of agriculture are majorly manual which is still prevalent among the farmers. These results in unnecessary usage of pesticides and wastage of water in irrigating fields when not required. The proposed work is an IoT Based Smart Agriculture system using Light Fidelity (LiFi) that is fully automated and checks the farm field using IoT. Besides uncommon instruments, web related things or gadgets, things inserted with devices, sensors, programming forms Internet of Things (IoT) which is a key part of future web. The framework consists of sensors that are deployed in the field and they sense parameters like temperature, light and soil moisture and then data is analyzed by Micro Controller Unit (MCU). The data is then transferred to the receiver end through LiFi transmitter. The system has been developed by using PIC microcontroller as it is very convenient to use. LiFi offers more security over Wi-Fi or any wired connection and is faster hence the integrity of the agricultural data can be secured while transmitting for analysis. LiFi is eco- friendly too. Through IoT farmer can access the information from anywhere at any time. Automated monitoring of field parameters helps in producing high quality yield.

关键词： Smart agriculture Temperature sensors Wireless communication Temperature distribution Transmitters Light fidelity Internet of Things

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Cancer Death Cases Forecasting using Supervised Machine Learning 4

Cancer Death Cases Forecasting using Supervised Machine Lear...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Degadwala, Sheshang Vyas, Dhairya Kothari, Ankita Khunt, Urmila Sigma University Computer Engineering Department Gujarat Vadodara India University of Baroda The Maharaja Sayajirao Gujarat Vadodara India Krishna School of Emerging Technology and Applied Research Kpgu Dept. of Comp. Sci. & Eng. Vadodara India Itm Vocational University Computer Science Engineering Vadodara India

ISBN: (纸本)9798350300093

In India, like in the rest of the world, cancer is a major killer. This research objective is to predict cancer mortality in India, using supervised machine learning methods. Cancer mortality rates in India between 1990 and 2017 are provided by age group, gender, and region using data from the Global Burden of Disease Study. We employ three distinct supervised learning algorithms - linear regression, decision tree regression, and random forest regression - after performing data preprocessing, which includes missing value imputation and feature engineering. Using a variety of criteria, we analyze the effectiveness of these models and conclude that the random forest regression model is superior to the other two. The scope of research is provide a long-term prediction of cancer mortality in India using the best model so it will help health department to work on it. Our research has implications for policymakers and healthcare providers in India, where it may inform efforts to reduce cancer rates and improve cancer care. © 2023 IEEE.

关键词： Forecasting

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Proactive Fault Prediction in Microservices Applications Using Trace Logs and Monitoring Metrics

Proactive Fault Prediction in Microservices Applications Usi...

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Progressive Innovations in Intelligent Systems and Data science (ICPIDS), International Conference on

作者： Neha Kaushik Harish Kumar Vinay Raj Puneet Garg Dept. of Computer Engineering JC Bose University of Science & Technology Faridabad India Dept. of Computer Applications NIT Tiruchirappalli Tiruchirappalli India Department of CSE-AI KIET Group of Institutions Ghaziabad

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

An increasing number of developers and researchers are embracing microservices due to the scalability and flexibility benefits they provide. Fault prediction in microservices architecture based systems is crucial for minimising computation costs and reducing development time. Early prediction of faults and their locations during the system development life cycle simplifies maintenance and enhances resource utilisation. To our knowledge, little to no effort has been made in predicting faults beforehand, instead most of the research that has been done thus far has been on detecting faults after they occur. Early error detection allows for proactive remediation, avoiding any negative impact on the performance of the application. This paper fills this gap by proposing a fault prediction model based on trace logs and monitoring metrics for microservices applications. The paper has inspected two different models for fault prediction of microservices application and the forecasting accuracy of these models is compared by using standard performance measures. A case study microservices application is used for empirical analysis of these models and the results indicate that RNN predicts the faults with minimum errors and higher accuracy.

关键词： Measurement Technological innovation Accuracy Costs Microservice architectures computer architecture Predictive models Resource management Monitoring Standards

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Secure Deduplication with Dynamic Key Management in Fog Enabled Internet of Things

Secure Deduplication with Dynamic Key Management in Fog Enab...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Jay Dave Nikumani Choudhury Utkarsh Tiwari Samyu Kamtam Kudapa Sai Rohith Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Hyderabad India

Fog computing is an extension of cloud computing and presents additional devices called fog devices near IoT devices providing services on behalf of cloud servers. Although fog computing brings several advantages, rapid growth in the data generated by IoT devices increases communication and computational costs. As data sensed by IoT devices may correlate, there is a high possibility of duplicate copies in the sensed data. Data deduplication is a compression technique that reduces communication and storage overhead by skipping the upload and storage of duplicate copies of data. However, deduplication for a fog-enabled IoT system introduces new security issues. In this paper, we propose a novel secure deduplication approach with dynamic key management in a fog-enabled IoT system. We introduce a multilayer encryption scheme with dynamic key management to prevent the access of revoked users to the data. We implement the proposed scheme in a realistic scenario using Raspberry Pi and Firebase cloud services. The performance analysis shows that our approach achieves confidentiality and forward secrecy along with lower storage, computational, and communication costs.

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Low power implementation of Geometric High-order Decorrelation-based Source Separation on an FPGA board

Low power implementation of Geometric High-order Decorrelati...

引用

IEEE Cool Chips

作者： Ziquan Qin Kaijie Wei Hideharu Amano Kazuhiro Nakadai Dept. of Information and Computer Science Keio University Yokohama Japan Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

Open source software for robot audition called HARK aims to make “OpenCV” in audio signal processing, providing comprehensive functions from multichannel audio input to sound localization, sound source separation, and au-tomatic speech recognition. Since each of these HARK modules takes considerable energy when executed on PC, we propose to implement each module on an FPGA board called M-KUBOS connected. Here, we focus on the most computationally expensive function of HARK; the sound source separation, and implement it on a Zynq Ultrascale+ board. More than twice a performance improvement was achieved by using the sound frequency level parallelization in the HLS description compared to the software execution on the Ryzen 3990X64-core server. Power evaluation of the real board showed that the energy consumption is only 1/23.4 of the server.

关键词： Location awareness Energy consumption Source separation Power demand Speech recognition Parallel processing Servers

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Model-Driven Sensing-Node Selection for Maneuvering Target Tracking

Model-Driven Sensing-Node Selection for Maneuvering Target T...

引用

IEEE Conference on Wireless Communications and Networking

作者： Lei Xie Shenghui Song Yonina C. Eldar Dept. of ECE The Hong Kong University of Science and Technology Hong Kong Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Maneuvering target tracking will be one of the es-sential applications for future perceptive mobile networks, where multiple sensing nodes (SNs) collaboratively track the same tar-get. However, the associated SN selection is a substantial challenge due to stringent latency requirements of sensing applications. In this paper, we propose a model-driven approach by unfolding conventional optimization-based methods to tackle this problem. To this end, we first propose an iterative selection method based on the majorization-minimization (MM) framework and the alternating direction method of multipliers (ADMM). Then, we unfold the MM-ADMM approach as a deep neural network (DNN) to reduce the computational complexity and improve the performance, by leveraging an enhanced surrogate function. Simulation results demonstrate that the unfolded DNN outper-forms conventional methods with much lower computational complexity.

关键词： Target tracking Computational modeling Simulation Artificial neural networks Convex functions Sensors Computational efficiency

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Malware Detection and Classification for Internet of Things Using Self-Attention Based Long Short-Term Memory 3

Malware Detection and Classification for Internet of Things ...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Sudhakar, K. Husain, Saif O. Babu, D. Raja Kumar, M. Ashok Dineshkumar, R. Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

The Internet of Things (IoT) connects numerous intelligent devices providing security features that interact with default settings accessed through applications. Additionally, Deep Learning (DL)-based mechanisms was involved for malware detection to identify and mitigate attacks. The Adaptive Synthetic Sampling (ADASYN) technique addresses class imbalances, especially when splitter class instances are distributed data in complex patterns. The proposed Self-Attention based Long Short-Term Memory (LSTM) classification method facilitates the handling of long-range dependencies in transferred data sequences offering high flexibility and adaptability to varying input lengths. For feature selection Particle Swarm Optimization (PSO) is utilized iteratively optimizes a solution in a high-dimensional space although it may have a low convergence rate due to its iterative process. The obtained results demonstrate the proposed Self-Attention based LSTM model achieves better accuracy reaching 98.05% on the NSL-KDD dataset and 99.01 % on the UNSW-NB15 dataset. These results ensure accurate classification compared to other existing methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). © 2024 IEEE.

关键词： Internet of things

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Breast Cancer Prediction Using Machine Learning 5

Breast Cancer Prediction Using Machine Learning

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Dhondiyal, Shiv Ashish Luthra, Pooja Sharma, Bhupender Rajni, Rajni Uniyal, Ishita Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Coer University Department of Computer Science and Application Uttarakhand Roorkee India Guru Jambheshwar University of Science and Technology Department Of Computer Science & Engineering Haryana Hisar India Graphic Era Hill University Department of Electrical Engineering India

ISBN: (纸本)9798331522100

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer. © 2025 IEEE.

关键词： Image segmentation

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InterACT: Enhancing Human-AI Interaction

InterACT: Enhancing Human-AI Interaction

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Jithendra Katta Nikhil Reddy Kodumuru Swathi Katta School of Engineering and Applied Sciences The George Washington University Washington D.C USA Computer Science & Engineering dept. Vignana Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid advancement of large language models and computer vision systems has opened new frontiers in artificial intelligence. This paper introduces InterACT, a novel cross-modal system that integrates leading language and vision models to enable more intuitive and context-aware human AI interactions. By leveraging the strengths of both modalities, InterACT processes natural language queries to guide object detection in visual environments, bridging the gap between linguistic and visual understanding. We evaluate the performance of three language models of varying sizes in conjunction with an advanced object detection model, exploring the balance between computational efficiency and interaction quality. Our experiments demonstrate InterACT's potential across diverse real-world scenarios, revealing promising results in maintaining contextual relevance and adaptability. This research contributes to the growing field of cross-modal AI systems and lays the groundwork for more sophisticated human AI interactions, with implications for applications ranging from assistive technologies to educational tools.

关键词： Visualization computer vision Adaptation models Computational modeling Large language models Object detection Machine learning Computational efficiency Artificial intelligence Context modeling

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