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DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm

Intelligent Automation & Soft computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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Next-Generation Consumer Electronics Data Auditing Scheme Toward Cloud-Edge Distributed and Resilient Machine Learning

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 2244-2256页

作者： Li, Yi Shen, Jian Vijayakumar, Pandi Lai, Chin-Feng Sivaraman, Audithan Sharma, Pradip Kumar Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Tindivanam604001 India National Cheng Kung University Department of Engineering Science Tainan701 Taiwan Netaji Subhas University of Technology Department of Information Technology New Delhi110078 India University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

Distributed and resilient machine learning (DRML) endues next-generation consumer electronics with AI function. Intuitively, AI provides innovative, humanized, convenient applications based on the data extended by next-generation consumer electronics. Cloud-edge computing is an ideal undertaken architecture of DRML due to its distributed property. However, as one of the core elements driving AI applications, data could be lost or corrupted owing to damaged electronics, unstable communication, and even cloud providers' malicious behavior. It is essential to ensure the data integrity of next-generation electronics before AI applications. To this end, we proposed a privacy-protection distributed data auditing scheme for cloud-edge DRML. An efficient data integrity verification method that only uses algebraic operation is constructed. Then, Function Secret Share (FSS) extends the data integrity verification method to protect consumer privacy. Besides, a consensus for data auditing results is designed among the edge servers. Finally, we present an abundance of theoretical analyses and experimental findings to substantiate and validate the efficiency and effectiveness of our proposed scheme. © 1975-2011 IEEE.

关键词： Edge computing

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Geopolitical Risk Analysis and its Impact on Market Volatility 9

Geopolitical Risk Analysis and its Impact on Market Volatili...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Rathinavel, Keshav Indira Kumar, A.K. Sahoo, Anshuman Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India Universiti Tunku Abdul Rahman Dept. of Internet Engineering & Computer Science Malaysia

ISBN: (纸本)9798350394474

This study addresses the influence of geopolitical risks on financial markets, as noticed in recent events like the COVID-19 pandemic and the Russia-Ukraine war. This study showcases a model combining financial indices with the Geopolitical Risk Index (GPRI), employing Bi-LSTM networks with attention mechanisms to analyze the data. This approach captures the nuanced effects of geopolitical events on market volatility. Focused on the pandemic period, our model offers insights into market dynamics during times of geopolitical unrest and serves as a predictive tool for market behavior in response to global events. © 2024 IEEE.

关键词： Risk analysis

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Lungcanary- Pioneering Early Lung Cancer Detection using Machine Learning Algorithms 8

Lungcanary- Pioneering Early Lung Cancer Detection using Mac...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Govindarajan, Priya Abhishek, M. Niharika, K. Tesfahun, Abebe Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru Campus India School of Electrical & Computer Engineering Debre Markos Institute of Technology Debre Markos University Ethiopia Ethiopia

ISBN: (纸本)9798331505462

Lung cancer, a malignant tumour originating in the lung cells, is notoriously challenging to detect early through conventional clinical procedures, which often require invasive techniques. This study introduces LungCanary, an innovative approach leveraging machine learning algorithms to address the global health challenge of early lung cancer detection. By employing advanced computational methods and utilizing transfer learning principles, LungCanary effectively extracts meaningful features from medical imaging data. The model incorporates multiple decision-making components to capture diverse data patterns, significantly enhancing diagnostic precision. Through rigorous experimentation, LungCanary demonstrated superior performance with an accuracy of 98.36%, a precision of 96%, and an error rate of just 1.64%. These findings highlight LungCanary's potential to outperform existing models, marking a breakthrough in the accuracy and reliability of lung cancer diagnostics. The significance of this research lies in its potential to revolutionize early detection methodologies, ultimately improving patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Pareto-Optimal Hybrid Beamforming for Finite-Blocklength Millimeter Wave Systems

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9910-9915页

作者： Singh, Jitendra Naveen, Banda Srivastava, Suraj Jagannatham, Aditya K. Hanzo, Lajos Indian Institute of Technology Kanpur Department of Electrical Engineering Kanpur208016 India Indian Institute of Technology Jodhpur Department of Electrical Engineering Jodhpur342030 India University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Short-packet communication (SPC) is essentially synonymous with ultra-reliable low-latency communication (uRLLC), which must meet stringent latency and reliability requirements. However, achieving efficient hybrid beamforming (HBF) in SPC-based millimeter wave (mmWave) systems is challenging due to the constraints of finite block lengths, limited number of radio frequency chains (RFCs), and owing to the complex optimization of transmit precoders (TPCs). In this work, we investigate the achievable rate region of an SPC-based mmWave downlink system. We harness the HBF for finite block lengths low-latency communication, relying on a low number of RFCs. We formulate a Pareto optimization problem for characterizing the achievable rate region, while considering the transmit power, mmWave hardware, and block length constraints. To solve this highly non-convex problem, we propose a bisection search-based block coordinate descent (Bi-BCD) algorithm, in which we optimize the RF TPC, the baseband (BB) TPC, and the block length. Specifically, we jointly optimize the RF and BB TPCs for a fixed block length, which involves both Remanian conjugate gradient (RCG) and second-order cone programming (SOCP) techniques, and then we optimize the block length by the mixed integer programming method. Subsequently, we update the achievable rate via the bisection search method. Finally, we present simulation results and quantify the efficiency of the proposed scheme. © 2025 IEEE.

关键词： Integer programming

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Identification and Detection of Leaf Miner, Pests Infestation in Cucurbitaceae Family in Real Time Infield Scenarios using YOLOv5s Object Detection Model 18

Identification and Detection of Leaf Miner, Pests Infestatio...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Keerthi Prasad, M.A. Shobha Rani, N. Sangamesha, M.A. Vinay, K.V. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India The National Institute of Engineering Department of Chemistry Mysuru India The National Institute of Engineering Department of Computer Science Mysuru India

ISBN: (纸本)9789380544519

This study contributes to technological developments in precision agriculture by developing a YOLOv5sbased model with a high F1 score and mAP, highlighting its potential for real-world use in wax gourd plants. In precision agriculture, early disease identification is crucial for effective harvesting. The present research focuses on the creation and evaluation of a YOLOv5s-based model for detecting pests and leaf miner infections in wax gourd plants in real time infield conditions at various stages of plant growth. Using a dataset of over 2000 samples collected in a variety of in-field settings, the model was trained for 200 epochs with a low learning rate (0.00001) and a batch size of 32. The achieved F1 score of 0.96 demonstrates the model's remarkable combination of precision and recall, ensuring precise disease detection. Furthermore, a mean Average Precision (mAP) greater than 0.45 demonstrates the model's proficiency in precise leaf localization and overall detection ability. The model's ability to detect numerous occurrences of disease in different leaves simultaneously is a crucial strength, reflecting the complexities common in real-world agricultural scenarios. The dataset's quantity and diversity help the model be robust and generalizable across diverse infection types and growth stages. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： crop health monitoring Data-driven agriculture object detection precision agriculture Smart agriculture sustainable farming

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Feature Enhancer Segmentation Network (FES-Net)for Vessel Segmentation

Feature Enhancer Segmentation Network (FES-Net)for Vessel Se...

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2023 International Conference on Digital Image computing: Techniques and Applications, DICTA 2023

作者： Khan, Tariq M. Arsalan, Muhammad Iqbal, Shahzaib Razzak, Imran Meijering, Erik School of Computer Science & Engineering Unsw Sydney Australia Qatar University Kindi Center for Computing Research Doha2713 Qatar Department of Electrical and Computer Engineering Islamabad Pakistan

ISBN: (纸本)9798350382204

Diseases such as diabetic retinopathy and age-related macular degeneration pose a significant risk to vision, highlighting the importance of precise segmentation of retinal vessels for tracking and diagnosis of progression. However, existing vessel segmentation methods that heavily rely on encoder-decoder structures struggle to capture contextual information about retinal vessel configurations, leading to challenges in reconciling semantic disparities between encoder and decoder features. To address this, we propose a novel feature enhancement segmentation network (FES-Net) that achieves accurate pixel-wise segmentation without requiring additional image enhancement steps. FES-Net directly processes the input image and utilizes four prompt convolutional blocks (PCBs) during downsampling, complemented by a shallow upsampling approach to generate a binary mask for each class. We evaluate the performance of FES-Net on four publicly available state-of-the-art datasets: DRIVE, STARE, CHASE, and HRF. The evaluation results clearly demonstrate the superior performance of FES-Net compared to other competitive approaches documented in the existing literature. © 2023 IEEE.

关键词： Image enhancement

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Exploring Video Event Classification: Leveraging Two-Stage Neural Networks and Customized CNN Models with UCF-101 and CCV Datasets 18

Exploring Video Event Classification: Leveraging Two-Stage N...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Sachdeva, Kumud Sandhu, Jasminder Kaur Sahu, Rakesh Chandigarh University Department of Computer Science and Engineering Punjab Gharuan India Galgotias University School of Computing Science & Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9789380544519

Rapid technological advances have significantly improved our ability to analyse video data. This comprehensive review examines machine learning (ML) models applied to video event detection and classification, including CNNs, deep neural networks (DNNs), and RNNs. When evaluated on benchmark datasets for accuracy, these approaches demonstrate their relative strengths and weaknesses. Researchers have encountered numerous challenges in video event detection, which are addressed throughout the review. However, achieving high detection precision remains challenging due to diverse event types, video quality issues, model over fitting risks, and lack of large labeled training datasets. Background scenes, lighting, and object occlusion further complicate accurate identification. As datasets and computational power grow, video event detection stands to gain significantly. This review assessed action recognition models trained on the UCF-101 and CCV databases. On CCV, a 2-stage neural network achieved 75% accuracy;while a multi-stream deep learning (DL) system obtained 77.5%. For the larger UCF101, 2-stream and RNN architectures realized 92% and 89% accuracy using video-level prediction. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： CCV datasets Convolutional Neural Network (CNN) Deep Neural Network (DNN) Recurrent Neural Network (RNN) UCF-101 Video Event Detection (VED)

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Profanity Detection from Audio Recordings Using Natural Language Processing Techniques

Profanity Detection from Audio Recordings Using Natural Lang...

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Patil, Swapnil Chandre, Pankaj R. Vanarote, Viresh Pathan, Shafi Nimbalkar, Madhukar Shinde, Gitanjali Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor India Department of Computer Science and Engineering Vishwakarma Institute of Information Technology Kondhwa India

ISBN: (纸本)9789819998104

Profanity detection has become increasingly important in various industries, including media and online content moderation. In this paper, we propose a novel approach to identifying profane words from audio using natural language processing (NLP) techniques. We first convert the audio files into textual transcripts using automatic speech recognition (ASR) tools. We then apply pre-processing techniques such as tokenization, stemming, and stop-word removal to the transcripts. Finally, we train various machine learning models, such as Naive Bayes, Support Vector Machines (SVMs), and Random Forests, to classify the transcripts as either containing profane language or not. The proposed approach can be integrated into various applications, such as content moderation tools, online chat systems, and automatic transcription services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audio recordings

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Intelligent Satin Bowerbird Optimizer Based Compression Technique for Remote Sensing Images

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computers, Materials & Continua 2022年第8期72卷 2683-2696页

作者： M.Saravanan J.Jayanthi U.Sakthi R.Rajkumar Gyanendra Prasad Joshi L.Minh Dang Hyeonjoon Moon Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Instrumentation Engineering Kongu Engineering CollegePerundurai638060India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

Due to latest advancements in the field of remote sensing,it becomes easier to acquire high quality images by the use of various satellites along with the sensing *** the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing ***,image compression techniques can be utilized to process remote sensing *** this aspect,vector quantization(VQ)can be employed for image compression and the widely applied VQ approach is Linde–Buzo–Gray(LBG)which creates a local optimum codebook for image *** process of constructing the codebook can be treated as the optimization issue and the metaheuristic algorithms can be utilized for resolving *** this motivation,this article presents an intelligent satin bowerbird optimizer based compression technique(ISBO-CT)for remote sensing *** goal of the ISBO-CT technique is to proficiently compress the remote sensing images by the effective design of ***,the ISBO-CT technique makes use of satin bowerbird optimizer(SBO)with LBG approach is *** design of SBO algorithm for remote sensing image compression depicts the novelty of the *** showcase the enhanced efficiency of ISBO-CT approach,an extensive range of simulations were applied and the outcomes reported the optimum performance of ISBO-CT technique related to the recent state of art image compression approaches.

关键词： Remote sensing images image compression vector quantization sand bowerbird optimizer metaheuristics space savings

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