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An approach for early prediction of multi-disease using ML techniques 2nd

An approach for early prediction of multi-disease using ML t...

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2nd international conference on Next-Generation computing and Information Systems, ICNGCIS 2023

作者： Malagar, Vani Sharma, Mekhla Upadhyay, Navin Mani Model Institute of Engineering and Technology J&K Jammu India

ISBN: (纸本)9781032738659

Leveraging the explosion of medical data including patient histories, electronic health records & medical imaging, the integration of machine learning techniques with healthcare for multi-disease prediction has experienced tremendous growth. Several algorithms have already been proposed by many researchers but they cannot fit for all the solutions at once. So, this research paper uses Machine Learning (ML) techniques to investigate the complexity involved in multi-disease prognosis in the context of convergence. It explores three well-knows ML- algorithms (Decision Tree, Random Forest classifier and Gradient Boosting) to investigate and compare the best way to identify and diagnose the prediction problems. Further a detailed comparison with existing algorithms shows a better result from 70%–77%. The accuracy of used algorithm has shown 78.57% for Decision Tree, 77.92% for Random classifier and for Gradient Boosting 95.38%. Similarly, the F1-score is also improved up to 3%. The paper also redefines several machine learning algorithms used by several researchers to solve the prediction problem, by emphasizing the importance of gradient boosting as a successful strategy. This work also validates the used models based on a statistical analysis "ANOVA and Chi-square". © 2025 The Author(s).

关键词： Decision trees

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Delay-Optimized Task Scheduling Based on Deep Reinforcement Learning in computing Power Networks 3rd

Delay-Optimized Task Scheduling Based on Deep Reinforcement ...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Tang, Qinqin Xu, Deyun Xie, Renchao Feng, Li Chen, Tianjiao Huang, Tao Beijing University of Posts and Telecommunications Beijing China China Mobile Research Institute Beijing China

ISBN: (纸本)9789819624676

With the rapid advancement of information and communication technology, there arises an increasing demand for heightened computing capabilities to facilitate a myriad of intelligent services. The computing power network (CPN) amalgamates an array of resources including cloud and edge devices to enable collaborative sensing, routing, and scheduling. This integration significantly amplifies the utilization of computing network resources while concurrently augmenting service quality. In the realm of multi-node distributed joint computing within CPNs conventional scheduling methodologies frequently neglect the interplay between computing and the network, resulting in suboptimal task scheduling. To address this issue, this study proposes a delay-optimized task scheduling mechanism based on deep reinforcement learning. Simulation results demonstrate that this framework achieves faster convergence speed, particularly under dual-depth networks, outperforming other algorithms in reducing system delays, energy and enhancing scheduling efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computing power

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Optimizing Latency and Communication in Federated Edge computing with LAFEO and Gradient Compression for Real-Time Edge Analytics 6

Optimizing Latency and Communication in Federated Edge Compu...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Namratha, P. Chaganti, Koushik Reddy Elicherla, Sivananda Lahari Reddy Guddati, Surekha Swarna, Akarapu Thiramal Reddy, P. Department of CSE GATES Institute of Technology and Science Anantapur India Dept. of Computer and Information Systems University of the Cumberlands KY United States Dept of CSE Dayananda Sagar University Bangalore India Dept of CSE Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

The increasing need for real-time data analytics in edge computing is furthered by the latency pressures from computational and communication bottlenecks, thus demanding novel solutions. Fundamental approaches to federated learning and distributed machine learning do not accommodate changes in dynamic latency conditions very well, thereby being inefficient in terms of synchronization, communication overhead, and delay in task execution. This paper presents a holistic framework consisting of three innovative approaches to solve these problems: Latency-Aware Federated Edge-Aware Optimization (LAFEO), Latency-Driven Gradient Compression with Adaptive Sparsity (LAGCAS), and Predictive Latency Compensation with Dynamic Replication (PLCDR). The LAFEO framework consists of a double-layer optimization framework, combining latency-weighted scheduling with reinforcement learning to dynamically adapt the synchronization frequencies. This results in 25-30% end-to-end latency reduction with an accuracy deviation that is capped at 2% centralized models. LAGCAS reduces communication overhead by applying adaptive gradient scarification methods, bringing compressions of gradients under real-time accuracy thresholds: it reduces 40% communication volume and accelerates convergence by 20%. PLCDR uses predictive modelling to predict latency bottlenecks and dynamically replicates critical computations across the edge nodes. It hence offers 15-25% decrease in the completion time of tasks and up to 30% enhancement in the resilience of system sets. The existing methods will be addressed with the key limitations due to the introduction of latency-aware mechanisms that adapt to real-time edge conditions along with high accuracy and efficiency. Simulation results have shown marked scalability improvements, latency reductions, and resilience;in its positioning, this work's effort is a transformative advancement in the latency-sensitive edge computing process. © 2025 IEEE.

关键词： Federated learning

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Research on optimization techniques for thread-level parallelism implementation targeting the GCC compiler 5

Research on optimization techniques for thread-level paralle...

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5th international conference on Signal Processing and Computer Science, SPCS 2024

作者： Chen, Mengyao Yan, Pengyan Han, Lin Li, Haoran Wang, Cuixia Zhenzhou University Zhengzhou450001 China Zhongyuan University of Technology Zhengzhou450001 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

The automatic OpenMP implementation in the current GCC compiler adopts the fork-join model, where frequent creation and convergence of thread groups result in significant management control overhead. This paper studies methods to reduce thread group creation and convergence to improve the efficiency of automatic OpenMP programs. A universal optimization method for merging parallel regions is proposed in this paper to address the fork-join model. Through variable attribute modifications, handling of serial statements, and synchronization operation optimization, adjacent continuous parallel regions are merged into a larger parallel region to reduce parameter passing within the parallel region and lower the overhead caused by the creation and destruction of parallel regions. This work is implemented based on the GCC 10.3.0 compiler and experimentally validated using the NPB 3.4-OMP test suite, achieving an average overall performance improvement of 20%. The experimental results demonstrate the effectiveness and generality of the method proposed in this paper. This method can effectively enhance the runtime efficiency of OpenMP programs, serve as a reference for optimizing the implementation of OpenMP programs, and provide support for thread dynamic merging techniques in AI compilation. © 2025 SPIE.

关键词： Program compilers

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Breast Cancer Stage Identification Using Machine Learning 11th

Breast Cancer Stage Identification Using Machine Learning

引用

11th EAI international conference on Advancement of Science and technology, ICAST 2023

作者： Arega, Birtukan Shegaw Mengsitu, Tesfahunegn Minwuyelet Faculty of Computing Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Information Convergence Engineering Pusan National University Busan Korea Republic of

ISBN: (纸本)9783031641503

This paper presents a machine learning model for determining the stages of breast cancer. The model normalizes images, eliminates noise, and removes artifacts through preprocessing, segmentation, and classification. Patching is used to extract the region of interest (ROI), while multi-thresholding is used for segmentation. The suggested models, including CNN-KNN, VGGNet-16, CNN-naïve Bayes, and CNN-SVM, were tested on a dataset of images from the CBIS-DDSM database. Experiments were conducted using full mammogram images and segmented ROI images, resulting in accuracies of 39%, 85%, 86%, and 91%, respectively. The CNN-SVM model achieved a 6% increase in classification accuracy compared to the state-of-the-art VGGNet-16 model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Emotional Contribution to Parkinson's Disease Diagnosis using Machine Learning for EEG Dataset Generation 6

Emotional Contribution to Parkinson's Disease Diagnosis usin...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Arockiya Selvi, S. Kamalakannan, T. Department of Computer science VEL’S Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

Emotion is the root cause of nearly all harmful illnesses in humans. Emotion affects blood pressure, heart rate, kidney function, and a few disorders of the nervous system. Parkinson's disease, which causes impairment of movement, is one of the brain disorders. This issue is caused by a reduction in dopamine release in the cortex of the brain. Dopamine production deviations have been related to essential hypertension. Dopamine has a crucial role in anxiety modulation in multiple regions of the brain. Patients with Parkinson's disease frequently indicate that acute stress worsens their motor symptoms, especially gait freezing, dyskinesia, and tremors and the emotions play a substantial role in Parkinson's disease. This study focuses on the Classification Learner algorithms and the Electro Encephalogram (EEG) dataset, both of which covered the diagnostic range of PD development. The classifiers were evaluated using key performance indicators such as error rate, accuracy, sensitivity, specificity, and precision. The findings illustrate the convergence of emotional analysis, machine learning (ML), and neuroscience in improving diagnostic capabilities by demonstrating how a combination of EEG datasets with KNN algorithms offers a reliable and accurate approach for early Parkinson's disease identification. © 2025 IEEE.

关键词： Blood pressure

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Revolutionizing Web Security with Lightweight, Minimal Vulnerability Detection

Revolutionizing Web Security with Lightweight, Minimal Vulne...

引用

2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Shankitha, M.G. Basavaraj, G.N. Mohan, B.A. Bms Institute of Technology & Management Department of Ise Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

Web vulnerability scanners have an important function of defining security vulnerabilities and possible penetrations in web applications. Old style scanners are usually more complicated with procedures and require many resources from a system which makes them rather inconvenient for developers and small businesses. This paper describes a new web vulnerability scanner that is designed to be extremely lightweight and easy to use while meeting the need for vulnerability detection. As a result of the application of minimalistic design and automation, the proposed scanner mainly targets fundamental features, including identification of well-known types of vulnerabilities, including SQL injections, cross-site scripting (XSS), and insecure headers. A modular design is used, and that means the scanner is built for flexibility and can adapt to different web environments. Performance evaluations show that this simplified approach has further accuracy and similar level of convergence as current methods while gaining considerable advantages in terms of setup time and computational burden. This work alleges to fill the gap between usability and security;giving users background information on how to incorporate effective vulnerability assessment into their developmental life cycle. © 2025 IEEE.

关键词： Performance evaluation Cross-site scripting Organizations SQL injection Forgery Security Usability Sustainable development Faces Payloads

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Enhanced Economic Load Dispatch Using a Novel Optimization Approach: A Cost Minimization Perspective 5

Enhanced Economic Load Dispatch Using a Novel Optimization A...

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5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Raaj, V. B. Thurai Srinivas, Chodagam Kumar, A. V. Pavan Rao, A.Venkata Subba Sindhuja, V. Navya, C.Venkata Madanapalle Institute of Technology & Science Department of Eee Andhra Pradesh India

ISBN: (纸本)9798350356236

This paper proposes an advanced African Vulture Optimization (AVO) algorithm integrated with Gaussian Process Regression (GPR) for the economic load dispatch (ELD) problem in power systems. The proposed method aims to enhance the ELD's efficiency by minimizing operational costs while maintaining power system constraints. The AVO algorithm optimizes the power generation across various units, and the GPR model is incorporated to improve convergence and accuracy in capturing the cost function's nonlinearity. The IEEE 30-bus system is used as a test case, examining three distinct load demand scenarios to evaluate the algorithm's robustness under varied conditions. Additionally, three different population sizes are applied to investigate the effects on algorithmic performance, specifically focusing on convergence rate and solution quality. Results demonstrate that the AVO-GPR method effectively reduces computational costs while achieving feasible and optimal power dispatch, outperforming traditional methods in terms of both accuracy and computational efficiency. This study contributes to the development of reliable and adaptive optimization techniques in economic load dispatch, supporting the operational efficiency of modern power systems. © 2025 IEEE.

关键词： Cost functions

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FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks 18th

FedDCT: A Dynamic Cross-Tier Federated Learning Framework in...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Xian, Youquan Gan, Xiaoyun Yao, Chuanjian Li, Dongcheng Wang, Peng Liu, Peng Zhao, Ying Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin54104 China School of Computer Science and Engineering Guangxi Normal University Guilin54104 China School of Business Guilin University of Electronic Technology Guilin54104 China

ISBN: (纸本)9783031714634

Federated Learning (FL), as a privacy-preserving machine learning paradigm, trains a global model across devices without exposing local data. However, resource heterogeneity and inevitable stragglers in wireless networks severely impact the efficiency and accuracy of FL training. In this paper, we propose a novel Dynamic Cross-Tier Federated Learning framework (FedDCT). Firstly, we design a dynamic tiering strategy that dynamically partitions devices into different tiers based on their response times and assigns specific timeout thresholds to each tier to reduce single-round training time. Then, we propose a cross-tier device selection algorithm that selects devices that respond quickly and are conducive to model convergence to improve convergence efficiency and accuracy. Experimental results demonstrate that the proposed approach under wireless networks outperforms the baseline approach, with an average reduction of 54.7% in convergence time and an average improvement of 1.83% in convergence accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Research on the allocation of kiwifruit yield tasks in planting areas based on improved local search algorithm 24

Research on the allocation of kiwifruit yield tasks in plant...

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2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Huang, Zhen Xi'an Mingde Institute of Technology Shaanxi Xi'an China

ISBN: (纸本)9798400709869

In the process of assigning kiwifruit production tasks, due to the large planting area of kiwifruit, it takes a long time to allocate suitable planting intervals for kiwifruit trees, resulting in an increase in algorithm execution time and cost. Therefore, it is proposed to further improve the local search algorithm based on the improved local search algorithm for kiwifruit yield task allocation, combined with the global search ability of the sparrow search algorithm and the introduction of variable helix factors and dimension wise lens learning strategies. The simulation results show that the improved local search algorithm has a faster convergence speed, and the curve has converged after 500 iterations. When the kiwifruit yield task is less than 30 tons, H is set to 2, and when the kiwifruit yield task is greater than 40 tons, H is set to 4. When the yield task is less than or equal to 40 tons, planting area 3 can meet the optimal planting interval. When the yield is greater than or equal to 50 tons, planting area 4 meets the requirements. Therefore, using an improved local search algorithm to allocate intervals between kiwifruit trees has good allocation performance, and can allocate kiwifruit trees reasonably based on different yield tasks and planting area. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Local search (optimization)

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