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Intelligent Computing and Networking - Proceedings of IC-ICN 2023

作者： Surbakti, Herison Kumar, Athmakuri Naveen Tara, Saikumar Keshta, Ismail khare, Amit Prasad, G.N.R. Faculty of Computing and Digital Technology HELP University Kuala Lumpur Malaysia Senior Software Engineer Glosoft Technologies Pvt Ltd Chennai India Department of ECE BVRIT Hyderabad College of Engineering for Women Hyderabad India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Technology JIET Institute of Design and Technology Rajasthan Jodhpur India Telangana Hyderabad500091 India

ISBN: (纸本)9789819931767

In recent years, support vector machine has become one of the most important classification techniques in pattern recognition, machine learning, and data mining due to its superior classification effect and solid theoretical base. However, its training time will increase dramatically as the number of samples increases, and training will become more sophisticated when dealing with problems involving multiple classifications. A quick training data reduction approach MOIS appropriate for multi-classification tasks is presented as a solution for the aforementioned issues. While eliminating redundant training samples, the boundary samples that play a vital role are chosen in order to considerably reduce training data and the problem of unequal distribution between categories. The experimental results demonstrate that MOIS may maintain or even improve the classification performance of support vector machines while substantially enhancing training efficiency. On the Opt digit dataset, the suggested method improves classification accuracy from 98.94% to 99.05%, while training time is reduced to 15% of the original;in HCL2000, the proposed method improves classification accuracy from 98.94% to 99.05%. When the accuracy rate is marginally increased (from 99.29% to 99.30%) on the first 100 categories dataset, the training time is dramatically reduced to less than 6% of the original. Additionally, MOIS has a high operational efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Design of a Lower Limb Exoskeleton Rehabilitation Robot System Based on Forward Dynamics Simulation

Design of a Lower Limb Exoskeleton Rehabilitation Robot Syst...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Yudong Fang Yuefei Wang Liucun Zhu Zhen Liu Lei Shi Muxin Du Jianchu Lin School of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China Sentinel Institute of Science and Technology Beibu Gulf University Qinzhou China Nagasaki Institute of Applied Science Nagasaki Japan Guangzhou Shiyuan Electronic Technology Company Limited Guangzhou China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Lower limb exoskeleton rehabilitation robots can effectively improve the rehabilitation of stroke patients and can substantially reduce the workload of rehabilitation therapists and improve work efficiency. In this paper, we propose a method to investigate the influence of muscle groups on the hip joint during the gait cycle through kinetic modeling simulation. The method is validated using electromyographic signals (EMG). The patients are treated according to their gait characteristics. Simulation with OpenSim; optimisation by Scone controller; joint motion change curves were analysed by Matlab simulation to verify the correctness of the mathematical model, and the simulation showed excellent following effect. The feasibility of the theory is verified, and a new scheme is proposed for the treatment of stroke patients.

关键词： Limbs Exoskeletons Stroke (medical condition) Muscles Assistive robots Mathematical models Kinetic theory MATLAB Robots Optimization

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Client Segmentation and Customization in E-Commerce: Applications of Machine Learning from a Management Perspective

Client Segmentation and Customization in E-Commerce: Applica...

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Communication, computer Sciences and engineering (IC3SE), International Conference on

作者： Sriprasadh K Shamik Palit B Pravallika Manjunatha Reena Lenka Atul Singla Department of Computer Science and Applications Faculty of Science and Humanities SRM Institute of Science and Technology Vadapalani Chennai Department of Computing Science and Software Engineering RAK Campus University of Stirling UAE Institute of Aeronautical Engineering Hyderabad Department of Mechanical Engineering New Horizon College of Engineering Bangalore Symbiosis Institute of Management Studies Symbiosis International (Deemed) University Pune Lovely Professional University Phagwara India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques.

关键词： Consumer behavior Social networking (online) Machine learning Companies Market research Real-time systems Libraries

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Leaf Guard: Detecting the Diseases in Banana Leaf

Leaf Guard: Detecting the Diseases in Banana Leaf

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Advancements in Computing (ICAC), International Conference on

作者： K. Yathurshan S. Barathy G. Karini P. Sajitha Pradeep Abeygunawardhna Kaushalya Rajapakse Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Engineering| Electrical & Electronic Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

This research paper introduces “Leaf Guard,” a mobile app designed for disease detection and management in banana trees, specifically tailored for Sri Lanka's agricultural landscape. Through the amalgamation of image processing, deep learning, and customized chemical recommendations, this app proficiently identifies early-stage diseases, offering tailored remedies. Catering to farmers and agricultural experts, ‘Leaf Guard’ provides an intuitive tool, revolutionizing disease management and bolstering sustainable banana cultivation practices. By seamlessly integrating these cutting-edge technologies, this research significantly advances disease detection methods, paving the way for environmentally friendly and economical interventions. The study's findings not only enrich the realm of disease detection technology but also hold promise for future innovations, fostering sustainable approaches to enhance banana cultivation.

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Beyond Accuracy: Evaluating certainty of AI models for brain tumour detection

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computers in Biology and Medicine 2025年 193卷 110375-110375页

作者： Nisa, Zaib Un Bhatti, Sohail Masood Jaffar, Arfan Mazhar, Tehseen Shahzad, Tariq Ghadi, Yazeed Yasin Almogren, Ahmad Hamam, Habib Department of Computer Science and Information Technology The Superior University Lahore54600 Pakistan Lahore54600 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh11633 Saudi Arabia School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon University of Ha'il Ha'il55476 Saudi Arabia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada

Brain tumors pose a severe health risk, often leading to fatal outcomes if not detected early. While most studies focus on improving classification accuracy, this research emphasizes prediction certainty, quantified through loss values. Traditional metrics like accuracy and precision do not capture confidence in predictions, which is critical for medical applications. This study establishes a correlation between lower loss values and higher prediction certainty, ensuring more reliable tumor classification. We evaluate CNN, ResNet50, XceptionNet, and a Proposed Model (VGG19 with customized classification layers) using accuracy, precision, recall, and loss. Results show that while accuracy remains comparable across models, the Proposed Model achieves the best performance (96.95 % accuracy, 0.087 loss), outperforming others in both precision and recall. These findings demonstrate that certainty-aware AI models are essential for reliable clinical decision-making. This study highlights the potential of AI to bridge the shortage of medical professionals by integrating reliable diagnostic tools in healthcare. AI-powered systems can enhance early detection and improve patient outcomes, reinforcing the need for certainty-driven AI adoption in medical imaging. © 2025

关键词： Electrotherapeutics

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Sudha Merlin K. Malathy Ravi S Arun M Raut S.P H. Fawareh Department of Electrical and Electronics Engineering DMI College of Engineering Palanchur Chennai Department of MCA S.A. Engineering College Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Dept of Computer Science and Engineering Kings College of Engineering Punalkulam Thanjavur Department of Civil Engineering Yeshwantrao Chavan College of Engineering Nagpur India Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proliferation of consumer data can be attributed to “the Internet of Things (IoT),” which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics.

关键词： Analytical models Consumer behavior Accuracy Computational modeling Neural networks Predictive models Data models Internet of Things Logic Business

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A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

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computers, Materials & Continua 2021年第4期67卷 51-71页

作者： Mohamed Elhoseny Mazin Abed Mohammed Salama A.Mostafa Karrar Hameed Abdulkareem Mashael S.Maashi Begonya Garcia-Zapirain Ammar Awad Mutlag Marwah Suliman Maashi Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of DeustoBilbao48007Spain Department of Pure Science Ministry of EducationGeneral Directorate of CurriculaBaghdadIraq Department of Medical Laboratory Science Faculty of Applied Medical ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart disease(HD)is a serious widespread life-threatening *** heart of patients with HD fails to pump sufcient amounts of blood to the entire *** the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective *** methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related *** an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD ***,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with *** this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)*** MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD *** CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD *** evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation *** AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on ***,the AHDD system proposed herein has the highest capability to identify patients with *** system can be used by medical

关键词： Heart disease machine learning multi-agent feature wrapper model heart disease diagnosis HD cleveland datasets convolutional neural network

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Random forest‑based prediction of stroke outcome

arXiv

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arXiv 2024年

作者： Fernandez‑Lozano, Carlos Hervella, Pablo Mato‑Abad, Virginia Rodríguez‑Yáñez, Manuel Suárez‑Garaboa, Sonia López‑Dequidt, Iria Estany‑Gestal, Ana Sobrino, Tomás Campos, Francisco Castillo, José Rodríguez‑Yáñez, Santiago Iglesias‑Rey, Ramón Department of Computer Science and Information Technologies Faculty of Computer Science CITIC-Research Center of Information and Communication Technologies Universidade da Coruña A Coruña Spain SERGAS Universidade da Coruña A Coruña Spain Santiago de Compostela Spain Software Engineering Laboratory Department of Computer Science and Information Technologies Faculty of Computer Science University of A Coruña Campus de Elviña A Coruña15071 Spain Hospital Clínico Universitario Rúa Travesa da Choupana s/n Santiago de Compostela15706 Spain Santiago de Compostela Spain

We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3‑months after admission. The dataset consisted of patients with ischemic stroke (IS) and non‑traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/ morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3‑months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p‑value © 2024, CC BY.

关键词： Forecasting

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Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation

arXiv

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arXiv 2025年

作者： Naeem, Muhammad Ahtsam Saleem, Muhammad Asim Sharif, Muhammad Imran Akber, Shahzad Saleem, Sajjad Akhtar, Zahid Siddique, Kamran School of Information and Software Engineering University of Electronic Science and Technology of China China Faculty of Computing Riphah International University Faisalabad Campus Pakistan Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Department of Information and Technology Washington University of Science and Technology AlexandriaVA United States Department of Network and Computer Security State University of New York Polytechnic Institute United States Department of Computer Science and Engineering University of Alaska Anchorage Anchorage United States

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image;(2) Feature extraction is performed using a Deep CNN model, then these extracted features are concatenated;(3) Feature selection is performed using wrapper-based feature selection;(4) Classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features. © 2025, CC BY.

关键词： Deep learning

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Linking network- and neuron-level correlations by renormalized field theory

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Physical Review Research 2024年第3期6卷 033264页

作者： Michael Dick Alexander van Meegen Moritz Helias Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10) Department of Computer Science 3 - Software Engineering Peter Grünberg Institut (PGI-1) and Institute for Advanced Simulation (IAS-1) Institute of Zoology Department of Physics Faculty 1

It is frequently hypothesized that cortical networks operate close to a critical point. Advantages of criticality include rich dynamics well suited for computation and critical slowing down, which may offer a mechanism for dynamic memory. However, mean-field approximations, while versatile and popular, inherently neglect the fluctuations responsible for such critical dynamics. Thus, a renormalized theory is necessary. We consider the Sompolinsky-Crisanti-Sommers model which displays a well studied chaotic as well as a magnetic transition. Based on the analog of a quantum effective action, we derive self-consistency equations for the first two renormalized Greens functions. Their self-consistent solution reveals a coupling between the population level activity and single neuron heterogeneity. The quantitative theory explains the population autocorrelation function, the single-unit autocorrelation function with its multiple temporal scales, and cross correlations.

关键词： Neuronal network activity Statistical field theory Cortical networks Critical phenomena Dynamical mean field theory

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