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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Bamansoor, Samer Pande, Bishwajeet Al Moaiad, Yazeed Pathmanathan, P. Ravindran El-Ebiary, Yousef A. Baker Latiff, Nurul Adilah Abdul Aseh, Khairi Aledinat, Lowai Saleh Yusoff, Fakhrul Hazman MEDIU Faculty of Computer and Information Technology Malaysia Faculty of Informatics and Computing UniSZA Malaysia Gyancity Research Consultancy Pvt Ltd Motihari India MEDIU Faculty of Computer and Information Technology Malaysia International Institute of Applied Science Swiss School of Management Netherlands UniSZA Faculty of Informatics and Computing Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia Universiti Teknologi MARA Malaysia

ISBN: (纸本)9781665432221

With the emergence of current developments and their devices that allow correspondence between organizations with each other, just as it is between organizations and service providers, just as with their customers, for some reasons, including advertising and a return to what is acceptable to them with the organization. Items, deals, etc., therefore, this examination provides an internal and external investigation of the current web-based business phase with a focus on promising conditions, risks, qualities, and shortcomings of FashionValet's electronic stage, whose administrations offer more than the Internet. Also, FashionValet is a stage organizer for online shopping and style in Southeast Asia other than Poplook, Zalora, and others, and the bulk of them are important competitors to FashionValet and they implement their own systems. The FashionValet experience, for example, is the history of the creator just as the other company's foundation has been pointed out to characterize how organizations, especially FashionValet, have moved from their primary stage to where they are today as the most aggressive design project. In addition, the technologies and innovations used by FashionValet are shown to provide a comprehensive detailed blueprint. To understand the improvement in style organizations in Malaysia, it was examined further. The paper outlines the critical elements to FashionValet's thriving as an end. © 2021 IEEE.

关键词： Online systems

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Navigating the Artificial Intelligence Revolution in NeuroOncology: A Multidisciplinary Viewpoint

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SSRN 2024年

作者： Saxena, Sanjay Panda, Soumyaranjan Tiwari, Ekta Singh, Rajesh Fouda, Mostafa M. Kalra, Mannudeep K. Kotecha, Ketan Saba, Luca Suri, Jasjit S. Department of Computer Science & Engineering International Institute of Information Technology Odisha Bhubaneswar India Department of Computer Science & Information Technology Mahatma Gandhi Central University Bihar India Department of Computer & Engineering Visvesvaraya National Institute of Technology Maharashtra Nagpur India Department of Research and Innovation Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Electrical and Computer Engineering Idaho State University PocatelloID83209 United States Department of Radiology Massachusetts General Hospital BostonMA United States Symbiosis centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Cagliari Italy Department of ECE Idaho State University PocatelloID83209 United States Department of CE Graphics Era Deemed to be University Dehradun248002 India University Center for Research & Development Chandigarh University Mohali India Pune India Stroke Diagnostic and Monitoring Division AtheroPoint RosevilleCA95661 United States Harvard Medical School United States University Hospital of Cagliari Italy

This comprehensive review presents the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in neuro-oncology, focussing their groundbreaking impact on the field. It gives a historical overview of AI's evolution in area of neuro-oncology, representing its progress from basic data management to high-level predictive analytics that now significantly makes personalized treatment ***, This study examines various ML based methods that emphasize brain tumor classification, genetic profiling, and individualized care plans proposals, which basically reflects the shift towards precision medicine. This paper also highlights, developments in DL particularly in the development of neuro-imaging analysis, which has significantly guided surgical planning and radiation therapy for improved patient outcomes. Through various case studies, the paper presents recent breakthroughs where AI interventions have noticeably enhanced the treatment of neuro-oncological conditions. Moreover, It also addresses the ethical considerations encompassing the use of automated decision-making tools, emphasizing the need for ethical governance in AI deployment. By integrating multidisciplinary perspectives, the study underscores the collaborative effort between clinicians, data scientists, and ethicists, which is very crucial for the ethical and effective implementation of AI in current clinical practice. The main aim of discussion is to equip neurooncologists/sergeons and doctors with understandings into sustainable AI adoption which ensures that AI's path in neuro-oncology aligns with both clinical needs for personalized medicine and ethical *** paper concludes by outlining considered methods and frameworks for the future integration of AI in neuro-oncology, predicting trends, and preparing to meet the challenges ahead. © 2024, The Authors. All rights reserved.

关键词： Personalized medicine

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A CNN-based Stock Price Trend Prediction with Futures and Historical Price 1

A CNN-based Stock Price Trend Prediction with Futures and Hi...

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1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Wu, Jimmy Ming-Tai Li, Zhongcui Srivastava, Gautam Frnda, Jaroslav Diaz, Vicente Garcia Lin, Jerry Chun-Wei Shandong University of Science and Technology School of Computer Science and Engineering China Brandon University Department of Mathematics and Computer Science Brandon Canada University of Zilina Department of Quantitative Methods and Economic Informatics Zilina Slovakia University of Oviedo School of Computer Engineering Oviedo Spain Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9781665404839

In this paper, focusing on the task of feature extraction using financial time series as well as trend prediction for prices, a new stock sequence array convolutional neural network model is presented. This model shows promising results in improving the accuracy in stock trading forecasts. The implemented model collects data from historical sources and futures (leading indicators) of stocks. The model then uses arrays as the input map for a Convolutional Neural Network (CNN) framework. Through in-depth experimental results using the Taiwanese stock market, we are able to show that the designed approach achieves strong results. Furthermore, where compared with state-of-the-art similar models, our results show promising performance among all compared approaches. © 2020 IEEE.

关键词： Forecasting

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Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

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computers, Materials & Continua 2021年第9期68卷 4191-4203页

作者： J.Jayanthi M.Kavitha T.Jayasankar A.Sagai Francis Britto N.B.Prakash Mohamed Yacin Sikkandar C.Bharathiraja Department of Computer Science and Engineering Sona College of TechnologySalem636005TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication Engineering University College of EngineeringAnna UniversityTiruchirappalli620024India Department of Mechanical Engineering Rohini College of Engineering and TechnologyPalkulam629401India Department of Electrical and Electronics Engineering National Engineering CollegeKovilpatti628503India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Kingdom of Saudi Arabia Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this *** various cancers identified so far,glioma,a type of brain tumor,is one of the deadliest cancers,and it remains challenging to the medicinal *** only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the *** diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of *** the captured image,the affected part of the brain is analyzed using magnetic resonance imaging(MRI)*** of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant *** of image processing techniques are available for processing the images to identify the affected *** study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization(P-BTLBO)algorithm that automatically segments a brain tumor in an given MRI *** involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization *** traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting *** results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its *** experimental findings show that P-BTLBO algorithm approach outperforms other existing algorithms of its kind.

关键词： Brain tumor TLBO algorithm skull stripping preprocessing segmentation

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Optimized SMOTE for Imbalanced Data Handling in Machine Learning

Optimized SMOTE for Imbalanced Data Handling in Machine Lear...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Gomathy B Jyothi P Kalyani G Ishwarya Niranjana M Mohanarathinam Jayaraj Velusamy Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India Department of AIMLDS QIS College of Engineering and Technology Ongole India Department of Civil Engineering QIS College of Engineering and Technology Ongole India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Nehru Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Machine learning models face imbalanced data as a significant challenge after reaching over two decades of development. The prevalent class imbalance creates imbalanced models which prefer the majority group while reducing performance numbers across minority classes. This research presents an optimized version of SMOTE for Imbalanced Data handling through machine learning (SMOTE-IDEA) that acts as a technique for imbalanced data classification. The process of making minority-class synthetic samples to address class imbalance has been traditionally handled through SMOTE approaches. Standard SMOTE applications allow unwanted irrelevant data points to enter the data which produces undesirable results. The research suggests a new version of SMOTE which addresses past limitations to enhance the oversampling procedure. SMOTE technique generates synthetic data more efficiently through several additions. The method selects significant features through an automated process that removes unimportant information. Sample filtering and high-quality synthetic instance generation take place as part of this method. The method adapts weight distribution schemes to ensure that data generation occurs primarily in high-density regions thereby improving minority class composition. Several classification methods analyzed different ratio-imbalanced datasets to evaluate the newly upgraded SMOTE approach. The proposed strategy demonstrates superior performance against traditional SMOTE implementation and top-level benchmark approaches by delivering improved classifications in terms of accuracy and precision in addition to recall and F1-score operation on minority and majority groups.

关键词： Support vector machines Adaptation models Accuracy Machine learning algorithms Filtering Data handling Classification algorithms Decision trees Standards Synthetic data

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Optimized SMOTE for Imbalanced Data Handling in Machine Learning 3

Optimized SMOTE for Imbalanced Data Handling in Machine Lear...

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3rd International Conference on Advancement in Computation and computer Technologies, InCACCT 2025

作者： Gomathy, B. Jyothi, P. Kalyani, G. Niranjana, Ishwarya M. Mohanarathinam Velusamy, Jayaraj Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India Department of AIMLDS QIS College of Engineering and Technology Ongole India Department of Civil Engineering QIS College of Engineering and Technology Ongole India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Nehru Institute of Engineering and Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798331543891

关键词： Sorting

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Underwater Image Restoration Based on Improved SwinIR Model

Underwater Image Restoration Based on Improved SwinIR Model

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Yanke Lu Fang Li Xiaonan Luo Guangxi Key Laboratory of Image and and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Image restoration is a classic foundational visual task, aimed at recovering damaged images, such as those affected by compression, blurring, or noise, to high-definition clarity. Although current image enhancement techniques largely rely on convolutional neural networks, the efficient capabilities of Transformers demonstrated in complex visual tasks have not yet been extensively applied in the field of image restoration. This paper presents an optimized image restoration strategy based on the SwinlR model, which is applied to the task of underwater image restoration. In the shallow feature extraction phase, deep separable convolution technology is employed; while in the deep feature extraction phase, a Spatial Gated Feedforward Network (SGFN) is utilized. The experimental data suggests that the novel approach surpasses conventional techniques in effectiveness and has effectively minimized both the model's parameter quantity and computational demands.

关键词： Visualization Convolution Computational modeling Noise Information processing Logic gates Feature extraction Transformers Image restoration Intelligent control

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Green Leaf Disease Detection Using Raspberry Pi and Random Forest Machine Learning Algorithm: An Automated Approach for Precision Agriculture

Green Leaf Disease Detection Using Raspberry Pi and Random F...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Chitra Sabapathy Ranganathan Amjath Ali Jakul Basha A Vijayalakshmi N. Mohankumar S. Sujatha C. Sasikala Mphasis Corporation Chennai Tamil Nadu India Department of Engineering Computer Engineering Electrical and Electronic Section University of Technology and Applied Sciences Ibra Sultanate of Oman Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

Farm production and food security are affected by green leaf diseases. Modern disease detection technologies may discover plant health issues early. This work uses Raspberry Pi, a cheap and flexible single-board computer, along the advanced Random Forest machine learning technique to identify a green leaf disease. To identify crop diseases, the study uses automated, non-invasive technologies. From labeled images of good and sickly leaves, the algorithm extracts color histograms and texture, and form. It feeds a Random Forest classifier these properties for classifying data with high dimensions. Several green leaf diseased plant species were examined. This method detected plant health concerns with high precision. Movement and low cost make the Raspberry Pi ideal for remote and resource-limited agriculture, increasing farming accuracy. Helping farmers and users identify and develop green leaf diseases improves efficient farming. This methodology has the potential to improve crop management, optimize pesticide usage, and increase agricultural productivity. These results might contribute to the development of advanced automatic disease detection techniques to address green leaf diseases in modern agriculture.

关键词： Productivity Precision agriculture Machine learning algorithms Accuracy Plants (biology) Crops Real-time systems Random forests Diseases Farming

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A Process-Oriented Deadlock Recovery Policy for Flexible Manufacturing Systems

A Process-Oriented Deadlock Recovery Policy for Flexible Man...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Andrei Karatkevich Iwona Grobelna Dept. of Applied Computer Science AGH University of Science and Technology Kraków Poland Autom. Control Electronics and Electr. Eng. University of Zielona Góra Zielona Góra Poland

ISBN: (纸本)9781665476881

This paper proposes a deadlock recovery method for flexible manufacturing systems modeled with Petri nets. Recovery transitions are added to the net allowing to return from all the deadlock markings to the legal ones. Unlike similar methods based on the recovery transitions, the proposed recovery policy focuses on process instances of the system. Its aim is to reset the lowest number of them, with minimal executed number of steps in a process instance. The proposed approach is formally analysed regarding its correctness and complexity, as well as illustrated with some widely used examples and compared with the similar known methods. The comparison shows that the presented method is efficient, despite the slightly different optimization criteria than used in other similar methods.

关键词： Law Firing Machine vision Petri nets System recovery Explosions Flexible manufacturing systems

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Quantifying the Contemporary Enablers in Achieving e-Governance for Sustainable Techno-Societal Development: A High Directive SEM Analysis

Quantifying the Contemporary Enablers in Achieving e-Governa...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Sonali Bhati Manish Dadhich Anand A Bhasker Kamal Kant Hiran Roshni Sharma Anurag Shukla School of Management Sir Padampat Singhania University Udaipur India School of Engneering Sir Padampat Singhania University Udaipur India School of Computer Science and Information Technology Symbiosis University of Applied Sciences Indore India Department of EAFM MLS University Udaipur India

This paper quantifies the contemporary determinants (economic, social, environmental, and e-governance) for sustainable techno-societal development. E-governance has the potential to drive sustainable societal development by promoting economic, social, and environmental sustainability. It can improve efficiency, transparency, accessibility, and cost-effectiveness in government processes while contributing to environmental sustainability by reducing paper usage and carbon emissions. This study employed 320 techno-respondents from Southern Rajasthan, and the model was empirically evaluated with statistical tests. This study uses a multidimensional research approach to establish a meaningful relationship between economic, social, and environmental factors in achieving e-Governance for sustainable societal development. The research developed and supported the research model using Smart-PLS. Furthermore, e-governance can improve access to education, healthcare, and social services, improving quality of life and reducing social inequalities. Despite some limitations, e-governance can be a valuable tool for achieving sustainable societal development, and its future scope can further enhance its effectiveness in addressing society's complex challenges.

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