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2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Samantaray, Swati Patra, Sudhansu Shekhar Mohanty, Amlan Barik, Lalbihari Barik, Akash Brahma, Aditya Narayan Kiit Deemed to Be University School of Humanities Bhubaneswar India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India Iiit Department of Computer Science Bhubaneswar India King Abdulaziz University Faculty of Computing & Information Technology in Rabigh Department of Information Systems Saudi Arabia New Horizon College of Engineering Dept. of Computer Science Bengaluru India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

Folk dances are a primordial expression of ecstasy and bliss and serve as a mode of communication. They also facilitate in keeping the people connected to their traditions as well as ancestry. Furthermore, these dances help in preserving cultural unity and their tales can often reveal a lot about the periods these dances have developed through. Mahari, Gotipua and Odissi are such folk dance forms of the eastern state of Odisha. These folk dances are on the verge of extinction owing to the reducing number of practitioners, audience, absence of documentation, and digitalization. The absence of documentation has presented a barrier to the legitimacy of folk dance as an academic field of study. This research paper intends to focus on the classification of the folk dances of Odisha such as Mahari, Gotipua and Odissi which will help them to digitize and preserve. We propose deep learning-based methods for the classification and then it can be preserved. To validate the poses and hand motions, we offer a convolutional neural network model (CNN). © 2022 IEEE.

关键词： Neural network models

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Cloud-based Machine Learning Adoption Model for Higher Education Institutions

Cloud-based Machine Learning Adoption Model for Higher Educa...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Kiros, Atakilti Brhanu Salau, Ayodeji Olalekan Vats, Satvik Kaushik, Keshav Tin, Ting Tin Omeje, Crescent Onyebuchi Mekelle University Mekelle Institute of Technology Mit Mekelle Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Department of Cse Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248007 India Amity School of Engineering and Technology Amity University Mohali Punjab India Inti International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia University of Port Harcourt Department of Electrical/Electronic Engineering Rivers State Choba500004 Nigeria

ISBN: (纸本)9798350379716

As a new concept, cloud computing has attracted the IT Initiations attention especially the Mobile learning (M-learning). At present, there Higher Education Institutions (HEI's) have struggle with limited IT budgets, difficulties in obtaining and managing infrastructure licenses, and the complexities of software and hardware management. The coming of cloud computing, all of the above challenges was solved. The migration to cloud based M-learning in HEI's is a significant stage in terms of online education, especially during the COVID-19 pandemic. Cloud based M-learning approach has the potential to be a powerful and rapid solution to the many challenges faced by universities in this unprecedented time. This study starts with some brief concepts of Cloud Computing and M-learning for HEI's, mentioning for the significance of the results obtained so far. Additionally, it starting point as strategy for HEI's to adopt Cloud based M-learning is provided, by comparisons of existing models and select DOI model to propose cloud based adoption model. But, the DOI model has its own weaknesses;for example, it does not take into account the localization as Innovation determinants. This study introduces localization phase for the optimal performance of the adopted model. The adoption model was evaluating the importance of the proposed model by IT domain experts and it initiates to be both useful and suitable for a successful cloud based M-learning adoption Ethiopian perspective. Therefore, the research results obtained are promising and funding the use of Cloud based M-learning solutions for Ethiopia HEI's by improving knowledge and skill in this field provides a useful strategy for the HEI's structure. © 2024 IEEE.

关键词： Federated learning

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Palm Fruit Ripeness Detection and Classification Using Various YOLOv8 Models 9

Palm Fruit Ripeness Detection and Classification Using Vario...

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9th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2023

作者： Gunawan, Teddy Surya Kartiwi, Mira Mansor, Hasmah Yusoff, Nelidya Md. International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Universiti Teknologi Malaysia Kuala Lumpur Razak Faculty of Technology and Informatics Kuala Lumpur54100 Malaysia

ISBN: (纸本)9798350343380

The significance of palm oil, which contributes 30 % of the world's total vegetable oil production, cannot be overstated. Its numerous applications, ranging from soap to cosmetics, have increased demand, thereby increasing the importance of yield management. Human graders have traditionally been responsible for determining the ripeness of oil palm fresh fruit bunches (FFBs), a task upon which the oil extraction rate (OER) relies heavily. This rate has significant economic implications: a 0.13 % drop in OER due to unripe fruits can result in a staggering RM 340 million loss. Precision is stressed, prompting automated detection research. computer vision and Artificial Intelligence are becoming more effective at assessing oil palm fruit ripeness. However, many methods require complex operations, controlled settings, or manual calibrations. Although innovative, microwave sensors and inductive techniques have drawbacks like sample preparation and equipment dependence. This study investigates the potential of the YOLOv8 framework, particularly its YOLOv8m variant, for ripeness classification and detection. This model's mAP50-95 of 0.927 balances computational efficiency and accuracy, indicating its potential to revolutionize the palm oil industry's fruit assessment procedures. The findings here shed light on the model's efficacy and highlight its potential as an industry-standard solution, bridging gaps in ripeness detection methodologies. © 2023 IEEE.

关键词： Object detection

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A novel hybrid algorithm for lucky imaging

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Research in Astronomy and Astrophysics 2021年第5期21卷 155-167页

作者： Jin-Liang Wang Bin-Hua Li Xi-Liang Zhang Faculty of information engineering and automation Kunming University of Science and TechnologyKunming 650500China Key Laboratory of Applications of Computer Technologies of the Yunnan Province Kunming University of Science and TechnologyKunming 650500China Yunnan Observatories Chinese Academy of SciencesKunming 650011China

Lucky imaging is a high-resolution astronomical image recovery technique with two classic implementation algorithms,*** selecting,shifting and adding in image space,and data selecting and image synthesizing in Fourier *** paper proposes a novel lucky imaging algorithm where with space-domain and frequency-domain selection rates as a link,the two classic algorithms are combined successfully,making each algorithm a proper subset of the novel hybrid *** results show that with the same experiment dataset and platform,the high-resolution image obtained by the proposed algorithm is superior to that obtained by the two classic *** paper also proposes a new lucky image selection and storage scheme,which can greatly save computer memory and enable lucky imaging algorithm to be implemented in a common desktop or laptop with small memory and to process astronomical images with more frames and larger *** addition,through simulation analysis,this paper discusses the binary star detection limits of the novel lucky imaging algorithm and traditional ones under different atmospheric conditions.

关键词： methods:data analysis techniques:high angular resolution techniques:image processing

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Crop Optimization and Disease Detection using Satellite Imagery & Artificial Intelligence

Crop Optimization and Disease Detection using Satellite Imag...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： P. Chinnasamy D. Tejaswini Ramesh Kumar Ayyasamy S. Dhanasekaran B. Santhosh Kumar Likha Chandran Department of Computer Science and Engineering School of Computing Kalsalingam Academy of Research and Education Srivilliputtur TamilNadu India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Information Technology Kalasalingam Academy of Research and Education (Deemed to be University) Srivilliputtur Tamilnadu India Department of Computer Science & Engineering Guru Nanak Institute of Technology Ibrahimpatnam Telangana Department of Chemistry VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Small-scale farmers are essential for global food production yet lack access to modern technology and resources. This hinders informed decision-making, leading to reduced yields and economic hardship. Our project addresses this challenge by leveraging satellite data to deliver actionable insights to farmers, empowering them to enhance productivity and make informed decisions. We propose a mobile application that integrates AI with satellite imagery to revolutionize precision agriculture. By providing real-time data on soil health, water usage, and plant disease, the app enables farmers to optimize resource utilization, mitigate risks, and ultimately improve yields. Our project bridges the technological gap for small-scale farmers, transforming traditional practices into a more sustainable and efficient model. We believe this technology holds significant potential for positive socio-economic impacts on the livelihoods of farmers worldwide.

关键词： Pathogens Satellites Surveillance Crops Soil Satellite images Convolutional neural networks Artificial intelligence Optimization Water resources

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Recognition of Human Activity from ECG and IMU Signals Using Deep Learning Networks

Recognition of Human Activity from ECG and IMU Signals Using...

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2022 IEEE Region 10 Symposium, TENSYMP 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665466585

ISBN: (纸本)9781665466585

Human activity recognition (HAR) is becoming more significant in various industrial applications, including medical consideration and rehabilitation monitoring. With the rapid development of information and communication technologies, wearable technologies have inspired a new variety of human-computer interaction. Wearable inertial sensors are typically operated in the area of HAR because they deliver the most critical data on motion signals. Researchers in HAR continuously investigate alternative methodologies and signal sources to enhance HAR approaches. This study aims to determine the effect of combining biosignals with a publicly available dataset derived from wearable sensors on detecting everyday human activities. We utilized the MHEALTH dataset, which contains electrocardiogram (ECG), accelerometer, gyro-scope, and magnetometer data gathered from ten individuals engaged in twelve everyday actions. We proposed a unique deep learning technique for automatically extracting features and constructing a recognition model in several circumstances, including integrated sensor data. Our findings indicate that combining the ECG and IMU signals increases the F1-score of the classifier by 11.53%, from 86.83% to 98.36%. © 2022 IEEE.

关键词： Electrocardiograms

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Multi-view Semantic information Guidance for Light Field Image Segmentation

Multi-view Semantic Information Guidance for Light Field Ima...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yiming Cong, Ruixuan Wang, Sizhe Zhao, Mingyuan Zhang, Yang Li, Fangping Sheng, Hao Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Beijing University of Chemical Technology College of Information Science and Technology Beijing100029 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

One of the great important fields of computer vision is semantic segmentation. As for single image semantic segmentation, due to limited available information, it appears poor performance when the occlusion and similar color interference occur, and has difficulty exploiting the rich scene information. In comparison, the special micro-len array structure of light field camera can record multi-view information of the scene, which provides us with a new solution to solve this issue. In this paper, we propose a multi-view semantic information guidance network (MSIGNet) for light field semantic segmentation. It can effectively utilize semantic information from multi-view images to guide pixel feature of center view image. First, we extract feature of each view image and further obtain semantic probability. Then all probabilities are aggregated through a self-adaptive multi-view probability fusion module. Last, the resulting coarse fusion representation interacts with center view feature to obtain the refined segmentation result. The proposed method shows excellent performance on both real-world and synthetic light field datasets. © 2023 IEEE.

关键词： Semantic Segmentation

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information Theoretic Weighted Fuzzy Clustering Ensemble

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computers, Materials & Continua 2021年第4期67卷 369-392页

作者： Yixuan Wang Liping Yuan Harish Garg Ali Bagherinia Hamïd Parvïn Kim-Hung Pho Zulkei Mansor Department of Electronic Information Engineering School of Information EngineeringWuhan University of TechnologyWuhanChina School of Information Engineering Wuhan University of TechnologyWuhanChina School of Information Engineering Wuhan Huaxia University of TechnologyWuhanChina School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatialaPunjabIndia Department of Computer Science Dehdasht BranchIslamic Azad UniversityDehdashtIran Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsaan Malaysia 43600 UKM BangiSelangorMalaysia

In order to improve performance and robustness of clustering,it is proposed to generate and aggregate a number of primary clusters via clustering ensemble *** clustering ensemble approaches attempt to improve the performance of fuzzy clustering ***,in these approaches,cluster(or clustering)reliability has not paid much attention *** cluster(or clustering)reliability makes these approaches weak in dealing with low-quality base clustering *** this paper,we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means(RBFCM),Reliability Based Graph Partitioning(RBGP)and Reliability Based Hyper Clustering(RBHC)as three new fuzzy clustering consensus *** fuzzy clustering ensemble approach works based on fuzzy cluster unreliability *** unreliability is estimated according to an entropic criterion using the cluster labels in the entire *** do so,the new metric is dened to estimate the fuzzy cluster unreliability;then,the reliability value of any cluster is determined using a Reliability Driven Cluster Indicator(RDCI).The time complexities of RBHC and RBGP are linearly proportional with thnumber of data *** and robustness of the proposed method are experimentally evaluated for some benchmark *** experimental results demonstrate efciency and suitability of the proposed method.

关键词： Fuzzy clustering ensemble cluster unreliability consensus function

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DenMune: Density peak based clustering using mutual nearest neighbors

arXiv

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arXiv 2023年

作者： Abbas, Mohamed El-Zoghabi, Adel Shoukry, Amin Information Technology Institute of Graduate Studies and Research Alexandria Egypt Computer Science and Engineering Egypt-Japan University of Science and Technology New Borg-El-Arab City Alexandria Egypt Computer and Systems Engineering Dept Faculty of Engineering Alexandria Egypt

Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm "DenMune" is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neighborhoods of size K, where K is the only parameter required from the user, besides obeying the mutual nearest neighbor consistency principle. The algorithm is stable for a wide range of values of K. Moreover, it is able to automatically detect and remove noise from the clustering process as well as detecting the target clusters. It produces robust results on various low and high dimensional datasets relative to several known state of the art clustering algorithms. © 2023, CC BY.

关键词： Clustering algorithms

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Comparative Analysis of CNN-based Deep Learning Approaches on Complex Activity Recognition 7

Comparative Analysis of CNN-based Deep Learning Approaches o...

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7th International Conference on Digital Arts, Media and technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, NCON 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665495103

For those with Parkinson's disease, freezing of gait (FOG) could be a distressing condition. In individuals with diabetes, FOG could have a significant influence on their quality of life and cause falls, which can be dangerous. A questionnaire has traditionally been used to measure the severity of FOG symptoms;nevertheless, this technique is subjective and can not accurately reflect the intensity of this illness. The adoption of sensor-based devices may give reliable and objective information to follow the progression of symptoms, allowing for better Parkinson's disease management and therapy. A compact deep convolutional neural network comprising squeeze and excitation components, which we called SE-DeepConV model, was designed to identify FOG utilizing wearable data from the sensors. The SE-DeepConV model was constructed and evaluated using a publicly accessible standard FOG dataset, particularly the Daphnet dataset. The SE-DeepConV model outperforms other benchmark deep learning models in terms of accuracy, with the most outstanding accuracy of 95.65%, according to research observations. © 2022 IEEE.

关键词： Deep neural networks

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