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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Devi Fitrianah Kristien Margi Suryaningrum Noviyanti Tri Maretta Sagala Vina Ayumi Siew Mooi Lim Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Informatics Engineering Program Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Computer Science Department Faculty of Computing and Information Technology Tunku Abdul Rahman University College Kuala Lumpur Malaysia

ISBN: (纸本)9781665473286

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of mobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%.

关键词： Training Biological system modeling Sociology Ecosystems Fish Statistics Monitoring

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Self-Assessment for Personal Growth: Evaluating the Effectiveness of the BeeBest mobile application

Self-Assessment for Personal Growth: Evaluating the Effectiv...

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International Conference on ICT For Smart Society (ICISS)

作者： Yasinta Indrianti Sasmoko Sonya Rapinta Manalu Lukas Tanto Kurniawan Abu Yazid Abu Bakar Davy Ronald Hermanus Entrepreneurship Department Universitas Agung Podomoro Research Interest Group in Educational Technology Jakarta Indonesia Primary Teacher Education Department Faculty of Humanities Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Mobile application & Technology School of Computer Science Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Faculty of Education Universiti Kebangsaan Malaysia Malaysia Computer Science Department Bina Nusantara University Research Interest Group in Educational Technology Bandung Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Self-assessment in personal growth is an important element in evaluating the effectiveness of mobile applications such as BeeBest, which are designed to support the concept of smart education by helping students with time management and self-development. This study evaluates the relationship between multiple intelligences, entrepreneurial awareness, and students' academic performance through the use of the BeeBest application. Using the SEM-PLS method, the results show that Intrapersonal Intelligence (coefficient 0.55) and Logic Intelligence (coefficient 0.45) have a significant influence on academic achievement. Entrepreneurial Mindfulness also shows a positive contribution (coefficient 0.35), reflecting the importance of focus, creativity, and stress resilience in supporting academic success. This model explains 60% of the variability in academic performance, reinforcing the importance of a personalized approach in smart education. The BeeBest application is proven to be effective in supporting self-management and academic growth through features that monitor student potential and provide learning recommendations based on intelligence and entrepreneurial profiles.

关键词： Entrepreneurship Educational technology mobile applications Logic Stress Monitoring Creativity Resilience

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MMTSA: Multi-Modal Temporal Segment Attention Network for Efficient Human Activity Recognition

arXiv

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arXiv 2022年

作者： Gao, Ziqi Wang, Yuntao Chen, Jianguo Xing, Junliang Patel, Shwetak Liu, Xin Shi, Yuanchun Institute Tsinghua University China Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University China Department of Computer Technology and Application Qinghai University China University of Virginia United States Department of Computer Science and Technology Tsinghua University China Paul G. Allen School for Computer Science and Engineering University of Washington United States Department of Computer Science and Technology Tsinghua University China Qinghai University China

Multimodal sensors provide complementary information to develop accurate machine-learning methods for human activity recognition (HAR), but introduce significantly higher computational load, which reduces efficiency. This paper proposes an efficient multimodal neural architecture for HAR using an RGB camera and inertial measurement units (IMUs) called Multimodal Temporal Segment Attention Network (MMTSA). MMTSA first transforms IMU sensor data into a temporal and structure-preserving gray-scale image using the Gramian Angular Field (GAF), representing the inherent properties of human activities. MMTSA then applies a multimodal sparse sampling method to reduce data redundancy. Lastly, MMTSA adopts an inter-segment attention module for efficient multimodal fusion. Using three well-established public datasets, we evaluated MMTSA’s effectiveness and efficiency in HAR. Results show that our method achieves superior performance improvements (11.13% of cross-subject F1-score on the MMAct dataset) than the previous state-of-the-art (SOTA) methods. The ablation study and analysis suggest that MMTSA’s effectiveness in fusing multimodal data for accurate HAR. The efficiency evaluation on an edge device showed that MMTSA achieved significantly better accuracy, lower computational load, and lower inference latency than SOTA methods. © 2022, CC BY-NC-SA.

关键词： Ubiquitous computing

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Using Asynchronous Frameworks and Database Connection Pools to Enhance Web application Performance in High-Concurrency Environments

Using Asynchronous Frameworks and Database Connection Pools ...

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International Conference on I-SMAC (IoT in Social, mobile, Analytics and Cloud) (I-SMAC)

作者： Lei Ju Amit Yadav Asif Khan Abhishek Pratap Sah Dakshina Yadav College of Intelligent Science and Engineering Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Industry Tourism and Trade Line Plant Industry Darwin Australia

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Modern web applications are experiencing exponential expansion in terms of the number of users and data volume, which puts increased demand on the system's capacity for high concurrent processing. This paper aims to explore the use of asynchronous frameworks and database connection pools in high-concurrency environments to significantly enhance the performance of web applications. Asynchronous framework enables applications to handle requests in a non-blocking manner, effectively releasing thread resources and improving the system's concurrency processing capability. Simultaneously, database connection pool optimizes the management of database connections resulting in reduced overhead of connection creation and destruction, thereby alleviating the burden on the database. This paper elucidates the significant impact of adopting asynchronous frameworks and database connection pools on improving system performance. The design adopts Springer WebFlux, an asynchronous framework, along with the proper configuration and utilization of database connection pool design, which exhibits lower average response time, higher throughput, better stability, and outstanding performance in high-concurrency scenarios. The developed model is tested, and it exhibits better results than synchronous models and outperforms asynchronous models without connection pool. This research holds practical guidance for enhancing web application performance and optimizing user experience.

关键词： Analytical models Databases System performance Throughput Stability analysis User experience Time factors Springs Thermal stability Testing

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MCLA Task Offloading Framework for 5G-NR-V2X Based Heterogeneous VECNs

TechRxiv

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TechRxiv 2023年

作者： Mirza, Muhammad Ayzed Yu, Junsheng Raza, Salman Ahmed, Manzoor Asif, Muhammad Irshad, Azeem Kumar, Neeraj BUPT-QMUL EM Theory and Application International Research Lab Beijing University of Posts and Telecommunications Beijing100876 China School of Physics and Electronic Information Anhui Normal University Wuhu241003 China School of Intelligence and Digital Engineering Luoyang Vocational College of Science and Technology Luoyang471000 China Department of Computer Science National Textile University Faisalabad Pakistan School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China Faculty of Computer Science Asghar Mall College Govt. of the Punjab Rawalpindi Pakistan Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Engineering Graphics Era University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Department of Computer Science and Engineering Chandigarh University Gharuan Mohali India Department of Electrical and Computer Engineering Lebanese American University Beirut Lebanon Faculty of Computing and IT King Abdulaziz University Jeddah Saudi Arabia

Ensuring dependable quality of service (QoS) and quality of experience (QoE) for computation-intensive and delay-sensitive applications in vehicles can be a challenging task that impacts performance. While multi-access edge computing (MEC) based vehicular edge computing network (VECN) and vehicular cloudlets (VC) enable task offloading, but their prompt and optimal accessibility is another challenge. The conventional wireless technologies may not suffice to meet the stringent ultra-low latency and cost constraints of such applications. Nonetheless, the combination of different wireless technologies can enhance network performance and satisfy these requirements. Focusing on the computational efficacy of VECN, this paper proposes a mobility, contact, and computational load-aware (MCLA) task offloading scheme for heterogeneous VECN. The MCLA scheme dynamically considers the mobility, contact, and computational load of vehicles for making task offloading decisions. To optimize the performance, the MCLA scheme integrates the Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave communications. The MCLA scheme provides an opportunistic switching mechanism between these modes and heterogeneous radio access technologies (RATs) to reduce communication delays and costs. Moreover, the MCLA scheme leverages public vehicles (i.e., public buses), in proximity by using their computational power to manage computational latency and cost. Furthermore, it also considers the shareable computations from passengers’ mobile equipment within the public vehicle to improve the computation capacity of the public vehicles. Extensive evaluations and numerical results show that the proposed MCLA scheme significantly improves the task turnover ratio by 4%–15% with 4.7%–29.8% lower transmission and computation costs. © 2023, CC BY.

关键词： Delay-sensitive applications

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Web Scraping Methods on Odoo Framework to Collect Rupiah Exchange Rate From Bank Indonesia Website 10

Web Scraping Methods on Odoo Framework to Collect Rupiah Exc...

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10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021

作者： Tarigan, Masmur Gaol, Ford Lumban Matsuo, Tukoro Universitas Bina Nusantara Doctor of Computer Science Graduate Program Jakarta Indonesia Doctor of Computer Science Universitas Bina Nusantara Jakarta Indonesia Japan Department of M-Commerce and Multimedia Applications Graduate School of Industrial Technology Asia University Japan Faculty of Computer Science Universitas Esa Unggul Jakarta Indonesia

ISBN: (纸本)9781665424202

The foreign currency exchange rate against the rupiah in the Odoo ERP framework system accommodates multi-currencies. However, Odoo has not updated the rupiah exchange rate based on the value from Bank Indonesia regularly. The exchange rate is the price at which one country's currency can be exchanged for another country's currency. The rupiah exchange rate in business has an influence on the cost revenue as well as the profit of the Indonesian company. The problem is updating the rupiah exchange rate must be done manually by looking at the exchange rate on the Bank Indonesia website and entering it into the ERP system according to the current date. The Rupiah exchange rate in foreign currencies needs to be updated regularly by Bank Indonesia exchange rates. To collect rupiah exchange rate uses the Odoo Framework ERP system using the web scraping methods to extract data from Bank Indonesia exchange rates, the library used in scraping is Beautiful Soup. Beautiful Soup is a Python package for parsing HTML and XML. Is used to extract data from HTML, which is useful for web scraping. Rupiah exchange rate updates will be scheduled by the system every day at 12.30 p.m. GMT+07. © 2021 IEEE.

关键词： Websites

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Identifying BeeBest E-application: An Analysis of User Feedback

Identifying BeeBest E-Application: An Analysis of User Feedb...

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International Conference on ICT For Smart Society (ICISS)

作者： Sasmoko Yasinta Indrianti Sonya Rapinta Manalu Lukas Tanto Kurniawan Abu Yazid Abu Bakar Davy Ronald Hermanus Primary Teacher Education Department Faculty of Humanities Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Universitas Agung Podomoro Research Interest Group in Educational Technology Jakarta Indonesia Mobile application & Technology School of Computer Science Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Faculty of Education Universiti Kebangsaan Malaysia Malaysia Computer Science Department Bina Nusantara University Research Interest Group in Educational Technology Bandung Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Smart education has an increasingly important role along with the development of knowledge and technology. The BeeBest application is specifically designed to make it easier for respondents to conduct self-assessments through innovative features that can improve the respondent's personal experience. User feedback analysis is an important process in the development and refinement of the BeeBest application. User feedback can provide important insights according to user needs and expectations. The research method used is the Neuroresearch Method, which consists of three stages: Exploratory, Explanatory, and Confirmatory Research. The results of the study show that User Satisfaction and Content Quality have the most significant influence on the performance of the BeeBest application, with a very high T Statistic value and a significant P Value. Personalization and Usability have the lowest influence on user feedback on the BeeBest application.

关键词： Educational technology Usability

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Analysis and Prediction of Parkinson's Disease using Machine Learning Algorithms

TechRxiv

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TechRxiv 2022年

作者： Sen, Sohom Ghosh, Ankit Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India School of Nuclear Studies and Application Jadavpur University Kolkata India

Even with radical advancement of medical science, there are medical abnormalities in human beings which are still incurable. First-time correct diagnosis of such ailment helps in decelerating the deterioration of a patient. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), nearly accurate diagnosis of various medical abnormalities is achievable;helping the Doctors to a great extent. Parkinson’s Disease is one such ailment for which no cure exists till date. It causes the malfunctioning of our Central Nervous System (CNS) leading to Tremor in limbs, Slowed Physical Movement, Rigid Muscles, Impaired Posture and Balance, Loss of Automated Movements and sometimes Loss of Speech. This article focuses on implementing State-Of-The-Art Machine Learning Models namely, K-Nearest Neighbour (KNN), Logistic regression (LR), Random Forest Classifier (RFC) and XGBoost Classifier (XGBC) for near accurate diagnosing of whether a patient suffers from Parkinson’s Disease or not. The ML Models are implemented on Oxford Parkinson's Disease Detection Dataset. The following metrics were used for the performance analysis of implemented ML Algorithms: Precision, Recall, F1-Score, Accuracy, AUC-ROC Curve and AUC-PR Curve. The Performance Analysis revealed, XGBoost Classifier to have an overall highest Accuracy of 96.7%, Precision of 0.8571, Recall of 1.00 and F1-Score of 0.923. © 2022, CC BY.

关键词： Classification (of information)

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Role of Contemporary Techniques in Agriculture Development: A Systematic Review 2

Role of Contemporary Techniques in Agriculture Development: ...

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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Popli, Renu Singh, Daljeet Kumar, Rajeev Saini, Gurpreet Singh Garg, Kanwal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Center for Space Research DRD Lovely Professional University Phagwara India School of Electronics and Electrical Engineering Lovely Professional University Phagwara India Kurukshetra University Department of Computer Science and Application Kurukshetra India

ISBN: (纸本)9781665437899

Gone are the days when agriculture was said to be a profession of uneducated people who used only basic mechanical tools to survive. Use of technology in agriculture has transformed it into a high-tech, smart and efficient business. The abrupt increase in population has pushed the efficiency of agriculture activities to achieve higher and better yield from given set of resources. In this paper, a comprehensive survey on the role of contemporary techniques used in agricultural development is presented. Various aspects of smart agriculture involving the use of Wireless Sensor Networks (WSNs) to gather information, big data analysis for processing the collected information, Internet of Things (IOT) framework for communication, image processing techniques and mobile phone application developed for agriculture are discussed. Landmark studies published in each of these related fields are also presented. © 2022 IEEE.

关键词： Wireless sensor networks

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Revolutionizing Plant Disease Management Through Image Processing technology

Revolutionizing Plant Disease Management Through Image Proce...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Gaurav Kumar Ankit Kumar Manoj Singhal Kamred Udham Singh Lalan Kumar Teekam Singh Department of Computer Engineering & Applications GLA University Mathura UP India Department of Information Technology GL Bajaj Institute of Technology and Management India School of Computing Graphic Era Hill University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

Agricultural productivity plays a vital role in the economy, and disease detection in plants is crucial for the agriculture sector as plant diseases are widespread. Failure to take appropriate care can lead to serious consequences for plant quality, quantity, or productivity. For instance, a disease called little leaf disease has been found in pine trees in the United States, which can severely affect plant health. Automated methods for detecting plant diseases are useful as they reduce the need for manual inspection in large crop fields, and they can identify disease symptoms as soon as they appear on plant leaves. This paper presents an algorithm for image segmentation for the automatic identification and classification of plant leaf diseases. It also discusses various disease classification methods that can be used for plant leaf disease detection. Genetic algorithms are used for image segmentation, which is a crucial aspect of disease detection in plant leaves. The paper focuses on identifying the Powdery Mildew disease in organic grape products, which can cause significant damage if not detected in time. The accurate identification and classification of diseases from leaf images are crucial for preventing significant losses in output and agricultural inputs. The proposed process can help improve the accuracy and efficiency of disease detection in plants and lead to better agricultural productivity.

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