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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ali, Asfak Pandit, Arya Mandal, Shirshendu Bhattacharjee, Srinjan Maiti, Sauptik Acharjee, Suvojit Sarkar, Ram Chaudhuri, Sheli Sinha Agaian, Sos S. Djemal, Khalifa Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Department of Electronics and Communication Narula Institute of Technology Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Computer Science Department Graduate Center College of Staten Island City University of New York New York India University of Evry Paris-Saclay IBISC Laboratory 40 rue du Pelvoux Evry91020 France

ISBN: (纸本)9783031801389

Tracking objects in nighttime video presents significant challenges due to factors like low visibility, poor illumination, and the presence of distant, small, or low-contrast objects. Traditional tracking methods, which rely on frame differencing, are largely ineffective under these conditions. Although deep learning has advanced the field, it demands extensive labeled data, which is often difficult to obtain for rare or extreme events. Historically, research in spatial and temporal data models and database systems has been conducted independently, limiting progress in handling complex spatiotemporal scenarios. Nighttime video analysis involves complex, dynamic, and context-dependent spatiotemporal events, requiring sophisticated algorithms capable of accurately monitoring and detecting these events across both spatial and temporal dimensions. Such advancements are crucial for enhancing applications in surveillance, security, and autonomous navigation systems. To address the gap in specialized datasets, we introduce the Night Vision Spatiotemporal Infrared-Video Dataset (NV-SID). This dataset comprises 100 annotated nighttime infrared videos, setting a new benchmark for evaluating deep learning-based object-tracking algorithms. Furthermore, the target of this challenge was to set a new standard for evaluating deep learning-based object-tracking algorithms, utilizing the proposed Qualitative Precision (QP) metric. NV-SID is a key component of the VISTAC Challenge, part of the ICPR 2024 Challenge on VISual Tracking in Adverse Conditions, which aims to push the boundaries of nighttime object tracking technology. Here, we have briefly described the methods used by the participants, and compiled their results. We have also mentioned further research avenues regarding this task. Challenge details and dataset can be found at: https://***/view/ju-nvisot/home. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Predicting Dota 2 Game Period Winners Through Hero Analysis 12

Predicting Dota 2 Game Period Winners Through Hero Analysis

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12th International Electrical engineering Congress, iEECON 2024

作者： Musika, Chanwit Malathong, Kritthanit Arthayukti, Chananate Konpang, Jessada Jarernpon, Boonchuay Thitimahatthanakusol, Phatsakul Rajamangala University Of Technology Krungthep Faculty Of Science And Technology Department Of Computer Science 2 Nanglingee Rd. Sathorn Thungmahamek Bangkok10120 Thailand Rajamangala University Of Technology Krungthep Faculty Of Engineering Department Of Electrical And Telecommunication Engineering 2 Nanglingee Rd. Sathorn Thungmahamek Bangkok10120 Thailand Rajamangala University Of Technology Isan Faculty Of Engineering And Technology Department Of Telecommunication Engineering 744 Sura Narai Rd Nai-muang Mueang Nakhon Ratchasima Nakorn Ratchasima30000 Thailand

ISBN: (纸本)9798350383591

The impact of hero selection on game outcomes is well-recognized, with input from respected streamers and commentators who emphasize the significance of hero choices. These insights, supported by substantial followers and views, underscore the importance of astute hero selection. However, hero selection is not straightforward due to factors like player proficiency and team strategy. While extensive game data allows us to gauge hero performance, practical challenges exist. To address these, a method for real-time outcome prediction based on hero selection is introduced. This approach includes an interface for hero selection, displays sections for hero information, and identifies both winning and losing heroes. The method also calculates and displays the financial advantage in-game, a crucial factor, and adapts to evolving patches, ensuring its reliability and accessibility to players. © 2024 IEEE.

关键词： Forecasting

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A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification

A Comparison of Multi Class Support Vector Machine vs Deep C...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Cahyo, Nur Ryan Dwi Sari, Christy Atika Rachmawanto, Eko Hari Jatmoko, Cahaya Al-Jawry, Rabei Raad Ali Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Department of Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia Northern Technical University Department Computer Science Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Sciece DhiQar Iraq

ISBN: (纸本)9798350339215

Multi SVM has long been one of the popular methods in classification, while DCNN has recently gained significant attention in image processing and pattern recognition. This research evaluates the effectiveness of Multi Class Support Vector Machine (M-SVM) and Deep Convolutional Neural Network (DCNN) techniques in classifying brain tumors. A dataset of 2660 3D medical images with dimensions 227 x 227 x 3;including Glioma, Meningioma, and Pituitary tumors, has been partitioned into distinct sets for both training and testing purposes. DCNN approach achieves excellent accuracy in identifying tumor names, with a training accuracy of 97.8% and 100% success rate in 9 experiments. The Multi SVM method demonstrates relatively good accuracy, with training accuracies ranging from 70% to 90% based on different kernel functions. These findings provide valuable insights for selecting appropriate methods in brain tumor classification and encourage further exploration of hybrid Multi SVM-DCNN approaches to enhance accuracy and reliability. © 2023 IEEE.

关键词： Convolutional neural networks

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Deep Learning and Heuristic Optimization for Secure and Eficient Energy Management in Smart Communities

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computer Modeling in engineering & sciences 2025年第5期143卷 2027-2052页

作者： Murad Khan Mohammed Faisa Fahad R.Albogamy Muhammad Diyan Department of Computer Science and Engineering Kuwait College of Science and TechnologyDoha DistrictKuwait CityP.O.Box 35001Kuwait Computer Sciences Program partment of MathematicsTurabah University CllegeTaifUniversityO.Box99Taif2944 Saudi Arabia Department of Computing&Games Teesside UniversityMiddlesbroughTS13BXUK

The rapid advancements in distributed generation technologies,the widespread adoption of distributed energy resources,and the integration of 5G technology have spurred sharing economy businesses within the electricity *** technologies such as blockchain,5G connectivity,and Internet of Things(IoT)devices have facilitated peer-to-peer distribution and real-time response to fluctuations in supply and ***,sharing electricity within a smart community presents numerous challenges,including intricate design considerations,equitable allocation,and accurate forecasting due to the lack of well-organized temporal *** address these challenges,this proposed system is focused on sharing extra electricity within the smart *** working of the proposed system is composed of five main *** phase 1,we develop a model to forecast the energy consumption of the appliances using the Long Short-Term Memory(LSTM)integrated with the attention *** phase 2,based on the predicted energy consumption,we designed a smart scheduler with attention-induced Genetic Algorithm(GA)to schedule the appliances to reduce energy *** phase 3,a dynamic Feed-in Tariff(dFIT)algorithm makes real-time tariff adjustments using LSTM for demand prediction and SHapley Additive exPlanations(SHAP)values to improve model *** phase 4,the energy saved from solar systems and smart scheduling is shared with the community ***,in phase 5,SDP security ensures the integrity and confidentiality of shared energy *** evaluate the performance of energy sharing and scheduling for houses with and without solar support,we simulated the above phases using data obtained from the energy consumption of 17 household appliances in our IoT ***,the simulation results show that the proposed scheme reduces energy consumption and ensures secure and efficient distribution with peers,promoting a more sustainable energy management and res

关键词： Community-centric internet of things energy management micro-grids smart homes deep learning prediction security

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Improving Visual Recognition with Enhanced Convolutional Neural Networks 7

Improving Visual Recognition with Enhanced Convolutional Neu...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Patil, Yamini Madhav Chandhok, Gunita Arun Supreeth, B.R. Pundir, Sumit Kunisetti, Subramanyam Gayathri, S. Sinhgad College of Engineering Department of Electronics and Telecommunication Maharashtra Pune India Saveetha College of Liberal Arts and Sciences Simats Department of Commerce General Thandalam Tamil Nadu Chennai India Oxford Degree and Pg College Department of Computer Science Sir. M. Visveshwaraiah Layout Ullal Bangalore India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India R.V.R & J.C College of Engineering Department of Csbs Andhra Pradesh Guntur India Sathyabama Institute of Science and Technology Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350067

The study focuses on evaluating the performance of enhanced Convolutional Neural Network (CNN) architecture on the CIFAR-100 dataset for image recognition tasks. The proposed CNN architecture and all four models, namely ResNet50, DenseNet121, EfflcientNet-B3, and one, were trained and evaluated. The performance of the proposed CNN architecture is undeniably superior to that of the other models evaluated, with an accuracy of 99% and precision of 98% In terms of performance, DenseNet121, EfficientNet-B3, and ResNet50 all achieved commendable results, with an accuracy of 90% and precision of 91%. The effectiveness of the proposed convolutional neural network architecture in categorizing images in multiple dimensions demonstrates the potential for discrete visual recognition tasks. The results underscore the importance of custom CNN designs in real-world applications that require high precision and accuracy. The results of the paper offer valuable insights for future efforts to enhance visual recognition systems and push forward the field of computer vision. © 2024 IEEE.

关键词： Convolutional neural networks

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Identification of Sounds from Construction Sites: A Machine Learning Approach 4th

Identification of Sounds from Construction Sites: A Machin...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Maji, Sayantan Mukherjee, Himadri Dhar, Ankita Marciano, Matteo Roy, Kaushik Department of Computer Science and Engineering Sister Nivedita University Kolkata India TISA Lab Department of Computer Science West Bengal State University Kolkata India Gazelien Records Lab Department of Arts and Humanities Music Program New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (纸本)9789819757022

The rate at which the world is evolving is astonishing with cutting-edge technologies being introduced every day. There have been developments in every field ranging from constructing gigantic architectures to enhancing the ease and comfort with which we can commute daily. Nevertheless, this has given rise to different types of pollution, sound pollution being one of them. Although the impact of sound pollution is not instantaneously noticed, it has a distinct effect on humans and nature which is observed over a period of time. There are multiple sources that cause sound pollution. Sounds from ongoing construction sites are one of them. Such sounds disrupt the daily lifestyle of people, their health, and daily productivity. Mostly, these sounds start very early in the day thereby making it difficult for people to manually take precautions like shutting doors and windows or deploying mobile soundproofing solutions. Machine learning has advanced significantly over the years leading to the refinement of artificial intelligence. This has aided in the development of multifarious solutions to ease and simplify our daily lifestyle. This sets the stage for an autonomous system, that can detect sounds from construction sites very early to automatically deploy precautionary measures so that the effect of these sounds on close by residents can be minimized. In this paper, a system is presented that models audio clips using MFCC-based features for the identification of sounds from construction sites in the Urban environment. Experiments were performed on a dataset of over 6K clips composed of the varied construction sounds as well as ambient urban sounds and a maximum accuracy of 98.02% was achieved using random forest-based classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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AlexNet Architecture Based Convolution Neural Network for Realtime Audio to Text Translator of Bisindo Hand Sign

AlexNet Architecture Based Convolution Neural Network for Re...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Sujatmiko, Dhiki Sari, Christy Atika Rachmawanto, Eko Hari Krismawan, Andi Danang Altamer, Bilal R. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Animation Semarang Indonesia Mosul University Presidency University of Mosul Department of Computer Science Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Deafness is a condition that results in the loss of hearing function, hindering the reception of information such as oral communication that relies on auditory senses. Consequently, individuals with hearing impairment experience communication barriers and may have limited or no ability to respond. One solution is the use of sign language. In Indonesia, there are two known sign languages: Sibi and Bisindo. Both serve the same function but differ in their style of movement and expression. Bisindo is considered more flexible as it conveys meaning based on the Indonesian language. However, the universal understanding of this language solution is still limited among many people. Therefore, a program is needed to facilitate translation between deaf individuals who use sign language and their counterparts who do not communicate through sign language. CNN (Convolutional Neural Network) is a deep learning algorithm used for training visual input data recognition by computer systems. There are various CNN-based architectures, and one of them is AlexNet. Based on the author's testing, the AlexNet architecture proves to be suitable for real-time sign language translation. The evaluation of the system involved 7,800 datasets and 520 testing instances, with an average accuracy of 468 correct translations. When averaged, the system achieved a 90% accuracy rate, representing a 100% increase in accuracy compared to previous approaches. © 2023 IEEE.

关键词： Audition

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Toward Learning Factory for Industry 4.0: Virtual Reality (VR) for Learning Human–Robot Collaboration 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Colombo, Giorgio Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Industry 4.0 will bring not only transformation to the manufacturing technologies but also to the profile of the workforce. Education system should be revised to prepare the future graduates embracing the knowledge of the ongoing revolution. Initiatives on the modification of curriculum and tools to deliver the concepts of Industry 4.0 must be taken. This paper examines limitations of current engineering tools and proposes a virtual reality (VR)-based learning platform to support the teaching and learning activity for Industry 4.0, focusing on the design of Human–robot Collaboration (HRC). The development process began with the identification of the general Intended Learning Outcomes (ILOs) which were extracted from the review of the HRC design issues, followed by the evaluation of the current teaching tool. From these results, the new requirements of the tools to achieve ILOs are redefined. The implementation of the system is demonstrated to show the feasibility of the proposed learning platform. This workflow can serve as initial development of another learning platform for the other innovative concepts that form the building blocks of Industry 4.0. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Industry 4.0

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A Secure Cloud-Based Infrastructure for Virtual Sensors in IoT Environments 6

A Secure Cloud-Based Infrastructure for Virtual Sensors in I...

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6th International Conference of Women in Data science at Prince Sultan University, WiDS-PSU 2023

作者： Makda, Tahira Jehan Luiza Barros, Ana Dilek, Selma University of Doha for Science and Technology College of Business Management Doha Qatar University of Ceará Computer Science Program State Fortaleza Brazil Hacettepe University Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665477239

Recent work has proven the effort of researchers to integrate small sensors and a cloud environment, delivering the Internet of Things (IoT). Sensors as a service are one of the leading research concerns in this context. Nevertheless, security is becoming one of the most significant attributes of the IoT as sensors become more human-independent and are being extensively used to monitor human lives. That way, IoT brings many key security challenges that need attention, some of which we address in this position paper. We present a cloud-based infrastructure that can deliver sensors and actuators as a service, providing secure communication between them and the control nodes on which IoT applications rely while implementing Big Data algorithms. For mapping our proposal, two scenarios related to Health Assistance are discussed, considering secure communications in a sensor network. In conclusion, we propose a scope for future research in this field considering digital twin concepts. Since domains exploiting IoT technologies can benefit from adopting Digital Twin, our goal is to evolve a virtual sensor and actuator system into this technology. © 2023 IEEE.

关键词： Actuators

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News-Driven Price Movement Forecasting with Label-Prior Graph Attention 24

News-Driven Price Movement Forecasting with Label-Prior Grap...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Liou, Yi-Ting Chen, Chung-Chi Huang, Hen-Hsen Chen, Hsin-Hsi Graduate Program of Data Science National Taiwan University Academia Sinica Taiwan AIST Japan Institute of Information Science Academia Sinica Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9798400701726

This paper introduces a novel approach to stock movement prediction using multi-label classification, leveraging the interconnections between news articles and related company stocks. We present the Label-Prior Graph Attention (LPGA) model, which significantly enhances the performance of news-driven stock price movement forecasting. This model is comprised of a unique graph attention architecture, incorporating a label encoder and a text encoder, designed to effectively capture and utilize the relationships between labels in a graph-based context. Our model demonstrates superior performance over several benchmark models. The LPGA model’s efficacy is further validated through experiments on two multilabel datasets. The model outperforms established baseline models across various evaluation metrics. The success of the LPGA model in both stock movement prediction and general multi-label classification tasks indicates its potential as a versatile tool in the realm of machine learning and financial analysis. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graphic methods

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