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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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SAR Ship Detection Based on Improved YOLOv7-Tiny 8

SAR Ship Detection Based on Improved YOLOv7-Tiny

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Liu, Yang Wang, Xiaoqing Anhui Post and Telecommunication College Department of Computer Hefei China University of Science and Technology of China Department of Electrical Engineering and Information Science Hefei China

ISBN: (纸本)9781665450515

Ship detection in Synthetic Aperture Radar (SAR) images is an important remote sensing technology. It plays an essential role in maritime monitoring and warfare. Traditional SAR ship detection methods suffer from the interference of complex backgrounds. The speckle noise and the special imaging system of SAR will deteriorate the performance of traditional methods in SAR image interpretation. Deep learning has recently been widely used in SAR image interpretation while achieving state-of-the-art performance. In this paper, a novel SAR ship detection method based on Improved You Only Look Once version 7-tiny (Improved YOLOv7-tiny) is proposed. Based on the YOLOv7-tiny network, we have made the following improvements. First, the coordinate attention is plugged into the backbone network to ensure the lightweight design. Second, the Spatial Pyramid Pooing (SPP) of the backbone network and the SIoU loss function are modified to improve performance. Eventually, the proposed method has achieved better detection performance than YOLOv7-tiny and YOLOv7 on the HRSID dataset. © 2022 IEEE.

关键词： Synthetic aperture radar

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A Performance Study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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