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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Yulita, Intan Nurma Mutyara, Muhammad Ariq Farhansyah Suryana, Ino Ardiansyah, Firman Ramdhani, Muhammad Rasyid Wicaksono, Mokhamad Arfan Universitas Padjadjaran Department of Computer Science Sumedang Indonesia Akademi Digital Bandung Applied Bachelor in Digital Business Bandung Indonesia Universitas Padjadjaran Department of Electrical Engineering Sumedang Indonesia Akademi Digital Bandung Bachelor of Software Engineering Bandung Indonesia

ISBN: (纸本)9798331511470

Music genre classification is a prominent challenge in music information retrieval research. Most techniques for music genre classification employ machine learning algorithms to classify feature vectors extracted from sound recording segments. Several datasets have been utilized in experiments to evaluate classification accuracy in this domain. However, the datasets commonly used are relatively small, and much of the research on music genre classification continues to focus on these limited datasets. This research utilizes data from three main sources: (1) metadata derived from the Million Song Dataset, (2) the CD2C tagtraum genre annotations dataset as ground truth for genre labels, and (3) the Million Song Dataset mapping archive, which provides metadata mapping to SpotifyAPI, along with (4) acoustic features extracted via SpotifyAPI. The resulting dataset comprises 64,088 music tracks spanning five popular genres: Rock, Electronic, Jazz, Pop, and Rap. The objective of this research is to develop a model capable of predicting music genres based on acoustic features extracted through SpotifyAPI. To achieve this, a Convolutional Neural Network (CNN) architecture was implemented, with a focus on hyperparameter optimization, particularly in terms of the number of layers and the type of weight initialization. The optimal hyperparameters were identified as a two-layer CNN architecture utilizing Xavier Normal Initialization for weight initialization. The resulting model achieved a weighted F1-score of 0.7030 during testing. © 2024 IEEE.

关键词： Convolutional neural networks

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification 1

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Vinod Jain, Isha Dongla, Durga Sritha Rai, Shivangi Rastogi, Vani Kaushik, Eva Kaushik, Rohit George Washington University School of Engineering and Applied Science WashingtonDC United States Dr. Akhilesh Das Gupta Institute of Technology and Management Computer Science and Engineering Department New Delhi India Gandhi Institute of Technology and Management Computer Science and Engineering Department Andhra Pradesh Visakhapatnam India Data Scientist DXC Technology Noida India Data Analytics Program University of Illinois CS and Mathematics Department Springfield United States

ISBN: (纸本)9798350356816

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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What Can We Learn from On-Demand Transit Services for Ridership? A Case Study at the City of Regina, Canada

What Can We Learn from On-Demand Transit Services for Riders...

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Proceedings of the Annual Conference of the Canadian Society of Civil engineering 2022

作者： Tang, Yili Abdullah, Duha Adewuyi, Adesola Luhning, Nathan Bhalla, Satinder Faculty of Engineering and Applied Science University of Regina Regina Canada Department of Civil and Environmental Engineering University of Western Ontario London Canada Department of Electrical and Computer Engineering University of Western Ontario London Canada Transit and Fleet Department City of Regina Regina Canada TransSIGHT LLC California United States

ISBN: (纸本)9783031340260

The urgent need to optimize operational efficiency, boost ridership, enlarge effective communication technologies, and improve customer convenience has led to the emergence of on-demand transit (ODT) services. ODT can be advantageous in several ways, including reliability, improving mobility, cost-effectiveness, and reducing the need for multi-transit services. This paper analyzed the trip patterns of on-demand services and the impacts on ridership by the difference-in-difference method. The pattern analyses showed that the origin–destination flow patterns are concentrated in large commercial and dense residential areas with significantly reduced travel times including the waiting times and in-vehicle times. Furthermore, the difference-in-difference model analyses yielded positive relations between the ridership and the on-demand transit services for the overall transit network while the effects vary with specific landuse zones. Results indicated the potential of on-demand transit services to benefit passengers and ridership recovery during the pandemic and post-pandemic. © Canadian Society for Civil engineering 2024.

关键词： Travel time

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AHHO: Arithmetic Harris Hawks Optimization algorithm for demand side management in smart grids

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Discover Internet of Things 2023年第1期3卷 3页

作者： Manzoor, Awais Judge, Malik Ali Islam, Saif ul Neggaz, Nabil Abulaigh, Laith Ahmad, Ishfaq Department of Computer Science COMSATS University Islamabad Islamabad44550 Pakistan Department of Computer Sciences Institute of Space Technology Islamabad Pakistan Faculty of Mathematics and Computers University of Sciences and Technologies-USTO-MB Oran Algeria Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Department of Computer Science and Engineering The University of Texas at Arlington Arlington United States

Energy management strategies are crucial to efficiently scheduling appliances and preventing peak generation due to increased energy demand. It is essential to manage the demand and supply of energy based on the consumer’s consumption patterns using various heuristic optimization techniques. Additionally, the end-user is more concerned with minimizing electricity costs and reducing peak-to-average ratios (PARs). This work proposes an arithmetic Harris hawks optimization (AHHO) as a new approach for improving the Harris hawks algorithm to optimize residential demand response (DR) load management in a smart grid. Our method employs arithmetic and lightweight flight operators based on the Lévy flight distribution to generate diverse design solutions and improve the HHO’s exploration capabilities. We consider 15 smart appliances and categorize them based on how much energy they use, computing the electricity price using real-time pricing (RTP) and critical peak pricing (CPP). While maintaining user satisfaction within operational and power limits, the objective is to decrease energy costs and PAR. We evaluate the effectiveness of our proposed AHHO approach against nine cutting-edge algorithms using both RTP and CPP schemes. The findings demonstrate that our suggested approach performs better than the other algorithms because it achieved cost savings of 42.10% and 30% under RTP and CPP schemes, respectively. Meanwhile, it also reduced PAR by 55.17% and 50% under RTP and CPP schemes, respectively. © The Author(s) 2023.

关键词： Optimization

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Enhanced accuracy of deep learning method for fruit images classification 25

Enhanced accuracy of deep learning method for fruit images c...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Arabiat, Mohammad Abuowaida, Suhaila Alshdaifat, Nawaf Aburomman, Ahmad Al Henawi, Esraa Dmour, Mohmmad Elrashidi, Ali Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Al al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan Applied Science Private University Faculty of Information Technology Amman Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

In the last few years, artificial intelligence has shown its importance and ability in computer vision due to its high level of accuracy and wide acceptance. The main goal of the research is to look into a better way to separate different types of fruits. Although deep learning is used, the standard classification methods for different types of fruit don't work very well. The main steps of the recommended better multiple fruit classification method are: 1) Making changes to the filter's size. Selecting the most suitable number of repeats is part of the process of improving the ResNet-101 block. 3) It is proposed that there be one more convolutional layer. Exists support for the suggested method after collecting and labeling 27,000 pictures as part of an actual study. The dataset with many fruits is split into two parts: 80% is used for training and 20% is used for testing. These pictures were used to train an application based on deep learning (DL) to help successfully determine multiple fruit objects in images. Then and then possible to compare the results of this method to the most current methods in the area of object division. The application being studied at does better than expected in terms of accuracy AP in a range of levels. The values always go farther than the equivalent measures of three widely used methods. Compared to the other methods, the suggested one takes less time to train. There are higher numbers for the thresholds than there are for the state of the art method's thresholds © 2024 IEEE.

关键词： Deep reinforcement learning

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Two Industrial Reference Demonstrators for High Throughput and Low Latency in 5G-Standalone (SA) Network Setups 27

Two Industrial Reference Demonstrators for High Throughput a...

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27. VDE ITG-Fachtagung Mobilkommunikation: Technologien und Anwendungen - 27th VDE ITG Symposium on Mobile Communication: Technologies and Applications

作者： John, Fabian Sievers, Björn Sellhorn, Ole Hendrik Hellbrück, Horst University of Applied Sciences Lübeck - Technische Hochschule Department of Electrical Engineering and Computer Science Center of Excellence CoSA Lübeck Germany

ISBN: (纸本)9783800760947

The fifth generation of mobile communications (5G) is known as a new key technology for industry. Industry and research institutes are enabled to operate their private 5G Campus networks with commercially available hardware and open-source implementations. Due to the higher costs for a 5G deployment compared to other wireless network systems, industrial network expansion is slow. With the demonstration of industrial use cases in the 5G system shows its advantages, and it will help the deployment of 5G campus networks. To demonstrate the advantages of high data throughput and low latency in 5G networks, we propose two wireless demonstrators as software applications that visualize and evaluate the key parameters in the industrial context. The first demonstrator implements the streaming of uncompressed high-resolution images and shows the importance of high data rates. The second demonstrator visualizes the importance of low latency for real-time applications with a control loop of an inverted pendulum. The advantage of the approach is that there is no need for physical hardware, and the solution is adaptable to all wireless technologies. For evaluation, we present measurement results with our demonstrators in a real 5G testbed deployment. © 2023 Mobilkommunikation: Technologien und Anwendungen - 27. VDE ITG-Fachtagung. All rights reserved.

关键词： 5G mobile communication systems

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An Isolated Three-Phase AC-AC Converter With Bipolar Output Voltage

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IEEE Transactions on Industrial Electronics 2024年第9期71卷 10050-10063页

作者： Ahmed, Hafiz Furqan Khan, Ashraf Ali Akbar, Fazal Islam, Shirazul Elkhateb, Ahmad National Sun Yat-sen University Department of Electrical Engineering Kaohsiung804 Taiwan Memorial University of Newfoundland Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada University of Edinburgh EdinburghEH9 3JL United Kingdom Qatar University Department of Electrical Engineering Doha2713 Qatar BelfastBT9 5AH United Kingdom

This article proposes a new topology of a three-phase high-frequency transformer (HFT) isolated bipolar ac-ac converter. It has a simple circuit topology consisting of nine active switches, three HFTs with primary and secondary series capacitors, and output LC filters. It can generate both noninverting and inverting outputs with a symmetric bipolar voltage gain of pm nd, where n is the turns ratio of the transformer and d is the switch duty cycle. The proposed converter can provide step-down output by setting the turns ratio to unity;however, step-up output operation can also be achieved by increasing n beyond one when required by a particular application. The proposed converter integrates HFT-based isolation and, therefore, can eliminate the sizeable low-frequency transformer for applications requiring grid interface. Owing to the use of series-blocking capacitors, the HFT is devoid of any line-frequency component. A safe-commutation-based switch modulation strategy is developed to avoid switch current and voltage overshoots. The proposed converter is well suitable for application as a three-phase dynamic voltage restorer. The inverting and noninverting operations can inject the antiphase and same-phase series voltages to mitigate grid voltage swell and sag, respectively. Moreover, the additional line-frequency isolation and voltage injection transformers can be saved. In-depth circuit analysis and operation are provided with proposed switch modulation strategies together with component design guidelines. Finally, the hardware measurements are taken on a laboratory-built circuit to confirm the operation of the proposed converter. © 1982-2012 IEEE.

关键词： Pulse width modulation

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Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

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computers, Materials & Continua 2022年第8期72卷 2429-2444页

作者： Thavavel Vaiyapuri K.Priyadarshini A.Hemlathadhevi M.Dhamodaran Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Electronics and Communication Engineering M.Kumarasamy College of EngineeringKarur639113India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Due to global financial crisis,risk management has received significant attention to avoid loss and maximize profit in any *** the financial crisis prediction(FCP)process is mainly based on data driven decision making and intelligent models,artificial intelligence(AI)and machine learning(ML)models are widely *** article introduces an intelligent feature selection with deep learning based financial risk assessment model(IFSDL-FRA).The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or *** addition,the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection(WSOA-FS)manner to an optimum selection of feature ***,Deep Random Vector Functional Link network(DRVFLN)classification technique was applied to properly allot the class labels to the financial ***,improved fruit fly optimization algorithm(IFFOA)based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN *** enhancing the better performance of the IFSDL-FRA technique,an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.

关键词： Financial risks intelligent models financial crisis prediction deep learning feature selection metaheuristics

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Analysis of Nonlinear Processes in the P-I-N Diodes Plasma by the Perturbation Theory Methods 13

Analysis of Nonlinear Processes in the P-I-N Diodes Plasma b...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Bomba, Andrii Moroz, Igor National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine Rivne State University of Humanities Department of Information Technologies and Modeling Rivne Ukraine

A nonlinear singularly perturbed boundary value problem of the electron-hole plasma state predicting in the p-i-n diodes active region is considered. The search for solutions to the problem is carried out by the asymptotic method of boundary corrections. The decomposition of the original problem is carried out, the results are analyzed, and the existence of its asymptotic solutions is proved. © 2023 IEEE.

关键词： Plasma diodes

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Deploying YOLOV10 for Affordable Real-Time Handgun Detection 22

Deploying YOLOV10 for Affordable Real-Time Handgun Detection

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Zigulic, Nikola Glucina, Matko Frank, Domagoj Lorencin, Ivan Sverko, Zoran Matika, Dario University of Rijeka Faculty of Engineering Rijeka Croatia Istrian University of Applied Sciences Department of Engineering Pula Croatia University North Department of Computer Science and Informatics Koprivnica Croatia Juraj Dobrila University of Pula Faculty of Informatics Pula Croatia University North Department of Electrical engineering Varaždin Croatia

ISBN: (纸本)9798350385601

The development of artificial intelligence is one of the most significant technological innovations that contributes to humanity with its characteristics and facilitates, secures, and improves everyday life. However the challenge arises when the programmer-engineer learns the algorithm of artificial intelligence to perform the proposed task, that is, the challenge lies in the issue of available hardware resources. Artificial intelligence algorithms perform many human-impossible tasks, such as detection and counting, i.e. calculating the interrelationships of individual objects, segmentation of tumors and other malignant diseases, i.e. tissues, classification of specific states of classes of a scene, and many other similar technologies. This research paper examines the possibility of implementing the You Only Look Once algorithm of the tenth generation on certain devices such as an affordable Raspberry Pi, and will discuss the advantages and disadvantages of changes in detection parameters, i.e. inferences to the applied model. In addition, a mock-up of the device will be shown, which will serve to provide timely information about criminals and suspicious persons who possess firearms in different situations, such as normal weather conditions in a populated place or in shops where petty robberies are frequent. The testing will be done using recorded videos in real-time scenarios. Finally, real-time inference or detection in real-time will be tested and the actions that Raspberry will perform will be simulated. The results indicate that the optimal model achieved a precision of 0.938, recall of 9.863, mAP50 of 0.91, and mAP50-95 of 0.739. This was achieved using an image size of 640, IOU threshold of 0.7, confidence threshold of 0.6, and training for 600 iterations with the Stochastic Gradient Descent optimizer, without augmentations, and employing the ONNX inference format. © 2024 IEEE.

关键词： Stochastic systems

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