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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Harmann, René Sohlbach, Lukas Perez-Peña, Fernando Schmidt, Karsten Department of Computer Science and Engineering Frankfurt University of Applied Sciences Nibelungenplatz 1 Frankfurt60318 Germany Department of Automation Electronics and Computer Architecture and Networks School of Engineering University of Cadiz Avda. de la Universidad 10 Puerto Real11519 Spain

ISBN: (纸本)9783031494208

Exploring and understanding the functioning of the human brain is one of the greatest challenges for current research. Neuromorphic engineering tries to address this challenge by abstracting biological mechanisms and translating them into technology. Via the abstraction process and experiments with the resulting technical system, an attempt is made to obtain information about the biological counterpart. One subsection of Neuromorphic engineering (NE) is Spiking Neural Networks (SNN), which describe the structures of the human brain more and more closely than Artificial Neural Networks (ANN). Together with their dedicated hardware, SNNs provide a good platform for developing new algorithms for information processing. In the context of these neuromorphic hardware platforms, this paper aims to develop an interface for a digital hardware platform (SPINN-3 Development Board) to enable the use of industrial or conventional sensors and thus create new approaches for experimental research. The basis for this endeavor is a Field Programmable Gate Array (FPGA), which is placed as a gateway between the sensors and the neuromorphic hardware. Overall, the developed system provides a robust solution for a wide variety of investigations related to neuromorphic hardware and SNNs. Furthermore, the solution also offers suitable possibilities to monitor all processes within the system in order to obtain suitable measurements, which can be examined in search of meaningful results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neural networks

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Joint Optimization of Wireless Charging Station Location and Operation Scheduling for Electric Buses under Uncertain Demand

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IEEE Access 2025年 13卷 91765-91776页

作者： Li, Jiacheng Noto, Masato Zhang, Yang Guo, Jia Kanagawa University Department of Applied Systems and Mathematics Yokohama Japan Kanagawa University Department of Computer Science Yokohama Japan Hubei Key Laboratory of Digital Finance Innovation Hubei University of Economics Wuhan China Hosei University Graduate School of Computer and Information Sciences Tokyo Japan Hubei University of Economics Japan School of Information Engineering Hubei Wuhan430205 China

In the context of electric bus route operations, the introduction of wireless charging facilities significantly impacts the operational scheduling of bus fleets. This study focuses on a single bus route and proposes a joint optimization model for determining departure frequencies and charging station locations under uncertain demand conditions. The model aims to minimize both user travel costs and transit operation costs by jointly optimizing departure schedules across different time periods, electric bus assignments, and wireless charging station placement. An adaptive genetic algorithm (AGA) is developed to solve the model efficiently. A case study based on a real-world bus route in China demonstrates that the proposed strategy not only improves system performance but also yields tangible benefits—reducing the required number of electric buses by 29.4%, shortening departure intervals by 12.5%, and lowering daily operational costs by 10.5%. © 2013 IEEE.

关键词： Genetic algorithms

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Car Crash Detection System using Machine Learning and Deep Learning Algorithm

Car Crash Detection System using Machine Learning and Deep L...

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2022 IEEE International Conference on Data science and Information System, ICDSIS 2022

作者： Supriya, S. Shankar, Sahana P Himanshu Jain, J. Narayana, Lisha L Gumalla, Nikhita Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

Over 80% of mishaps are caused by a lack of identifying the accident on time, as well as failure to arrive in time to provide emergency care for the victim. The point is to distinguish and utilize machine learning to decide the best means of detecting car crash with light of the live transfer of dash cam data in the vehicle. The thought is to take every pixel and run it with a deep learning model prepared to recognize video outlines into mishap or non-mishap. Essentially, in Artificial Intelligence (AI), informational indexes are collected. Gathered information bases will be refined and given to AI calculations to prepare for image recognition with the help of computer vision. After fruitful preparation, AI calculations will be tried and the outcomes will be recorded for examination purposes. In the proposed vehicle crash location framework, the impact identification is performed utilizing the Convolutional Neural Network (CNN), taking a bunch of pictures as information, the framework distinguishes the crash, the effect of the vehicle and the greatness of the mishap. Based on the performance of machine learning algorithms, comparative analysis is performed and the results will be tabulated. Two machine learning algorithms are considered i.e. Random Forest Classifier and Logistic Regression which enables the output regarding the generated index from the CNN and running through the indices with location impact and severity of the damage. © 2022 IEEE.

关键词： Deep learning

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VGG Models in Image Captioning: Which Architecture Delivers Better Descriptions? 18

VGG Models in Image Captioning: Which Architecture Delivers ...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Maaz, Ahmad Abbas, Shaheer Ali, Raja Hashim Ahmed, Iftikhar Khan, Talha Ali University of Europe for Applied Sciences Department of Business Think Campus Potsdam14469 Germany GIK Institute of Engineering Sciences & Technology Faculty of Computer Science & Engineering Topi23460 Pakistan

ISBN: (纸本)9798331508692

The recent advancements and a flurry of deep learning architectures in the fields of computer vision and natural language processing have greatly benefited the task of creating natural language descriptions for images in recent years. These advancements have proved to be the turning point with popular practical applications in artificial intelligence, as well as for driving cross-disciplinary developments. However, not many studies have investigated the performance of different convolutional and LSTM architectures for image captioning, motivating our study to bridge this gap. This study compares the performance of VGG16 and VGG19 models on the Flickr8k dataset. Image features are extracted using pretrained VGG16 and VGG19 models, textual input is tokenized, and a unique Encoder-Decoder architecture is built. We determine the effect of training length on captioning proficiency by conducting comprehensive tests with varied epochs. The BLEU ratings reveal detailed insights into the models' language generating capabilities, demonstrating comparable performance across VGG16 and VGG19. Our work fills this gap by assessing the efficiency of several neural designs on a given dataset. The findings highlight the complexities of model convergence, and the need for different evaluation measures and defined benchmarks in the picture captioning sector. We determine the relationship between training epochs and model performance in this study, and provides useful observations for researchers and practitioners. We systematically examine the design of various convolutional neural networks, and provide a basis for future research in the ever-advancing world of image captioning. © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Timetable Generation using Neural Networks Trained by Genetic Algorithms 18

Automatic Timetable Generation using Neural Networks Trained...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Abbas, Shaheer Maaz, Ahmad Ali, Raja Hashim Khan, Talha Ali Ahmed, Iftikhar University of Europe for Applied Sciences Department of Business Think Campus Potsdam14469 Germany GIK Institute of Engineering Sciences & Technology Faculty of Computer Science & Engineering Topi23460 Pakistan

ISBN: (纸本)9798331508692

Scheduling is a crucial task in various real-life situations, including educational institutions where creating a feasible timetable is a complex challenge due to numerous constraints. The educational timetable scheduling problem is NP-hard, making it a difficult problem to solve optimally within reasonable time frames. Traditional methods like Genetic Algorithms (GAs) and Evolutionary Algorithms (EAs) have been applied to this problem with varying degrees of success. In this paper, we introduce a novel approach for automatic timetable generation using neural networks trained by genetic algorithms. Our method leverages the strengths of neural networks in pattern recognition and GAs in optimization to effectively handle the constraints and complexities of timetable scheduling. Experimental results demonstrate a significant improvement in scheduling accuracy and computational efficiency. We compare the performance of our proposed method with traditional GAs and demonstrate that the neural network approach trained by GAs achieves superior results in terms of speed and solution quality. This research paves the way for more efficient and intelligent scheduling systems in educational environments. © 2024 IEEE.

关键词： Genetic algorithms

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Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

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computers, Materials & Continua 2023年第5期75卷 3239-3255页

作者： Majdy M.Eltahir Adil Yousif Fadwa Alrowais Mohamed K.Nour Radwa Marzouk Hatim Dafaalla Asma Abbas Hassan Elnour Amira Sayed A.Aziz Manar Ahmed Hamza Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Arts and Science Najran UniversitySharourahSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Applied SciencesKing Khalid UniversityMuhayil63772Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event *** is especially applicable in the case of elderly or disabled people who live self-reliantly in their *** sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during *** is one of the most important problems confronted by older people and people with movement *** previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled ***,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor ***,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s *** this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)*** presented IWODL-FDDP model aims to identify the fall events to assist disabled *** presented IWODLFDDP model applies an image filtering approach to pre-process the ***,the EfficientNet-B0 model is utilized to generate valuable feature vector ***,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall ***,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the *** experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

关键词： Fall detection disabled people deep learning improved whale optimization assisted living

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Classification of Ventricular Arrhythmia using Machine Learning and Deep Learning Techniques 4

Classification of Ventricular Arrhythmia using Machine Learn...

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4th International Conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Supriya, M.S. Shankar, Sahana P Mohan Reddy, Sodum Sai Geethanjali, R. Sathvika, S. Sona Shetty, R. Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350397475

ECG is one of the most important and commonly used tools to detect any kind of abnormality in the heart. One such abnormality is arrhythmia, an irregular pattern which can lead to sudden cardiac arrest and even death. The number of deaths caused by cardiac arrest, which is a result of arrhythmia, has recently increased, and keeping this in view various models have been developed using machine learning and deep learning techniques and have worked on similar terms to provide better results in terms of accuracy. There are many forms of arrhythmias, and this work focuses on the methods used to classify one of the most dangerous forms of arrhythmias which is Ventricular Arrhythmia based on the ECG datasets collected from MIT-BIH Arrhythmia Database, MIT-BIH Supraventricular Arrhythmia Database, INCART 12-lead Database. The study presents insights into classification of ventricular arrhythmia using models such as Convolutional Neural Network (CNN), Dense Neural Network (DNN) and Long Short Term Memory (LSTM) and the way they are trained with beats and labels that are then used for classification. The study focuses on comparing the classification results of all the three techniques based on accuracy, AUC score, recall, prevalence, precision and specificity in order to conclude the best classifier among them in terms of various performance parameters so that it can be adapted for clinical use. © 2022 IEEE.

关键词： Long short-term memory

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Intelligent recognition system for viewpoint variations on gait and speech using CNN-CapsNet

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International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 363-382页

作者： G.Merlin Linda N.V.S.Sree Rathna Lakshmi N.Senthil Murugan Rajendra Prasad Mahapatra V.Muthukumaran M.Sivaram Department of Computer Science and Engineering SRM Institute of Science and Technology–Vadapalani CampusChennaiIndia Department of Electronics and Communication Engineering Agni College of TechnologyChennaiIndia School of Computer Science and Engineering VIT-AP UniversityAmaravatiIndia Department of Computer Science and Engineering SRM Institute of Science and TechnologyGhaziabadIndia Department of Mathematics School of Applied SciencesREVA UniversityBangaloreIndia Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam

Purpose-The paper aims to introduce an intelligent recognition system for viewpoint variations of gait and *** proposes a convolutional neural network-based capsule network(CNN-CapsNet)model and outlining the performance of the system in recognition of gait and speech *** proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech ***/methodology/approach-This proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNNand used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in *** proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of ***-This research work provides recognition of signal,biometric-based gait recognition and sound/speech *** evaluations are conducted on three aspects of scenarios,namely fixed-view,cross-view and multi-view *** main parameters for recognition of gait are speed,change in clothes,subjects walking with carrying object and intensity of *** limitations/implications-The proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor *** can also act as a pre-requisite tool to analyze,identify,detect and verify the malware *** implications-This research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor *** can also act as a pre-requisite tool to analyze,identify,detect and verify the malware ***/value-This proposed research work proves to be performing better for the recognition of gait and speech when

关键词： Intelligent system Gait recognition Convolutional neural network Deep learning Capsule network Viewpoint variations

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Proof of Activity Protocol for IoMT Data Security

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computer Systems science & engineering 2023年第1期44卷 339-350页

作者： R.Rajadevi K.Venkatachalam Mehedi Masud Mohammed A.AlZain Mohamed Abouhawwash Department of Information Technology Kongu Engineering CollegeErode638060TamilnaduIndia Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time ***,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical *** the use of sensors,IoMT applications are used in healthcare *** such applications,one of the most important factors is data security,given that its transmission over the network may cause *** data security in IoMT systems,blockchain is used due to its numerous blocks for secure data *** this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare *** main aim is to enhance the data security over the *** PoA protocol enhances high security of data from the literature *** replacing the malicious node from the block,the PoA can provide high security for medical data in the *** with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.

关键词： Blockchain IoMT malicious code detection security secure data management framework data management PoA

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Clustering the Various Categorical Data: An Exploration of Algorithms and Performance Analysis 4

Clustering the Various Categorical Data: An Exploration of A...

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4th International Conference for Emerging Technology, INCET 2023

作者： Kumar, Raman Pati, Peeta Basa Deepa, K. Yanan, Suresh Amrita School of Computing Department of Computer Science and Engineering India Amrita School of Engineering Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Bengaluru India University of Technology and Applied Sciences-Al Musannah Department of Engineering Oman

ISBN: (纸本)9798350335750

Clustering is a method of grouping data based on similarities, and is an unsupervised technique for discovering patterns in data. In this research paper, various clustering algorithms such as k-Means, DBSCAN, Spectral Clustering, Gaussian Mixture, and Agglomerative Clustering are compared and evaluated on Amazon Prime Video Movies and TV Shows, Netflix Movies and TV Shows, and Disney+ Movies and Tv Shows datasets. The results of the study indicate that the k-Means algorithm performed well in clustering the data for all datasets, with an overall high level of performance. Additionally, the study provides valuable insights into the genre distribution of the data, and highlights the advantages and limitations of each clustering algorithm. © 2023 IEEE.

关键词： K-means clustering

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