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23rd International Conference on New Interfaces for Musical Expression, NIME 2023

作者： Díaz-Durán, Joaquín R. Vidal, Laia Turmo Tajadura-Jiménez, Ana DEI Interactive Systems Group Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain

Joakinator is a wearable interactive interface that allows users to activate different media materials, such as sound, music, and video, through body gestures. The device, designed in the context of music and performing arts, integrates surface electromyogram, force sensors, and machine learning algorithms with tailored-made software for sonifying muscle-tone and force. This allows the body to reflect expressively the content of the media and the architecture of the device. Recently, we have started to investigate the potential of Joakinator to alter body perception in the context of BODYinTRANSIT, a European Research Council Project focused on the transformations of body perception through the use of interactive sound/haptics technology. At NIME-2023, we will showcase Joakinator and invite visitors to experience the device firsthand. Visitors will have the opportunity to try on the device, observe others using it, and reflect on its capabilities to transform body movement and perception through the sonification of muscle-tone and force. Overall, Joakinator is a technology that pushes the boundaries of bodycomputer interaction and opens new possibilities for human-computer interaction and expression. © 2023, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Machine Perception

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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Plant Disease Diagnosis Using Transfer Learning Based Models

Plant Disease Diagnosis Using Transfer Learning Based Models

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Anand, Vatsala Gupta, Sheifali Gupta, Rupesh Alshahrani, Hani Hamdi, Mohammed Al Reshan, Mana Saleh Shaikh, Asadullah Chitkara University Institute of Engineering and Technology Chitkara University Punjab India College of Computer Science and Information Systems Najran University Department of Computer Science Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran University Department of Information Symens Najran61441 Saudi Arabia

ISBN: (纸本)9798350327595

Plant disease diagnosis is the process of identifying and characterizing diseases that affect plants. The primary goal of plant disease diagnosis is to determine the cause of the disease, and to develop appropriate control and management strategies to minimize its impact on crop yields and quality. In this research, four transfer learning models such as AlexNet, VGG16, MobileNetV2, and InceptionV3 has been used for the classification of plant disease into different categories. Out of all the four transfer learning models, the best performing model is InceptionV3 with the value of accuracy as 0.92 and precision as 0.84. © 2023 IEEE.

关键词： Convolutional neural networks Crop diseases Diagnosis Plant disease detection

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Refinery: Graph Solver as a Service: Refinement-based Generation and Analysis of Consistent Models 24

Refinery: Graph Solver as a Service: Refinement-based Genera...

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46th International Conference on Software engineering: Companion, ICSE-Companion 2024

作者： Marussy, Kristóf Ficsor, Attila Semeráth, Oszkár Varró, Dániel Budapest University of Technology and Economics Department of Measurement and Information Systems Budapest Hungary Linköping University Department of Computer and Information Science Linköping Sweden McGill University Department of Electrical and Computer Engineering Montreal Canada

ISBN: (纸本)9798400705021

Various software and systems engineering scenarios rely on the systematic construction of consistent graph models. However, automatically generating a diverse set of consistent graph models for complex domain specifications is challenging. First, the graph generation problem must be specified with mathematical precision. Moreover, graph generation is a computationally complex task, which necessitates specialized logic solvers. Refinery is a novel open-source software framework to automatically synthesize a diverse set of consistent domain-specific graph models. The framework offers an expressive high-level specification language using partial models to succinctly formulate a wide range of graph generation challenges. Moreover, it provides a modern cloud-based architecture for a scalable graph solver as a service, which uses logic reasoning rules to efficiently synthesize a diverse set of solutions to graph generation problems by partial model refinement. Applications include system-level architecture synthesis, test generation for modeling tools or traffic scenario synthesis for autonomous vehicles. Video demonstration: https://***/Qy-3udNsWsM. Continuously deployed at: https://***/. © 2024 IEEE computer Society. All rights reserved.

关键词： Open source software

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Real-time Gesture Recognition for Sign Language Communication with CNN

Real-time Gesture Recognition for Sign Language Communicatio...

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2024 International Conference on Distributed systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Pandiyan, K. Kamal Raj, T. Monikarchana, Y. Jayashree, A. Judith Varshini, J.C. Shenbaga Valli, S. Srm Arts and Science College Kattankulathur Department of Cs with Ai Chennai India RajaRajeswari College of Engineering Department of Cse Banglore India Mohan Babu University Department of Mathematics India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala EngineeringCollege Avadi Department of Computer Science and Business Systems Chennai India Aurobindo Pre University College Department of Computer Science Bangalore India J.P. College of Engineering Department of Computer Science and Engineering Tenkasi India

ISBN: (纸本)9798350375442

Communicating with hearing-impaired people is difficult. Meeting society's requirements requires developing systems that can recognize indications and notify people. Deaf-mute people now have better support thanks to sign language. Sign language-using hearing-impaired persons can enhance their communication and social skills with real-time gesture detection devices. The author presents a full CNN-based, real-time sign language recognition approach in this research. This system includes data collecting, preprocessing, architectural design, training, and real-time deployment. CNNs are trained on a vast and diverse sign language motion dataset. To increase model generalizability across signals and contexts, data preparation generally includes normalization, scaling, and augmentation. For optimal picture recognition, the CNN architecture is thoroughly trained and verified. A real-time video processing pipeline captures and prepares frames for CNN-based recognition. The model uses quantization and hardware acceleration for real-time performance to enhance efficiency. User-friendly interfaces make the system accessible to hearing-impaired people. Testing and assessment have shown the system's accuracy and resilience, permitting its actual deployment. Continuous improvement involves monitoring system performance and altering the model to fix issues and improve usability. © 2024 IEEE.

关键词： Gesture recognition

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Detection of Salient Objects in a Video using a Hybrid Neural Network Model 3

Detection of Salient Objects in a Video using a Hybrid Neura...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Indirani, M. Anitha, Cuddapah Goswami, Sohan Baranitharan, K. Govindaraju, S. Mahaveerakannan, R. Hindusthan College of Engineering and Technology Department of It Coimbatore India Mohan Babu University Sree Vidyaniketan Engineering College Department of Computer Science and Systems Engineering Tirupati India Technique Polytechnic Institute Sugandhya Department of Computer Science and Technology West Bengal India Vsb Engineering College Cse Department India Sri Ramakrishna College of Arts and Science Department of Cs Coimbatore India Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462167

Salient detection is an active and critical area that is designed within the detection of items of a video recording, nonetheless, it attracts elevated interest among scientists. With rising powerful video clip information, the overall performance of saliency item detection techniques is degrading with typical item detection techniques. The problems lie with blurry moving goals, super-fast motion of items as well as dynamic background or background occlusion alteration on foreground areas within the video clip frames. This kind of obstacle leads to bad saliency detection. This paper models a full mastering design to deal with the difficulties, and that works on an advanced framework by merging the thought of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) with firefly Optimization technique for video clip saliency detection. Good utilization of the firefly algorithm together with CRNN is completed for the removal of characteristics by the video clips for item recognition. The primary objective of this newspaper is to present an effective hyperparameter choice framework for Convolution Recurrent Neural Networks (CRNNs) that employ one of the more popular swarm intelligence methods, the firefly algorithm. The suggested technique goals at creating a spatiotemporal design that exploits temporal, local, and spatial restriction cues to attain worldwide SEO. The process of locating the salient items in deep benchmark powerful video recording datasets will be completed by recording the temporal, local, and spatial restriction characteristics with all the CRNN. The CRNN is examined on benchmark datasets from typical video clip salient item detection techniques within the terminology of accuracy and load of Computation. The tests show that the proposed design accomplishes enhanced overall performance compared to some other existing versions which prove to significantly satisfy all the traditional object detection models. © 2023 IEEE.

关键词： Convolution

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Analyzing the Attitudes of Consumers to Electric Vehicles Using Bayesian Networks 23rd

Analyzing the Attitudes of Consumers to Electric Vehicles Us...

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23rd International Conference on Computational science, ICCS 2023

作者： Bienias, Karolina Ramsey, David Department of Operations Research and Business Intelligence Wroclaw University of Science and Technology Wroclaw Poland Department of Computer Science and Systems Engineering Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031360268

Road transport, as ‘a producer’ of carbon dioxide (CO2), causes high levels of air pollution, especially in cities. A suggested solution to this situation is the effective diffusion of electric vehicles (EVs). Regulations in the European Union aim to encourage consumers to buy electric cars. In addition, car manufacturers are constantly expanding their range of hybrid vehicles (HEVs) and EVs. Nonetheless, consumers have still many doubts regarding adopting an EV. Our survey among social media users investigates the attitudes and readiness of consumers to adopt HEVs and EVs. To investigate the factors underlying consumers’ attitudes to such vehicles, Bayesian networks were used as an exploratory tool. This paper presents results of this analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Bayesian network consumers electric vehicle hybrid electric vehicle innovation diffusion survey willingness to pay

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Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

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computers, Materials & Continua 2022年第9期72卷 5939-5954页

作者： Shaher Alshammrei Sahbi Boubaker Lioua Kolsi Department of Mechanical Engineering College of EngineeringUniversity of Ha’ilHa’il2440Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Laboratory of Metrology and Energy Systems National Engineering SchoolEnergy Engineering DepartmentUniversity of MonastirMonastir5000Tunisia

Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and *** this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra *** achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and ***,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target *** its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following *** this aim,the MR was equipped with an ultrasonic sensor used as obstacle *** an obstacle is found,the MR updates its diagraph by excluding the corresponding ***,Dijkstra algorithm runs on the modified *** procedure is repeated until reaching the target *** verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 *** obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking *** study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.

关键词： Mobile robot(MR) STEAM path planning obstacle avoidance improved dijkstra algorithm

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Optimal Resource Allocation for NOMA Wireless Networks

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computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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