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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Morissa, Virnika Shefira Gina Setiadi, De Rosal Ignatius Moses Universitas Dian Nuswantoro Informatics Engineering Department Semarang50131 Indonesia Universitas Dian Nuswantoro Research Center for Quantum Computing and Materials Informatics Faculty of Computer Science Semarang50131 Indonesia

ISBN: (纸本)9798350366648

In the digital era, data communication security is one of the main challenges. This research proposes a new method for secure image communication, which is a mixed triple logistic map and BB84 quantum key distribution. This approach combines the strengths of classical and quantum cryptographic systems to improve security. BB84 quantum key distribution protocol for securely distributing encryption keys. The resulting key is then processed into a hash value using the SHA-256 algorithm. These hash values are converted into ASCII values, which are used to generate three key sequences using the modified logistic map. The three key sequences are combined to form a keystream used in image encryption. Several security metrics are used to evaluate encryption security, including entropy, the Chi-Square test, and pixel correlation. The analysis results show that the encrypted image has an entropy close to eight. In addition, all chi-square values smaller than 293 indicate a uniform distribution. The correlation of nearby pixels in the encrypted image is also very low, where all values are less than 0.01, which indicates the loss of local patterns in the encrypted image. These results demonstrate that the proposed method effectively secures image communications, providing high security and resistance to conventional and quantum cryptographic attacks. © 2024 IEEE.

关键词： Quantum cryptography

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Cascaded PFLANN Model for Intelligent Health informatics in Detection of Respiratory Diseases from Speech Using Bio-inspired Computation

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Journal of Artificial Intelligence and Technology 2024年第2期4卷 124-131页

作者： Jagannath Dayal Pradhan L.V.Narasimha Prasad Tusar Kanti Dash Manisha Guduri Ganapati Panda Electronics and Communications Engineering C V Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering Institute of Aeronautical EngineeringHyderabadIndia School of Computing and Informatics University of LouisianaLafayetteLAUSA

Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-inspired computation,and the Internet of Medical *** has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of ***-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising *** the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection ***,the two main requirements of this are higher accuracy and lower computational complexity and,in many cases,these two requirements do not correlate with each *** problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy.A cascaded perceptual functional link artificial neural network(PFLANN)is used to capture the nonlinearity in the data for better classification performance with low computational *** proposed model is being tested for multiple respiratory diseases,and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.

关键词： AI-ML ASR FLANN health informatics neural network PFLANN

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An Enhanced Grey Wolf Optimizer with Opposition, Mutation, and Local Search Strategy for Feature Selection 13

An Enhanced Grey Wolf Optimizer with Opposition, Mutation, a...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Sirmayanti Prastyo, Pulung Hendro Mahyati Politeknik Negeri Ujung Pandang Department of Electrical Engineering Makassar Indonesia Politeknik Negeri Ujung Pandang Department of Informatics and Computer Engineering Makassar Indonesia Politeknik Negeri Ujung Pandang Department of Chemical Engineering Makassar Indonesia

ISBN: (纸本)9798350368086

Feature selection is a pivotal process in machine learning and data analysis, aimed at improving model performance by identifying and utilizing the most relevant features while eliminating redundant and irrelevant ones. The Grey Wolf Optimizer (GWO), a metaheuristic algorithm based on the social hierarchy and hunting behavior of grey wolves, has proven to be an effective method for feature selection. However, it is also easy to get trapped in the local optima. Therefore, this study presents an enhanced Grey Wolf Optimizer with Opposition, Mutation, and Local Search Strategy (GWO-OMLSS) to boost exploration and exploitation of GWO in order to avoid the algorithm falling into local optima. The proposed method was validated using ten becnhmark data sets from the UCI machine learning repository and compared with state-of-the-art metaheuristic algorithm-based feature selections, such as GWO, PSO, ESSA, and CS. The Experimental results demonstrate that GWO-OMLSS outperforms the standard of GWO and other metaheuristic algorithms on all data sets except Ionosphere and Wine. The proposed method achieves the best an accuracy of 99.7% and 99.8% on the Exactly and M-of-n data sets, respectively, while also significantly reducing the number of features. It indicates that the proposed method not only obtains high accuracy, but also reduces the dimensionality of features effectively. It means that the proposed method can be considered as a promising algorithm for feature selection. © 2024 IEEE.

关键词： Heuristic algorithms

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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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Analyzing the impact and application of Augmented Reality in Education: The case of students with special educational needs 26

Analyzing the impact and application of Augmented Reality in...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Kapetanaki, Andrianthi Krouska, Akrivi Troussas, Christos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (纸本)9781450398541

This study presents a systematic review of the literature which analyzes the impact and application of Augmented Reality (AR) technology in the tutoring of students with special educational needs. In recent years, Information and Communication Technology (ICT) promotes learning in a pluralistic and multisensory environment that favors students with special educational needs. Augmented Reality is an emerging technology which has been widely used in the field of education, but only few studies are related to special education. This paper investigates the research in AR educational systems addressed to students with special educational needs. In total, 26 studies between 2014 and 2022 were selected and analyzed. Specifically, this systematic review examines the advantages and limitations of AR use in Special Education, the AR platforms and tools used in learning scenarios and the different types of students with special educational needs. © 2022 ACM.

关键词： Students

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WiCHORD: A Chord Protocol Application on P2P LoRa Wireless Sensor Networks 13

WiCHORD: A Chord Protocol Application on P2P LoRa Wireless S...

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13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

作者： Balatsouras, Christos-Panagiotis Karras, Aristeidis Karras, Christos Tsolis, Dimitrios Sioutas, Spyros Group Computer Engineering and Informatics Department Patras26504 Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

On the modern era of Internet of Things (IoT) and Industry 4.0 there is a growing need for reliable wireless long range communications. LoRa is an emerging technology for effective long range communications which can be directly applied to IoT applications. Wireless sensor networks (WSNs) are by far an efficient infrastructure where sensors act as nodes and exchange information among themselves. Distributed applications such as Peer-to-Peer (P2P) networks are inextricably linked with Distributed Hash Tables (DHTs) whereabouts DHTs offer effective and speedy data indexing. A Distributed Hash Table structure known as the Chord algorithm enables the lookup operation of nodes which is a major algorithmic function of P2P networks. In the context of this paper, the inner workings of Chord protocol are highlighted along with an introduced modified version of it for WSNs. Additionally, we adapt the proposed method on LoRa networks where sensors function as nodes. The outcomes of the proposed method are encouraging as per complexity and usability and future directions of this work include the deployment of the proposed method in a large scale environment, security enhancements and distributed join, leave and lookup operations. © 2022 IEEE.

关键词： Internet of things

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Exploiting time-series analysis to predict customers' behavioural dynamics in social networks 13

Exploiting time-series analysis to predict customers' behavi...

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13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

作者： Rompolas, Gerasimos University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

In the recent years, companies have focused on ways to take advantage of the rich and endless data that flow from the social media. The effective exploitation of such data has a crucial role in businesses' growth and development. Businesses in order to survive in a highly competitive market need to analyze and understand customers' needs and behaviours. This research work addresses the problem of predicting the behavioural dynamics of the customers regarding a brand name, using time-series analytics in social media. While in the existing literature many researchers have focused on studying the individual analytics, in this work we propose a more coarse-grained approach that analyses the overall behavioural dynamics in social networks. In particular, a data mining model is being presented, which exploits users' linguistic and emotional characteristics in social networks, in order to predict the future collective behavioural trends. The purpose of this work is to provide an efficient and automated tool, that will enable businesses to predict the relationships with their customers. Thus, businesses will be able to strengthen and reestablish their bonds with their customers, through appropriate social media marketing campaigns in a timely manner. © 2022 IEEE.

关键词： Data mining

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Intelligent Modeling Method to Optimal Predictive Model for Metal Powder Injection Molding Gears Using Fuzzy-Logic-Based Multi Objective Design

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International Journal of Fuzzy Systems 2023年第6期25卷 2338-2355页

作者： Huang, Wei-Tai Kung, Chien-Yu Chou, Jyh-Horng Department of Mechanical Engineering National Pingtung University of Science and Technology Pingtung Taiwan Department of Mechanical and Computer-Aided Engineering Feng Chia University Taichung407 Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung807 Taiwan

This study will use intelligent modeling to optimize the metal powder injection molding gear process. The robust process combined with fuzzy theory would be used to find process parameters for optimizing process single and multiple quality characteristics. And establish a reverse transfer neural network and adaptive neuro-fuzzy system prediction model. The model hyperparameter structure will be optimized during the modeling process to improve accuracy. The industry can use it to reduce process uncertainty, cost, and extremely a process innovation. The experimental results are single-objective optimization. After optimizing the parameters, the process target displacement, volume shrinkage rate, and powder concentration, compared with the manufacturer's original process, the improvements are increased by 5.79%, 5.66%, and 1.57%. Optimizing multiple quality characteristics has risen by 5.37%, 5.66%, and 0.31%. In the prediction model building part, after the optimization experiment of hyperparameter setting, the accuracy of the backward pass neural network model can reach 96.99%;the accuracy of the adaptive neuro-fuzzy system model can reach 97.45%. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

关键词： Injection molding

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Digital Twin Empowered PV Power Prediction

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Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1472-1483页

作者： Xiaoyu Zhang Yushuai Li Tianyi Li Yonghao Gui Qiuye Sun David Wenzhong Gao the School of Information Science and Engi neering Northeastern UniversityShenyang 110004China the Department of Informatics Universi ty of OsloOslo 0316mNorway the Department of Computer Science Aalborg UniversityAal borg 9220Denmark the Electrification and Energy Infrastructures Division Oak Ridge National LaboratoryOak RidgeTN 37830USA the Department of Electrical and Computer Engineering University of DenverDenverCO 80208USA

The accurate prediction of photovoltaic(PV)power generation is significant to ensure the economic and safe operation of power *** this end,the paper establishes a new digital twin(DT)empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power *** this framework,considering potential data contamination in the collected PV data,a generative adversarial network is employed to restore the historical dataset,which offers a prerequisite to ensure accurate mapping from the physical space to the digital ***,a new DT-empowered PV power prediction method is ***,we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model(i.e.,a parallel network of convolution and bidirectional long short-term memory model)for capturing the hidden spatiotemporal *** proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model,resulting in enhanced prediction ***,a real dataset is conducted to assess the effectiveness of the proposed method.

关键词： Photovoltaic power prediction digital twin hybrid prediction data recovery

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Confronting and intrusion detection techniques of cyber-attacks in wired and wireless communication networks 26

Confronting and intrusion detection techniques of cyber-atta...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Mavrommatis, Konstantinos University of West Attica Department of Informatics and Computer Engineering Greece

ISBN: (纸本)9781450398541

This publication aims to study the attacks that telecommunications networks face every day. Thus, the tools that have been developed through new technologies are presented in detail in order to successfully deal with these attacks. Some of these tools are more effective and others less effective depending on the form of attack. As a conclusion is that very significant steps have been taken in confronting with attacks by intruders by introducing a new and wide field of study in the networks. © 2022 ACM.

关键词： Intrusion detection

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