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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Jayatharan, Vithushigan Alwis, Dileeka University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350341454

The recent advancements in deep learning techniques and computational power have promoted the development of novel approaches for music generation. In this study, generating alapana, an improvisational form of Carnatic music was proposed, by leveraging Generative Adversarial Networks (GANs) and Finite State Machines (FSM). The goal is to create melodious alapana sequences that follow a given input Raga, ensuring continuity and coherence throughout the generated musical piece. The proposed approach incorporates Carnatic music theory rules into the generation process to enhance the structural coherence of the generated alapana. Additionally, various hyperparameter settings were explored to achieve the best performance. The Fréchet Audio Distance, Percentage of Correct Pitches and the Subjective evaluation through human listeners are the evaluation metrics of this approach. The result of this study demonstrates the potential of using GANs and FSM for generating continuous and pleasing alapana sequences in Carnatic music, contributing to the growing body of research in computational music generation. © 2023 IEEE.

关键词： Music

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RT DL Tasks Distribution for Sensitive Data Protection and Resource Optimization 11

RT DL Tasks Distribution for Sensitive Data Protection and R...

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11th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2023

作者： Zeidan, Farah Elhayani, Mostafa Soubra, Hassan German University in Cairo Computer Science and Engineering Cairo Egypt Informatics Technische Universität München Cairo Egypt

ISBN: (纸本)9798350322101

With the rapid growth of data-intensive applications and the proliferation of the Internet of Things (IoT) devices, traditional cloud architectures face significant challenges in enabling real-time decision-making. Centralized cloud infrastructures have limitations in handling the increasing volume of data generated at network ends. In response, edge computing has emerged as a promising paradigm that brings computation closer to the data source, offering advantages over traditional cloud architectures. This paper presents an edge computing system that incorporates a load balancer to determine whether real-time tasks should be offloaded to the edge or to the cloud. To enhance security, a data retention measure is introduced to ensure sensitive data remains within the edge device, irrespective of other load-balancing conditions. This approach minimizes the risk of attacks and unauthorized access by containing sensitive data within the edge device. Furthermore, the impact on system performance is evaluated by monitoring CPU utilization before and after task execution. This research contributes secure and efficient task offloading strategies in edge computing, with implications for real-time processing and data privacy sensitive applications. © 2023 IEEE.

关键词： Decision making

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Chaos-Based Novel Watermarked Satellite Image Encryption Scheme

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computer Modeling in engineering & Sciences 2025年第4期143卷 1049-1070页

作者： Mohamed Medani Yahia Said Nashwan Adnan Othman Farrukh Yuldashev Mohamed Kchaou Faisal Khaled Aldawood Bacha Rehman Applied College of Muhayil Aseer King Khalid UniversityAbha62529Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Computer Engineering College of EngineeringKnowledge UniversityErbil44001Iraq Department of Computer Engineering Al-Kitab UniversityAltun Kupri36001Iraq Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100070Uzbekistan Department of Industrial Engineering College of EngineeringUniversity of BishaBishaP.O.Box 001Saudi Arabia Department of Computer Science Solent UniversitySouthamptonSO140YNUK

Satellite images are widely used for remote sensing and defence applications,however,they are subject to a variety of *** ensure the security and privacy of these images,theymust be watermarked and encrypted before ***,this paper proposes a novel watermarked satellite image encryption scheme based on chaos,Deoxyribonucleic Acid(DNA)sequence,and hash *** watermark image,DNA sequence,and plaintext image are passed through the Secure Hash Algorithm(SHA-512)to compute the initial condition(keys)for the Tangent-Delay Ellipse Reflecting Cavity Map(TD-ERCS),Henon,and Duffing chaotic maps,*** bitwise XOR and substitution,the TD-ERCS map encrypts the watermark *** ciphered watermark image is embedded in the plaintext *** embedded plaintext image is permuted row-wise and column-wise using the Henon chaotic *** permuted image is then bitwise XORed with the values obtained from the Duffing *** additional security,the XORed image is substituted through a dynamic *** evaluate the efficiency and performance of the proposed algorithm,several tests are performed which prove its resistance to various types of attacks such as brute-force and statistical attacks.

关键词： DNA sequence TD-ERCS chaoticmap henon chaoticmap duffing chaotic system SHA-512 encryption technique watermark embedding

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A comprehensive evaluation of large language models in mining gene relations and pathway knowledge

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Quantitative Biology 2024年第4期12卷 360-374页

作者： Muhammad Azam Yibo Chen Micheal Olaolu Arowolo Haowang Liu Mihail Popescu Dong Xu Department of Electrical Engineering and Computer Science University of MissouriColumbiaMissouriUSA Bond Life Sciences Center University of MissouriColumbiaMissouriUSA Institute for Data Science and Informatics University of MissouriColumbiaMissouriUSA Department of Biomedical Informatics Biostatistics and Medical EpidemiologyUniversity of MissouriColumbiaMissouriUSA

Understanding complex biological pathways,including gene–gene interactions and gene regulatory networks,is critical for exploring disease mechanisms and drug *** literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the ***-scale language models(LLMs)trained on extensive text corpora contain rich biological information,and they can be mined as a biological knowledge *** study assesses 21 LLMs,including both application programming interface(API)-based models and open-source models in their capacities of retrieving biological *** evaluation focuses on predicting gene regulatory relations(activation,inhibition,and phosphorylation)and the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway *** indicated a significant disparity in model ***-based models GPT-4 and Claude-Pro showed superior performance,with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction,and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction,***-source models lagged behind their API-based counterparts,whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations,*** KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for *** study suggests that LLMs are informative in gene network analysis and pathway mapping,but their effectiveness varies,necessitating careful model *** work also provides a case study and insight into using LLMs das knowledge *** code is publicly available at the website of GitHub(Muh-aza).

关键词： biomedical text mining gene-gene interaction KEGG pathway large language model

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Transforming Education in the Digital Age: Exploring the Dimensions of Education 4.0

Transforming Education in the Digital Age: Exploring the Dim...

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IEEE Education engineering (EDUCON)

作者： Gerasimos Vonitsanos Ioanna Moustaka Spyridon Doukakis Phivos Mylonas Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

The education sector, a cornerstone for social and economic development, has seen minimal transformation despite technological advancements. The shift towards digital evolution in adult education and lifelong learning is imperative for societal progress. Utilizing digital tools enhances learning experiences and employment prospects, crucial for attracting skilled workers in a flexible work environment. Modern digital technologies contribute to global competitiveness, fostering social cohesion and improving the quality of teaching. The 21st-century education revolution emphasizes internet-based pedagogy, bridging formal and experiential learning through technology. Lifelong learning, essential for skills enhancement, gains significance with emerging technologies, thus reducing barriers to access while promoting inclusiveness. Digital platforms, e-learning, and innovative methods revolutionize adult education beyond dispute, transcending traditional classrooms into engaging learning environments. The global “right to quality and inclusive education” is therefore facilitated by digital technology, offering flexibility, accessibility, and adaptability. Expanding beyond conventional classrooms nurtures inclusive and effective adult education, thereby propelling overall progress. The education landscape evolves, emphasizing on remote learning, interdisciplinary collaboration, and the acquisition of digital skills to meet the demands of a dynamic future job market.

关键词： Economics Electronic learning Distance learning Employment Collaboration Propulsion engineering education

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Exploring Ensemble Machine Learning Models for Attention and Memory Assesment 46

Exploring Ensemble Machine Learning Models for Attention and...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Gramouseni, Foteini Prapas, Georgios Bellos, Christos Angelidis, Pantelis Giannakeas, Nikolaos Tsipouras, Markos G. University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Ioannina Department of Informatics and Telecommunications Ioannina Greece

ISBN: (纸本)9798350303964

EEG analysis for attention and memory assessment is an important research topic that has gained a lot of attention in recent years. Virtual Reality (VR) is an innovative technology that provides the ability to design experiments suitable for research in this field. This study explores the feasibility of utilizing ensemble classification algorithms to analyze EEG signals obtained during VR stimulation for the purpose of evaluating memory encoding and attention orienting. The obtained results indicate that EEG analysis can accurately assess memory and attention levels in VR environment setting with a high degree of certainty (84.90% accuracy, and an average of 0.86 true positive rate, 0.88 positive predicted value, 0.89 area under the ROC curve, and 0.89 true negative rate). © 2023 IEEE.

关键词： Virtual reality

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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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Force-directed graph embedding with hops distance

Force-directed graph embedding with hops distance

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Lotfalizadeh, Hamidreza Hasan, Mohammad Al Purdue University School of Electrical and Computer Engineering West LafayetteIN United States Indiana University Indianapolis School of Informatics Computing and Engineering IndianapolisIN United States

ISBN: (纸本)9798350324457

Graph embedding has become an increasingly important technique for analyzing graph-structured data. By representing nodes in a graph as vectors in a low-dimensional space, graph embedding enables efficient graph processing and analysis tasks like node classification, link prediction, and visualization. In this paper, we propose a novel force-directed graph embedding method that utilizes the steady acceleration kinetic formula to embed nodes in a way that preserves graph topology and structural features. Our method simulates a set of customized attractive and repulsive forces between all node pairs with respect to their hop-distance. These forces are then used in Newton's second law to obtain the acceleration of each node. The method is intuitive, parallelizable, and highly scalable. We evaluate our method on several graph analysis tasks and show that it achieves competitive performance compared to state-of-the-art unsupervised embedding techniques. © 2023 IEEE.

关键词： Data representation Dimension reduction Force-directed Graph embedding Unsupervised

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The Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks 3rd

The Effects of Weather Conditions on Data Transmission in Fr...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Kadhim, Aqeel A. Naif, Jolan R. Salman, Muna M. Hoomod, Haider K. Computer Techniques Engineering Department Imam AlKadhim University College Baghdad Iraq Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Information Technology Directorate Ministry of Science and Technology Baghdad Iraq College of Education Computer Science Department Al-Mustansiriya University Baghdad Iraq

ISBN: (纸本)9789819708918

It is necessary to collect data measured in open lands, whether for example for monitoring purposes or for border protection. The process of data transmission from active nodes and its next level to the cluster head (CH), followed by follow-up to arrive at the base station (BS), with safety and acceptability, are the main objectives of wireless sensor networks (WSNs). The key benefits of wireless systems are low-cost system installation and loss-free data transfer. Weather conditions in the spacious area affect wireless data transmission that is done between sensor nodes in free space. This study attempted to ascertain the influence of atmospheric and meteorological conditions on the loss of data when utilizing the wireless data transmission systems in a wireless sensor network. For this reason, the volume of data transfer losses that happen in free space because of three types of weather conditions is the main discussion point. The meteorological criteria that can be considered in this research are temperature, clear weather, dust, and rain as the weather elements that affect the Iraqi climate. The correlation analysis's outcomes show that the amount of precipitation and the size of raindrops have a main effect on the losses of data transmission. From a practical point of view, there is no confirmed statistical evidence of the amount of loss in the volume of transmitted data as a result of the amount of rainfall with the use of different transmission frequencies. In this research, we designed an objective routing protocol called Fuzzy Clustering with Ant Colony System FCACS, which has been utilized as a simulation program using the MATLAB 2018b application. The practical results of the proposed protocol for data transmission in free space and under different weather conditions were examined, and the simulation results were compared with those of two well-known routing protocols, which are DEEC and Z-SEP. In terms of the quantity of data received from the CH or BS a

关键词： Data transfer

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Federated Learning Models for Intrusion Detection in Industrial IoT Networks 7

Federated Learning Models for Intrusion Detection in Industr...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Prasad, Sanjana Sharma, Ishu Rajendraprasad, Deepashree HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Boston University Department of Health Informatics BostonMA United States

ISBN: (纸本)9798350372816

The Industrial Internet of Things has emerged as an essential tool for building Industry 4.0 and Industry 5.0 where timely information can be retrieved from different scenarios. These devices are highly vulnerable to cyberattacks as heterogeneous types of devices can be used in the infrastructure that may or may not be equipped with standardized security protocols. Artificial intelligence-based methodologies can effectively identify these types of attacks on a prior basis for taking mitigation action. This method raises concerns about data privacy as building a machine learning-based method requires the sharing of network data that can reveal the actual information of industries. The proposed Federated Learning based framework handles this concern by preserving each device's critical data by utilizing the benefits of the decentralized model aggregation. This research work presents the comparison of the proposed framework on the CICIoT2023 dataset with federated averaging and federated proximal techniques for achieving a global model. The performance evaluation of these two aggregation techniques is performed based on metrics of accuracy, loss, precision and recall. The results prove that the federated proximal method achieves higher accuracy in comparison to the federated averaging method. © 2024 IEEE.

关键词： Network intrusion

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