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Visualization, Informatics and Technology Conference (IVIT), International

作者： Shahidatul Arfah Baharudin Nurul Iman Muhammad Taufik Sri Banu Munisamy Adidah Lajis Computer Engineering Technology Section Universiti Kuala Lumpur MIIT Kuala Lumpur Malaysia

ISBN: (数字)9798350354447

ISBN: (纸本)9798350354454

The research presents that the Stream Control Transmission Protocol (SCTP) provides a very interesting alternative to classical TCP and UDP, especially in multihoming settings. We therefore specifically sought to test SCTP's performance in multihoming scenarios within 5G networks and compare its reliability to TCP and UDP under various Network Address Translation (NAT) settings through complete simulations. We took care to focus on the important performance metrics of throughput, latency, packet loss, and connection stability. The ability of multihoming with SCTP, where a single SCTP endpoint can support several IP addresses, was also tested. In the context that devices will be able to have more than one network interface, this feature is welcomed in 5G networks. An empirical investigation revealed that in NAT settings, while TCP and UDP see higher packet loss and more delay, SCTP maintains a more reliable connection. The as much as needed ultra-reliable and low-latency communications transport protocol for 5G must be very stable. For example, applications such as online gaming, video conferencing, and major IoT operations will be relying on this new network. This paper has specifically emphasized the potential that SCTP's multihoming feature and improved performance metrics hold for 5G networks. This means that SCTP should be recommended to increase network stability and enhance performance, based on the results presented here that have indicated its great potential. Further research will be done on enhancing SCTP for specific 5G use cases under different deployment scenarios and investigating its integration with promising new technologies like edge computing and network slicing.

关键词： TCP Transport protocols Visualization 5G mobile communication Packet loss Stability analysis Reliability Thermal stability Network address translation Videoconferences

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Foliar Chlorophyll Monitoring to Enhance Palm Oil Plant Productivity 9th

Foliar Chlorophyll Monitoring to Enhance Palm Oil Plant Prod...

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9th Future Technologies Conference, FTC 2024

作者： Adnan, Nur Damia Kassim, Mohd Sallehin Mohd Dao, Hassan Mohamed, Maizatul Suriza Sajak, Aznida Abu Bakar Computer Engineering Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur50250 Malaysia Faculty of Engineering Princess of Naradhiwas University Narathiwat96000 Thailand Malaysian Palm Oil Board Selangor Bandar Baru Bangi43650 Malaysia

ISBN: (纸本)9783031731211

In the modern agricultural landscape, enhancing productivity stands as a central objective. The monitoring system of palm oil plant health development based on Foliar Chlorophyll Leaves with IoT, presents an innovative approach to improve palm oil plantation management. Utilizing the ESP32 microcontroller, data from the color sensor is processed, with integrated Wi-Fi capabilities enabling seamless connectivity to the platform. These readings are subject to storage, analysis, and comparison against predefined ranges to ascertain the distinct health status of the plants. Collaboration with the Malaysian Palm Oil Board ensures alignment with user requirements and demands, strengthening the system’s efficacy and reliability. The paper provides a detailed overview of the plant health monitoring process, outlining various treatments and their corresponding RGB color sensor readings. Each treatment, representing a specific plant type, is accompanied by the date of leaf testing, offering a comprehensive insight into the monitoring procedure. Results indicate the system’s effectiveness in detecting stress-induced physiological changes and early signs of diseases, equipping farmers and plantation managers with actionable data for informed decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision making

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Landscape of sign language research based on smartphone apps: coherent literature analysis, motivations, open challenges, recommendations and future directions for app assessment

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Universal Access in the Information Society 2024年第2期23卷 687-702页

作者： David, Dianes Alamoodi, A.H. Albahri, O.S. Zaidan, B.B. Zaidan, A.A. Garfan, Salem Ismail, Amelia Ritahani Albahri, A.S. Alsinglawi, Belal Malik, R.Q. Perak Malaysia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates International Islamic University Malaysia Kuala Lumpur Malaysia Baghdad Iraq School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia Medical Intrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq

Numerous nations have prioritised the inclusion of citizens with disabilities, such as hearing loss, in all aspects of social life. Sign language is used by this population, yet they still have trouble communicating with others. Many sign language apps are being created to help bridge the communication gap as a result of technology advances enabled by the widespread use of smartphones. These apps are widely used because they are accessible and inexpensive. The services and capabilities they offer and the quality of their content, however, differ greatly. Evaluation of the quality of the content provided by these applications is necessary if they are to have any kind of real effect. A thorough evaluation like this will inspire developers to work hard on new apps, which will lead to improved software development and experience overall. This research used a systematic literature review (SLR) method, which is recognised in gaining a broad understanding of the study whilst offering additional information for future investigations. SLR was adopted in this research for smartphone-based sign language apps to understand the area and main discussion aspects utilised in the assessment. These studies were reviewed on the basis of related work analysis, main issues, discussions and methodological aspects. Results revealed that the evaluation of sign language mobile apps is scarce. Thus, we proposed a future direction for the quality assessment of these apps. The findings will benefit normal-hearing and hearing-impaired users and open up a new area where researchers and developers could work together on sign language mobile apps. The results will help hearing and non-hearing users and will pave the way for future collaboration between academicians and app developers in the field of sign language technology. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Smartphones

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Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

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Design and Stability Analysis of Control System in Multiport Autonomous Reconfigurable Solar Power Plants (MARS) 24

Design and Stability Analysis of Control System in Multiport...

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24th IEEE Workshop on Control and Modeling for Power Electronics, COMPEL 2023

作者： Xia, Qianxue Debnath, Suman Marthi, Phani R.V. Saeedifard, Maryam Energy Systems Integration Control Section Oak Ridge National Lab Knoxville United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798350316186

The Multiport Autonomous Reconfigurable Solar Power Plant (MARS) is an integrated photovoltaic (PV) power generation and energy storage system (ESS), that is designed to connect to both alternating current (AC) transmission grids and high-voltage direct current (HVDC) links. It is a three-phase plant consisting of numerous components with a complex hardware and hierarchical control architecture. This paper presents an approach to decouple the multivariable system of MARS using a recursive reduced-order and boundary layer system methodology. This approach enables efficient computation of the control parameters for the Ll, L2, and L3 controllers. To validate the effectiveness of the proposed control strategy, cyclic tests in accordance with pre-defined performance criteria using controller Hardware-in-the-Loop (cHIL) experiments are conducted. The results demonstrate that the MARS system operates consistently under steady-state conditions. Furthermore, the dynamic response of the MARS system to various grid events is analyzed, underlining the resilience of MARS in presence of faults or loss of generation within the connected WECC system. © 2023 IEEE.

关键词： Energy storage

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Predicting Protein-Protein Interactions Using Self-Attention-Based Deep Neural Networks and FastText Embeddings 15

Predicting Protein-Protein Interactions Using Self-Attention...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Oviya, I.R. Shanmukha Sravya, N. Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350370249

Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models focus on encoding protein sequences based on residue composition or physicochemical properties, they often overlook the complex relationships between amino acid segments within lengthy sequences. To address these limitations, we propose a novel hybrid model that combines a self-attention to enhance Deep neural network feature extraction with an with FastText Protein embeddings for predicting PPI. Utilizing transfer learning via the unsupervised FastText embedding technique to derive statistical representations of protein sequences, offers advantages over traditional protein encoding techniques in the context of analysing k-Mer sequences. Our model's performance is assessed through 5-fold cross-validation on datasets of *** and Human-Bacillus Anthracis protein interactions, resulting in accuracies of 92.28% and 94.08%, respectively. Compared to existing machine learning models, our model demonstrates superior performance on both datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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An Optimized Technique For Developing Optical Band Pass Filter

An Optimized Technique For Developing Optical Band Pass Filt...

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Ghosh, Arup Kumar Garai, Gautam Bhattacharjee, Subhankar University of Calcutta A.K.C. School of Information Technology Kolkata India Saha Institute of Nuclear Physics Computer Section Kolkata India Techno International New Town Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798331516666

In this paper we have introduced an innovative method for designing a one dimensional photonic crystal (1D PhC) based Optical Band Pass Filter (OBPF) using the Differential Evolution (DE) algorithm. These filters are essential in optical communication systems because of their ability to control light propagation. The DE algorithm efficiently optimizes the PhC structure to achieve the desired optical band pass characteristics and simplifying the design process. The OBPFs are made of multiple dielectric layers with varying thicknesses and refractive indices to enable high transmission at specific wavelengths and low transmission elsewhere. Optimization helps to find the optimal thickness and refractive index. MATLAB implementation shows encouraging results with high transmittance (99.2214%) and a narrow full width at half maximum (FWHM) of 0.6 nm. The experimental results demonstrate the superiority of this approach over the existing well-known traditional methods. © 2024 IEEE.

关键词： MATLAB

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks 4

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Oviya, I.R. Sravya N, Shanmukha Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350343670

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models. © 2024 IEEE.

关键词： Deep Learning Machine Learning Protein Feature Encoding Protein Protein interaction Protein sequence

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StakePage: Analysis of Stakeholders of an Information System Using Page Rank Algorithm 1

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Hassan, Tanveer Mohammad, Chaudhary Wali Sadiq, Mohd. Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi110025 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi110025 India

ISBN: (数字)9789811988257

ISBN: (纸本)9789811988240

The requirements elicitation process is the sub-process of requirements engineering in which stakeholders are involved to identify the requirements of an information system. Various methods have been developed to identify and analyze the stakeholders like goal-oriented requirements elicitation process, traditional methods, etc. Based on our review, we found that little attention is given to the analysis of the stakeholders using different measures of social networks before the starting of the requirements elicitation process. Therefore, to address this issue, this paper presents a StackPage method for the analysis of the stakeholders using page rank algorithm of social network measures. The applicability of the StackPage method is discussed with the help of the stakeholders of an institute examination system. Finally, the results are compared with two state-of-the-art methods, i.e., StakeRare and StakeSoNet methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Requirements engineering

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Emotional Kansei Words for Digital and Pervasive Product Design

Emotional Kansei Words for Digital and Pervasive Product Des...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Nik Ahmad, Nik Azlina Lokman, Anitawati Mohd Abdullah, Munaisyah Hakim Suhaimi, Ahmad Iqbal Universiti Kuala Lumpur Malaysian Institute of Information Technology Software Engineering Section Kuala Lumpur Malaysia Universiti Teknologi Mara Faculty of Computer and Mathematical Sciences Shah Alam Malaysia

ISBN: (纸本)9798350302684

The explosive growth of pervasive and diverse digital products demands the importance of addressing emotional design within the sphere of product development. As a result, there has been a significant focus from both industry and academia on studying consumer perspectives related to a wide range of pervasive and digital products, adopting a variety of methods, tools, and resources to delve into user emotional experiences. However, a comprehensive study focusing on users' emotions, incorporating a precisely defined semantic or lexicon tailored for this type of user interface (UI) product design remains elusive. Therefore, this study aims to synthesize and validate a comprehensive array of emotional keywords applicable for pervasive and digital products through an extensive literature search and a rigorous validation procedure involving language experts. The result is a meticulously refined set of 175 keywords that will serve as a valuable point of reference for all endeavors associated with evaluating and configuring the emotional structure of pervasive and digital product UI experiences. The study's findings have the potential to provide valuable guidance to industry practitioners and academic researchers, contributing to the improvement of UI design landscape and the development of new systems by enhancing their insight into users' emotional experiences through the utilization of pertinent emotional keywords in a wide range of digital and pervasive UI product designs. © 2023 IEEE.

关键词： Product design

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