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Identication of Antimicrobial Peptides Using Chou’s 5 Step Rul

Computers, Materials & Continua 2021年第6期 2863-2881页

作者： Sharaf J.Malebary Yaser Daanial Khan Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahorePakistan

With the advancement in cellular biology,the use of antimicrobial peptides(AMPs)against many drug-resistant pathogens has *** have a broad range of activity and can work as antibacterial,antifungal,antiviral,and sometimes even as anticancer *** traditional methods of distinguishing AMPs from non-AMPs are based only on wet-lab *** experiments are both time-consuming and *** the recent development in bioinformatics more and more researchers are contributing their effort to apply computational models to such *** study proposes a prediction algorithm for classifying AMPs and distinguishing between AMPs and *** proposed methodology uses machine learning algorithms to predict such sequences.A dataset was formulated based on 1902 samples of AMPs and 3997 samples of *** learning algorithms are trained on a xed number of succinct coefcients retaining sequence and composition information of primary *** features are extracted using position relative incidence and statistical *** performance is validated via various validation tests including a 10-fold cross-validation *** overall accuracy of 95.43%was achieved.A comparison of results with existing methodologies shows that the proposed methodology outperformed existing methodologies in terms of prediction accuracy.

关键词： Antimicrobial peptides multidrug-resistant antiviral antibacterial cytokine classication

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Forgetting User Preference in Recommendation Systems with Label-Flipping

Forgetting User Preference in Recommendation Systems with La...

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IEEE International Conference on Big Data

作者： Manal A. Alshehri Xiangliang Zhang Computer Science CEMSE King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz University Rabigh Kingdom of Saudi Arabia Department of Computer Science and Engineering University of Notre Dame Notre Dame USA

Recommendation systems play a crucial role in identifying users’ preferences based on their historical interaction records and those of other users. However, the ability to “forget” certain users’ preferences is indispensable for ensuring user privacy and maintaining recommendation accuracy. It is essential to accommodate a user’s request to exclude their behavioral data from the recommendation system when necessary. Likewise, if certain data corrupts the system, its impact should be removed to restore system performance. In this paper, we propose FlipRec, a general and efficient framework for recommendation models to “forget” the preferences of specific users while retaining the model’s performance for all other users. Our concept of forgetting user preferences is inspired by the label-flipping attack, a technique where the labels of some training samples are inverted to adversarially manipulate the weights of the trained model. FlipRec adjusts the recommendation model weights to forget the targeted users by flipping their interaction records $y \in \{ 0,1\}$. To preserve the model’s performance for the remaining users, we augment the fine-tuning data with samples from users who have interacted with the same items as the targeted users. This ensures minimal impact on these users during the “forgetting” process. FlipRec has been validated on both contentbased recommendation models and collaborative filtering models. The experimental results show that FlipRec outperforms stateof-the-art unlearning methods in terms of efficiency, the ability to forget targeted users, and the preservation of performance for the remaining users.

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Retracted: Using Fuzzy Uncertainty Quantization and Hybrid RNN-LSTM Deep Learning Model for Wind Turbine Power

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IEEE Transactions on Industry Applications 2023年 1-1页

作者： Lin, Lihua Li, Min Ma, Li Nazari, Masooma Mahdavi, Sadegh Yunianta, Arda Department of Telecommunication and information Xi'an University of Science and Technology 118249 Xi'an China 710054 Electrical and Electronics Department University of the Ryukkyus Japan Electrical and Electronics Department Islamic Azad University of Marvdasht Marvdasht Fars Province Iran Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia and Faculty of Engineering Mulawarman University Samarinda Indonesia

Along with the many benefits, which the high penetration of wind turbine has brought to the smart grids, the issues arising due to the uncertainty effect is an inevitable phenomenon, which needs to be controlled. The high uncertainty effects can cause unstable operation and management scheduling plans, which can cause severe problems for both the operator and customers. This paper proposes a new quantization approach based on hybrid deep learning to capture the forecast error in the wind turbine output power. The proposed method is equipped with a long-short term memory (LSTM) model and recurrent neural network (RNN) to learn the most effective spatial-temporal features in wind turbine output power. Due to the high complexity of the data, a new optimization method based on a modified sine cos algorithm is proposed to help more stable training of the model. The feasibility and appropriate performance of the model is assessed through experimental analysis on two datasets in the Australia wind farms. IEEE

关键词： Long short-term memory

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An adaptive normalized google distance similarity measure for extractive text summarization 2

An adaptive normalized google distance similarity measure fo...

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2nd International Conference on Computer and information Sciences, ICCIS 2020

作者： Abuobieda, Albaraa Osman, Ahmed Hamza Faculty of Computer Studies International University of Africa Khartoum Sudan Faculty of Computing and Information Technology King Abdulaziz University Rabigh Saudi Arabia

ISBN: (纸本)9781728154671

No doubt, for each clustering algorithm running improper similarity calculation, that can lead to reduce the clustering accuracy. Hence, several applications that employ such algorithm are affected negatively and generate improper results. In a previous work, we found that employing the Normalized Google Distance (NGD) similarity measure to cluster document's sentences for text summarization problem is unreasonable;since NGD was basically designed to work with large databases. On the other hand, a term-weighting approach is used widely to define document's contents. In this paper, a term-weighting approach is integrated with the NGD similarity measure to adopt the latter from being able to work in small database (single document). Differential Evolution (DE) algorithm is used to train and test the proposed method. The DUC2002 dataset is preprocessed and used as a test bed. The results showed that our proposed method could outperform the previous work in terms of F-score evaluation measure as well as outperformed the standard baseline methods Microsoft Word and Copernic Summarizer. © 2020 IEEE.

关键词： Clustering algorithms

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Retraction Note: Diagnosis of cardiovascular disease using deep learning technique

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Soft computing 2024年第2期28卷 897-897页

作者： Ahmad, Shakeel Asghar, Muhammad Zubair Alotaibi, Fahad Mazaed Alotaibi, Yasir D. Department of Computer Science Faculty of Computing and Information Technology in Rabigh (FCITR) King Abdulaziz University Jeddah Saudi Arabia Institute of Computing and Information Technology Gomal University DIKHAN Pakistan Department of Information Systems Faculty of Computing and Information Technology (FCIT) King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology in Rabigh (FCITR) King Abdulaziz University Jeddah Saudi Arabia

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System for Predicting Heart Disease using Sophisticated Machine Learning Techniques

System for Predicting Heart Disease using Sophisticated Mach...

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information Technologies and Communications (GCITC), Global Conference on

作者： Trilok Nath Pandey Bibhuti Bhusan Dash Lalbihari Barik Utpal Chandra De Abinash Tripathy Sudhansu Shekhar Patra School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India School of Computer Applications KIIT Deemed to be University Bhubaneswar India Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Kingdom of Saudi Arabia School of Computer Applications KIIT Deemed to be University Bhubaneswar Odisha India

Cardiovascular illness stands as a significant global health issue, and timely identification of this condition holds paramount importance to ensure effective intervention. In this paper, we introduce a fused model for anticipating heart ailments. This amalgamation incorporates four distinct algorithms: LightGBM, XGBoost, Multilayer Perceptron, and AdaBoost. Through an assessment on the Cleveland dataset, AdaBoost emerged as the most accurate with a precision of 0.985. Subsequently, XGBoost followed closely with an accuracy of 0.980, succeeded by LightGBM at 0.956, and Multilayer Perceptron at 0.936. To harness the collective power of these models, we employed a weighted bagging technique that accords weights to predictions based on their individual accuracy rates. The resultant composite model attained an accuracy of 0.96. This outcome is slightly lower than the model with the highest accuracy, as it involves a weighted mean calculation across model predictions. This study indicates the potential utility of the proposed methodology in cardiac disease prediction, leveraging the ensemble approach to potentially outperform standalone models.

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Performability analysis of FOGGIS model for geospatial web services 11

Performability analysis of FOGGIS model for geospatial web s...

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11th International Conference on Cloud computing, Data Science and Engineering, Confluence 2021

作者： Panigrahi, Símil K. Barik, Rabindra Kumar Behera, Satyaban Barik, Lalbihari Patra, Sudhansu Shekhar School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of Information Science Electronics and Telecommunications G.M. University Sambalpur India Department of Information Systems Faculty of Computing and Information Technology Rabigh King Abdulaziz University Saudi Arabia School of Computer Applications KIIT Deemed to be University Bhubaneswar India

ISBN: (纸本)9780738131603

The Internet of Spatial Things (IoST), continuously generates a large volume of geospatial data from a large number of connected smart devices. Cloud computing is inefficient to respond to IoST because of latency concern. Fog computing allows geospatial data processing as well as geospatial storage close to IoST devices and addresses this concern. This fog computing-based GIS model i.e. FogGIS is assisting to the cloud for invoking various geospatial web services. In many scenarios the service requests arrive to the fog layer and waits in a queue due to the current workload of fog servers. When all the Virtual Machines (VMs) are congested (busy), the arrived tasks are queued in the fog buffer until the fog servers becomes available to process the service. This creates the possibilities of task reneging or dropping. Reneged or dropped tasks could be resubmitted if the tasks were not dropped due to security reasons. In this paper, it experiments the performance of the fog layer based on the task arrival and the buffer size of the system using M/M/c/N Queuing theory. It also offers numerical illustrations to demonstrate the application of task reneging and feedback on the parameters such as queuing delay, probability of immediate service as well as probability of task rejection. © 2021 IEEE.

关键词： Fog computing

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EVMAS: An energy-aware virtual machine allocation scheme in fog centers 2

EVMAS: An energy-aware virtual machine allocation scheme in ...

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2nd International Conference for Emerging technology, INCET 2021

作者： Naik, Madhusudan Barik, Lalbihari Kandpal, Meenakshi Patra, Sudhansu Shekhar Jena, Sudarson Barik, Rabindra K. Department of Computer Science Hakimpada Angul India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India Sambalpur University SUIIT Department of Computer Science and Engineering Sambalpur India

ISBN: (纸本)9781728170299

Due to the rapid growth of the Industrial IoT (IIoT), social media and digitization, and wireless communication technology in various sectors, data volume is increasing rapidly. Cloud computing is an emerging solution with fog computing assistance for handling and processing a huge volume of data. It is a soar of means to improve the quality of services provided to users through cloud computing, which has been more overwhelmed by the massive data flow. Transmitting all the data to the cloud and getting it back from the cloud causes high latency and requires high network bandwidth. In the IIoT applications, there is enough energy needed in the fog layer, a promising area to be handled by the cloud service providers. An important factor that contributes to the energy consumption in fog servers is VM allocation. This paper proposes EVMAS, i.e., an energy-aware VM allocation scheme based on Lagrange's undetermined multiplier with the Hessian matrix, which improves the Quality of Service (QoS) in parallel with service level agreement (SLA). The suggested technique outperforms in comparison to the other state of the algorithm. © 2021 IEEE.

关键词： Fog computing

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Dynamic AI-Driven Network Slicing with O-RAN for Continuous Connectivity in Connected Vehicles and Onboard Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Shah, Syed Danial Ali Bashir, Ali Kashif Al-Otaibi, Yasser D. Dabel, Maryam M. Al Ali, Farman University of Leeds School of Electronic and Electrical Engineering LeedsLS2 9JT United Kingdom Manchester Metropolitan University Department of Computing and Mathematics M15 6BH United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah21589 Saudi Arabia University of Hafr Al Department of Computer Science and Engineering Batin Saudi Arabia Sungkyunkwan University School of Convergence Seoul Korea Republic of

The rise of connected and autonomous vehicles signifies an era of intelligent transportation systems, where robust and continued network connectivity is essential for critical applications and enhanced in-vehicle Consumer Electronics (CE) experiences. Slicing at the network's edge offers tailored and dedicated logical networks for diverse and low-latency vehicular demands, including Advanced Driver Assistance Systems (ADAS) and in-car infotainment. However, seamless migration of network slices as vehicles traverse coverage areas of different network operators presents formidable challenges, such as ensuring continuous connectivity and uninterrupted service for both safety-critical systems and consumer-oriented services. In this paper, we introduced dynamic network slicing for continuous connectivity in connected vehicles and onboard CE using the Open Radio Access Network (O-RAN) framework in a highly dynamic and mobile environment. We implemented an xAPP within O-RAN that enables Deep Reinforcement Learning (DRL) agent to learn optimal policies through interaction with the network, guiding intelligent decisions on slice migration, resource allocation, and handover optimization. We conducted simulations and evaluations to demonstrate the effectiveness of the proposed xAPP in maintaining optimal Quality of Service (QoS), ensuring efficient RAN resource utilization, minimizing service interruptions, and prioritizing safety-critical slices, all while supporting seamless operation of CE within vehicles during mobility. © 1975-2011 IEEE.

关键词： Advanced driver assistance systems

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Recognizing User Activity Using a Smartphone's Accelerometer and Deep Neural Network Classifier 6

Recognizing User Activity Using a Smartphone's Accelerometer...

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6th International Conference on computing, Engineering, and Design, ICCED 2020

作者： Pranata, Syech Mantoro, Teddy Ayu, Media Anugerah Prabuwon, Anton Satria Dewi, Deshinta Arrova Sampoerna University Department of Computer Science Jakarta Indonesia Faculty of Computing and Information Technology King Abdulaziz University Rabigh Saudi Arabia Faculty of Information Technology INTI International University Nilai Negeri Sembilan Malaysia

ISBN: (纸本)9781728191898

Along with today's fast-growing technology, machines/devices especially mobile devices have been developed using many sensors to simplify the user's activities. One of the most known and frequently used sensors is called accelerometer, daily used as a step counter, image stabilization, and user interfaces control. However, activity recognition is considered a difficult task due to the reality that each activity has its unique features and there is no clear analytical way to analyze sensor data into specific forms of action in general. This study examines the potential and exciting ability of the accelerometer to recognize user activity by making simple prototype to support the implementation of this user activity recognition. After data acquisition, deep learning classifier will be used to differentiate activities. This research will show the efficiency and utilization of using accelerometer combined with deep learning in recognizing user activity, which can be associated with many applications for advance study such as falling detection, abnormality detection, and prediction of human behavior. © 2020 IEEE.

关键词： Accelerometers

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