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Cyber Threat Analysis and Mitigation in Emerging Information Technology (IT) Trends
(Springer Science and Business Media Deutschland GmbH, 2024) Imam, Mohsin; Wajid, Mohd Anas; Bhushan, Bharat; Hameed, Alaa Ali; Jamil, Akhtar
For the information technology sector, cybersecurity is essential. One of the main issues in the modern world is sending information from one system to another without letting the information out. Online crimes, which are on the rise daily, are the first thing that comes to mind when we think about cyber security. Various governments and businesses are adopting a number of actions to stop these cybercrimes. A lot of individuals are still quite worried about cyber security after taking many safeguards. This study’s primary goal is to examine the difficulties that modern technology-based cyber security faces, especially in light of the rising acceptance of cutting-edge innovations like server less computing, blockchain, and artificial intelligence (AI). The aim of this paper is to give readers a good overview of the most recent cyber security trends, ethics, and strategies. This study focuses on the present state of cyber security and the steps that may be taken to address the rising dangers posed by modern technology through a thorough investigation of the existing literature and actual case studies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Reinforcement learning infused MAC for adaptive connectivity
(Institute of electrical and electronics engineers inc., 2024) Sah, Dinesh Kumar; Nauman, Ali; Jamshed, Muhammad Ali; Cengiz, Korhan; Ivković, Nikola; Uroš, Vedran
The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.
Interpretable Motor Sound Classification for Enhanced Fault Detection Leveraging Explainable AI
(Institute of Electrical and Electronics Engineers Inc., 2024) Khan, Shaiq Ahmad; Ahmad Khan, Faiq; Jamil, Akhtar; Hameed, Alaa Ali
In industries, machines communicate through sounds, decoded by predictive maintenance to prevent issues. Understanding motor sounds is crucial for seamless industrial operations. This research undertakes a comprehensive explo-ration of machine learning models, specifically Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest, applied to motor sound data for classifying instances as either healthy or faulty. The ANN, boasting an 81.22 % accuracy, reveals commendable precision and recall values for both classes, indicating its robust predictive capabilities. However, there is room for improvement, particu-larly in accurately classifying healthy motors. SVM marginally outperforms the ANN with an accuracy of 81.32%, showcasing balanced precision and recall for both classes. Notably, KNN, while exhibiting a slightly lower accuracy of 80.22 %, excels in recall for the healthy class, emphasizing its efficacy in correctly identifying healthy motor sounds. Random Forest attains an accuracy of 81.32 %, featuring notably high recall for the healthy class (0.91), underscoring its proficiency in capturing instances of healthy motor sounds. In-depth metrics provide nuanced insights into the strengths and specificities of each model, offering a foundation for informed decisions based on application priorities and requirements. The study contributes not only quantitative metrics but also interpretability tools, including LIME and SHAP, to enhance transparency and elucidate the intricate patterns within motor sound data. © 2024 IEEE.
Robust control of feeding speed for coal mine tunnel drilling machines
(Institute of electrical and electronics engineers inc., 2024) Liu, Xiao; Chen, Luefeng; Wu, Min; Cao, Weihua; Lu, Chengda; Pedrycz, Witold
Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed.
Advanced Generative AI Methods for Academic Text Summarization
(Institute of Electrical and Electronics Engineers Inc., 2024) Dar, Zaema; Raheel, Muhammad; Bokhari, Usman; Jamil, Akhtar; Alazawi, Esraa Mohammed; Hameed, Alaa Ali
The exponential growth of scientific literature emphasizes the need for employing advanced techniques for effective text summarization, which can significantly speed up the research process. This study tackles the challenge by advancing scientific text summarization through AI and deep learning methods. We delve into the integration and fine-tuning of cutting-edge models, including LED-Large, Pegasus variants, and BART, aiming to refine the summarization process. Unique combinations, such as SciBERT with LED-Large, were investigated to ensure the capture of critical details frequently missed by traditional methods. This novel approach led to notable improvements in summarization effectiveness. Our findings indicate that models like LED-Large excel in quickly adapting to training data, achieving impressive semantic understanding with fewer training epochs, evidenced by achieving a FRES score of 28.5852 and ROUGE scores, including a ROUGE-l F1-Score of 0.4991. However, while extensively trained models like BART -large and Pegasus displayed strong semantic capabilities, they also pointed to the necessity for refinements in readability and higher-order n-gram overlap in the produced summaries. © 2024 IEEE.
Combining Text Information and Sentiment Dictionary for Sentiment Analysis on Twitter During COVID
(Springer Science and Business Media Deutschland GmbH, 2024) Vidushi; Jain, Anshika; Shrivastava, Ajay Kumar; Bhushan, Bharat; Hameed, Alaa Ali
Presence of heterogenous huge data leads towards the ‘big data’ era. Recently, tweeter usage increased with unprecedented rate. Presence of social media like tweeter has broken the boundaries and touches the mountain in generating the unstructured data. It opened research gate with great opportunities for analyzing data and mining ‘valuable information’. Sentiment analysis is the most demanding, versatile research to know user viewpoint. Society current trend can be easily observed through social network websites. These opportunities bring challenges that leads to proliferation of tools. This research works to analyze sentiments using tweeter data using Hadoop technology. It explores the big data arduous tool called Hadoop. Further, it explains the need of Hadoop in present scenario and role of Hadoop in storing ample of data and analyzing it. Hadoop cluster, Hadoop Distributed File System (HDFS), and HIVE are also discussed in detail. The Dataset used in performing the experiment is presented. Moreover, this research explains thoroughly the implementation work and provide workflow. Next session provides the experimental results and analyzes of result. Finally, last session concludes the paper, its purpose, and how it can be used in upcoming research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Deep Learning-based Semantic Search Techniques for Enhancing Product Matching in E-commerce
(Institute of Electrical and Electronics Engineers Inc., 2024) Aamir, Fatima; Sherafgan, Raheimeen; Arbab, Tehreem; Jamil, Akhtar; Close Bhatti, Fazeel Nadeem; Hameed, Alaa Ali
Searching is the process of information retrieval utilizing specific criteria or keywords. Integrating search function-alities on e-commerce platforms enables users to efficiently locate exactly what they are searching for through keyword matching. Beyond conventional keyword matching, semantic search involves aligning products with customer queries by capturing the essence of the queries, thereby retrieving semantically related products from the pertinent catalog. Semantic search enhances the e-commerce shopping experience by allowing platforms to tailor responses to user preferences through an in-depth understanding of search intents. Challenges such as morphological variations, spelling errors, and the interpretation of synonyms, antonyms, and hypernyms are addressed through deep learning models de-signed for semantic query-product matching. This study conducts a comparative analysis of various semantic search methodologies and assesses their efficacy, incorporating deep learning strate-gies for query auto-completion and spelling corrections. The evaluation employs sentence transformer models to determine the optimal approach for semantic searching, gauged by nDCG, MRR, and MAP metrics. LSTM, BART, and n-gram models are also examined for auto-completion capabilities. The research analyzes the Amazon Shopping Queries Dataset and the Upstart Commerce catalog datasets. © 2024 IEEE.
CryptStego: Powerful Blend of Cryptography and Steganography for Securing Communications
(Springer Science and Business Media Deutschland GmbH, 2024) Pandey, Shraiyash; Baniya, Pashupati; Nand, Parma; Hameed, Alaa Ali; Bhushan, Bharat; Jamil, Akhtar
In today’s era, security is one of the most critical issues in the development of electronic communications applications, especially when sending private data. The data may be encrypted with several algorithms; however, an extra layer of security can improve protection by a significant amount. Therefore, in this paper, we have developed an application, CryptStego, to secure data using two techniques, cryptography and steganography, to transmit data securely. The encryption of original data is executed using Blowfish algorithm, a cryptographic technique. Additionally, the encrypted data is hidden through using Least Significant Bit (LSB), a steganography technique. The implementation of both techniques offers an extensive level of security, since an intruder must firstly identify the encrypted text within the image to attain the encrypted text, then secondly to decrypt using the algorithm to obtain the original message. Therefore, any intruder must encounter multiple levels of security to obtain the original message from the cipher image. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Applications and Associated Challenges in Deployment of Software Defined Networking (SDN)
(Springer Science and Business Media Deutschland GmbH, 2023) Baniya, Pashupati; Agrawal, Atul; Nand, Parma; Bhushan, Bharat; Hameed, Alaa Ali; Jamil, Akhtar
SDN, a rising technology within the realm of Internet of Things (IoT), has been increasingly well-received in recent times. This article presents a summary of SDN along with its different elements, advantages, and difficulties. The paper aims to provide practical solutions for introducing OpenFlow into commercial routers without hardware modifications and extending the integration of OpenFlow with legacy control protocols and control planes. In addition, the paper presents a refactoring process for migrating traditional network applications to OpenFlow-based ones, focusing on the security challenges and techniques of open technologies like SDN, OpenROADM, and SDN-based Mobile Networks (SDMN). The document also examines the advantages and possible uses of SDMN in enhancing network adaptability, streamlining network administration, and bolstering network security. The article also discusses O-RAN network innovations and difficulties, such as AI and ML workflows that are made possible by the architecture and interfaces, security concerns, and, most importantly, standardization issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Machine Learning Based Techniques for Failure Detection and Prediction in Unmanned Aerial Vehicle
(Institute of Electrical and Electronics Engineers Inc., 2024) Mustafa, Ata; Jamil, Akhtar; Hameed, Alaa Ali
Unmanned aerial vehicles (UAVs) are aircraft with-out human pilot on the board. UAVs have two flight mode: Auto and Manual. In Auto mode, UAV follows a predefined path. The path is embedded in the control system of UAV. In manual mode, human operator in ground control station controls the trajectory of the vehicle remotely. UAVs have diverse applications in military as well as civil sectors. UAVs have to operate in a variety of unseen environments. The diverse usage and uncertainty in operational environment demand safe and reliable operation. Timely identification and rectification of faults stand as a crucial requirement for the operation. One of the major cause of UAV failure is engine fault. In this paper we investigate affectiveness of machine learning techniques regarding engine fault detection and prediction. We analyzed the techniques on AirLab Failure and Anomaly (ALFA) Dataset. For fault detection we used Multi-Layer Perceptron, Random Forest, Support Vector Machine, Ada Boost, Gradient Boosting, Logistic Regression and Single Dimensional Convolutional Neural Network. We observed that Random Forest is most effective technique for fault detection with F-l Score of 0.99. Regarding fault prediction we tried LSTM and GRU based network in different settings. Gated Recurrent Unit performed best with F -1 Score of 0.99 while predicting fault four second ahead of time. © 2024 IEEE.
Comparison of machine learning based anomaly detection methods for ADS-B system
(Springer science and business media deutschland GmbH, 2025) Çevik, Nurşah; Akleylek, Sedat
This paper introduces an anomaly/intrusion detection system utilizing machine learning techniques for detecting attacks in the Automatic Detection System-Broadcast (ADS-B). Real ADS-B messages between Türkiye's coordinates are collected to train and test machine learning models. After data collection and pre-processing steps, the authors generate the attack datasets by using real ADS-B data to simulate two attack scenarios, which are constant velocity in-crease/decrease and gradually velocity increase or decrease attacks. The efficacy of five machine learning algorithms, including decision trees, extra trees, gaussian naive bayes, k-nearest neighbors, and logistic regression, is evaluated across different attack types. This paper demonstrates that tree-based algorithms consistently exhibit superior performance across a spectrum of attack scenarios. Moreover, the research underscores the significance of anomaly or intrusion detection mechanisms for ADS-B systems, highlights the practical viability of employing tree-based algorithms in air traffic management, and suggests avenues for enhancing safety protocols and mitigating potential risks in the airspace domain.
Anomaly detection with machine learning models using API calls
(Springer science and business media deutschland GmbH, 2025) Şahin, Varol; Satılmış, Hami; Yazar, Bilge Kağan; Akleylek, Sedat
Malware is malicious code developed to damage telecommunications and computer systems. Many malware causes anomaly events, such as occupying the systems’ resources, such as CPU and memory, or preventing their use. Malware causing these events can hide their destructive activities. Therefore, monitoring their behavior to detect and block such malicious software is necessary. In other words, the anomalies they cause are detected and intervened by monitoring the behaviors exhibited by malware. Various features such as application programming interface (API) calls or system calls, registry modification, and network activities constitute malware behavior. API calls and various statistical information of these calls, extracted by dynamic analysis, are considered one of the most representative features of behavior-based detection systems. Each API call in the sequences is associated with previous or subsequent API calls. Such relationships may contain patterns of destructive functions of malware. Many intrusion/anomaly detection systems are proposed, including machine and deep learning models, in which various information about API/system calls are used as features. This paper aims to evaluate the effect of various statistical information of API calls on the models in detecting anomaly events and classification performances. The anomaly detection performances of various machine learning (ML) models with known effects in the literature are examined using a dataset containing API calls. As a result of the experiments, it is seen that the models using statistical features of API calls have reached high performance in terms of precision, recall, f1-score, and accuracy metrics.
Exploring Deep Learning-based Approaches for Brain Tumor Diagnosis from MRI Images
(Institute of Electrical and Electronics Engineers Inc., 2024) Abdullah, Fasih; Jamil, Akhtar; Alazawi, Esraa Mohammed; Hameed, Alaa Ali
Brain tumors significantly impair health due to their aggressive growth, necessitating rapid and accurate detection and classification for effective treatment. Traditional methods relying on medical professionals' manual MRI analysis are time-consuming and resource-intensive. This study leverages Artificial Intelligence-based deep learning approaches to streamline and enhance the accuracy of brain tumor classification from MRI images. We evaluated five advanced deep learning models: a custom CNN, DeepTumor-Net, VGG-16, ResNet-50, and Xception. These models were applied to the 'Brain tumors 256×256' dataset, classifying brain tumors into four distinct categories: no tumor, Glioma, Meningioma, and Pituitary Tumors. These models were trained and then fine-tuned, which further refined their performance. The models were evaluated using the standard evaluation metrics, including accuracy, precision, recall, F1-Score, specificity, and Cohen's Kappa. The final results showed that high accuracies can be obtained for MRI classification using these deep learning models. Notably, ResNet-50 and VGG-16 stood out with test accuracies of 92.6 % and 92.1 %, respectively, indicating their significant potential in medical imaging analysis. © 2024 IEEE.
Classification of stroke using machine learning techniques : review study
(IEEE, 2023) Sawan, Aktham; Awad, Mohammed; Qasrawi, Radwan
Abstract—Presently, stroke is the leading cause of adult injury worldwide. The World Health Organization estimates that each year 15 million people around the world suffer a stroke. Five million of them die, and another five million are disabled for life. There is a chance to dramatically enhance the classification of strokes in the early stages. In this article, we reviewed all portable devices that produced electroencephalogram(EEG) data and all machine learning (ML) methods and deep-learning methods used to identify stroke using EEG data, and we noted that the amount of work on ML and deep learning in analyzing EEG data have increased rapidly in recent years. Such analysis has achieved greater precision compared to that conventional methods. We also discussed in this study the opportunities and key challenges for improving the accuracy of future work.
A critical evaluation on nuclear safety properties of novel cadmium oxide-rich glass containers for transportation and waste management: benchmarking with a reinforced concrete container
(FRONTIERS MEDIA SA, 2022) Almisned, Ghada; Şen Baykal, Duygu; Kılıç, Gökhan; İlik, Erkan; Zakaly, Hesham M. H.; Ene, Antoaneta; Tekin, Hüseyin Ozan
We examine the nuclear safety properties of a newly designed cadmiumoxide-rich glass container for nuclear material to a bitumen-reinforced concrete container. Individual transmission factors, detectormodelling, and energy deposition (MeV/g) in the air are calculated using MCNPX (version 2.7.0) general purpose Monte Carlo code. Two container configurations are designed with the material properties of cadmium dioxide-rich glass and Concrete + Bitument in consideration. First, individual transmission factors for 60Co and 137Cs radioisotopes are calculated. To evaluate potential environmental consequences, energy deposition amounts in the air for 60Co and 137Cs are also determined. The minimum gamma-ray transmission rates for two container types are reported for a cadmium dioxiderich glass container. In addition, the quantity of energy deposition is varied depending on the container type, with a lower value for cadmium dioxide-rich glass container. The 40% cadmium dioxide-doped glass container provides more effective safety than the Cement + Bitumen container, according to the overall findings. In conclusion, the utilization of cadmium dioxide-doped glass material along with its high transparency and advanced material properties may be a significant and effective option in areas where concrete is required to assure the safety of nuclear materials.
Gadolinium-tungsten-boron trioxide glasses: A multi-phase research on cross-sections, attenuation coefficients, build-up factors and individual transmission factors using MCNPX
(Elsevier, 2022) ALMisned, Ghada; Sen Baykal, Duygu; Ali, Fatema T.; Bilal, Ghaida; Kılıç, Gökhan; Tekin, Hüseyin Ozan
The oxide of the rare earth element gadolinium has the chemical formula Gd2O3. Also known as gadolinium sesquioxide, gadolinium trioxide, and Gadolinia, gadolinium oxide. In this study, various types of fundamental cross-sections, attenuation coefficients, build-up factors and individual transmission factors of high density gadolinium-tungsten-boron trioxide glasses with a chemical composition of (70-x)WO3-xGd2O3 –30B2O3 (where x: 17.5, 20.0, 22.5, 25.0 and 27.5 mol%) are determined using advanced Monte Carlo methods. In addition, gamma transmission factors (TFs) for a range of medical and industrial radioisotopes were calculated using MCNPX (version 2.7.0) Monte Carlo code. The investigated glasses were classified Gd17.5, Gd20.0, Gd22.5, Gd25.0, and Gd27.5 in accordance with xGd2O3. Our findings suggest that the Gd27.5 sample (with highest of Gd2O3 content mol. %) has possessed the maximum linear (µ) and mass (µ/?) attenuation coefficients at all gamma-ray energies investigated. The coded glass sample Gd27.5 is achieved the maximum effective atomic number (Zeff) and effective electron density (Neff) owing its superior attenuation properties. In terms of build-up factors, increasing the concentration of xGd2O3 in glasses is decreased the EBF and EABF values for all mean free path values (0.5–40 mfp). At a thickness of 3 cm, the lowest transmission factor (i.e., highest attenuation) was verified for all Gd17.5-Gd27.5 glasses investigated. Consequently, the Gd27.5 sample exhibits superior radiation shielding properties for a large range of photon energy and various medical and industrial radioisotope energies. © 2022 Elsevier GmbH
Impact of COVID-19 lockdown on smoking (waterpipe and cigarette) and participants' BMI across various sociodemographic groups in Arab countries in the mediterranean region
(2022) Al Sabbah, H.; Assaf, E. A.; Taha, Z.; Qasrawi, R.; İsmail, L. C.; Al Dhaheri, A. S.; Al-Mannai, M.
INTRODUCTION Tobacco smokers are at high risk of developing severe COVID-19. Lockdown was a chosen strategy to deal with the spread of infectious diseases; nonetheless, it influenced people’s eating and smoking behaviors. The main objective of this study is to determine the impact of the COVID-19 lockdown on smoking (waterpipe and cigarette) behavior and its associations with sociodemographic characteristics and body mass index. METHODS The data were derived from a large-scale retrospective cross-sectional study using a validated online international survey from 38 countries (n=37207) conducted between 17 April and 25 June 2020. The Eastern Mediterranean Region (WHO-EMR countries) data related to 10 Arabic countries that participated in this survey have been selected for analysis in this study. A total of 12433 participants were included in the analysis of this study, reporting their smoking behavior and their BMI before and during the COVID-19 lockdown. Descriptive and regression analyses were conducted to examine the association between smoking practices and the participant’s country of origin, sociodemographic characteristics, and BMI (kg/m2 ). RESULTS Overall, the prevalence rate of smoking decreased significantly during the lockdown from 29.8% to 23.5% (p<0.05). The percentage of females who smoke was higher than males among the studied population. The highest smoking prevalence was found in Lebanon (33.2%), and the lowest was in Oman (7.9%). In Egypt, Kuwait, Lebanon, and Saudi Arabia, the data showed a significant difference in the education level of smokers before and during the lockdown (p<0.05). Smokers in Lebanon had lower education levels than those in other countries, where the majority of smokers had a Bachelor’s degree. The findings show that the BMI rates in Jordan, Lebanon, Oman, and Saudi Arabia significantly increased during the lockdown (p<0.05). The highest percentages of obesity among smokers before the lockdown were in Oman (33.3%), followed by Bahrain (28.4%) and Qatar (26.4%), whereas, during the lockdown, the percentage of obese smokers was highest in Bahrain (32.1%) followed by Qatar (31.3%) and Oman (25%). According to the logistic regression model, the odds ratio of smoking increased during the pandemic, whereas the odds ratio of TV watching decreased. This finding was statistically significant by age, gender, education level, country of residence, and work status. CONCLUSIONS Although the overall rates of smoking among the studied countries decreased during the lockdown period, we cannot attribute this change in smokingbehavior to the lockdown. Smoking cessation services need to anticipate that unexpected disruptions, such as pandemic lockdowns, may be associated with changes in daily tobacco consumption. Public health authorities should promote the adoption of healthy lifestyles to reduce the long-term negative effects of the lockdown.
Algorithm selection for the team orienteering problem
(SPRINGER-VERLAG BERLIN, 2022) Mısır, Mustafa; Gunawan, Aldy; Vansteenwegen, Pieter
This work utilizes Algorithm Selection for solving the Team Orienteering Problem (TOP). The TOP is an NP-hard combinatorial optimization problem in the routing domain. This problem has been modelled with various extensions to address different real-world problems like tourist trip planning The complexity of the problem motivated to devise new algorithms. However, none of the existing algorithms came with the best performance across all the widely used benchmark instances. This fact suggests that there is a performance gap to fill. This gap can be targeted by developing more new algorithms as attempted by many researchers before. An alternative strategy is performing Algorithm Selection that will automatically choose the most appropriate algorithm for a given problem instance. This study considers the existing algorithms for the Team Orienteering Problem as the candidate method set. For matching the best algorithm with each problem instance, the specific instance characteristics are used as the instance features. An algorithm Selection approach, namely ALORS, is used to conduct the selection mission. The computational analysis based on 157 instances showed that Algorithm Selection outperforms the state-of-the-art algorithms despite the simplicity of the Algorithm Selection setting. Further analysis illustrates the match between certain algorithms and certain instances. Additional analysis showed that the time budget significantly affects the algorithms' performance.
User experience and performance evaluation of palestinian universities websites
(Institute of Electrical and Electronics Engineers Inc., 2021) Qasrawi, Aysar; Vicunapolo, Stephanny; Qasrawi, Radwan
User Experience and Performance analysis of websites are key factors in their accessibility and usability assessment. According to the human-computer interaction standards and guidelines, the user experience and performance can be affected by several factors such as page speed, web design, responsiveness, usability, accessibility, and security. Recently, the user experience and performance were conducted using automatic quality assurance tools. In this research paper, we aim to evaluate and compare the user experience and performance of Palestinian universities' websites by using the ImmuniWeb software, and JMeter software automated tools, as well as to validate the result through traditional survey user experience evaluation tool. The study evaluated response rates and security performance on a sample of 4 Palestinian university websites. The websites were also assessed in terms of efficiency, effectiveness, and user satisfaction through a comprehensive survey conducted on a sample of 84 students. The results indicated that the universities' websites had several challenges in terms of efficiency, effectiveness, and user satisfaction. Thus, Universities must upgrade their security features, use automation tools to tests their performance, and enhance their overall usability. © 2021 IEEE.
Selection-based per-instance heuristic generation for protein structure prediction of 2D HP model
(Institute of Electrical and Electronics Engineers Inc., 2021) Mısır, Mustafa
The present study aims at generating heuristics for Protein Structure Prediction represented in the 2D HP model. Protein Structure Prediction is about determining the 3-dimensional form of a protein from a given amino acid sequence. The resulting structure directly relates to the functionalities of the protein. There are a wide range of algorithms to address Protein Structure Prediction as an optimization problem. Being said that there is no an ultimate algorithm that can effectively solve PSP under varying experimental settings. Hyper-heuristics can offer a solution as high-level, problem-independent search and optimization strategies. Selection Hyper-heuristics operate on given heuristic sets that directly work on the solution space. One group of Selection Hyper-heuristics focus on automatically specify the best heuristics on-the-fly. Yet, the candidate heuristics tend to be decided, preferably a domain expert. Generation Hyper-heuristics approach differently as aiming to generate such heuristics automatically. This work introduces a automated heuristic generation strategy supporting Selection Hyper-heuristics. The generation task is formulated as a selection problem, disclosing the best expected heuristic specifically f or a given problem instance. The heuristic generation process is established as a parameter configuration problem. T he corresponding system is devised by initially generating a training data alongside with a set of basic features characterizing the Protein Structure Prediction problem instances. The data is generated discretizing the parameter configuration space o f a single heuristic. The resulting data is used to predict the best configuration of a specific heuristic used in a heuristic set under Selection Hyper-heuristics. The prediction is performed separately for each instance rather than using one setting for all the instances. The empirical analysis showed that the proposed idea offers both better and robust performance on 22 PSP instances compared to the one-for-all heuristic sets. Additional analysis linked to the selection method, ALORS, revealed insights on what makes the PSP instances hard / easy while providing dis/-similarity analysis between the candidate configurations. © 2021 IEEE.

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