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information Technology (ACiT)

作者： Raed Alazaidah Radwan Batyha Ghassan Samara Esraa Al Henawi Maram Y. Al-Safarini Ahmad A. Samhan Ali Elrashidi Mo’Ath Alluwaici Deana Al-Omari Department of Data Science and AI Faculty IT Zarqa University Zarqa Jordan Department of Computer Science Faculty of IT Applied Science Private University Amman Jordan Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Department of Software Engineering Faculty of IT Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Diabetes is one of the most common diseases in Jordan. it is the main reason of death among Jordanian adult citizens. Worldwide, 48% of all deaths are due to Diabetes occurred before the age of 70 years. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that BayesNet and NaiveBayes are the best classification models to handle the task of the early prediction of Diabetes in Jordan

关键词： Measurement Machine learning Predictive models Diabetes Bayes methods information technology Diseases

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A Crossover Algebra for Solving Minimum Spanning Tree in Network Design with Diameter Constraints

A Crossover Algebra for Solving Minimum Spanning Tree in Net...

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2021 international Conference on Cloud computer, ioT and intelligence System, CCiiS 2021

作者： Wang, Ziyi Shi, Kai Qiao, Fuqiang Li, Huanran School of Computer Science and Engineering Tianjin University of Technology China College of Software and Communications Tianjin Sino-German University of Applied Sciences Tianjin China

The minimum spanning tree problem with diameter constraint is an important problem for many applications such as network design and reliability. The diameter constraint makes it be different from the common minimum spanning tree problem, it is difficult to get the optimal solution, even the feasible solution can not be obtained by the traditional method. To address this problem, in this paper, a new crossover algebra is designed to ensure that the crossover tree still satisfies the diameter constraint. During the crossover process, structure of the subtree will be retained, thus avoiding the problem that the new generated tree is not a feasible solution. Experimental results show the effectiveness of the improved algorithm. © Published under licence by iOP Publishing Ltd.

关键词： Algebra

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innovative Poverty Estimation through Machine Learning Approaches 18

Innovative Poverty Estimation through Machine Learning Appro...

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18th iEEE international Conference on Emerging Technologies, iCET 2023

作者： Mashhood, Ahmad Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim Khan, Ammad Aslam Khan, Bahram BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences M.A. Road Satellite Town Rawalpindi Pakistan

ISBN: (纸本)9798350328172

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. it primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. in analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges. © 2023 iEEE.

关键词： Forecasting

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Usability Evaluation of Kids’ Learning Apps

Usability Evaluation of Kids’ Learning Apps

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Business Analytics for Technology and Security (iCBATS), international Conference on

作者： Amer ibrahim Murad Al-Rajab Khalid Hamid Muhamamd Aqeel Salman Muneer Mehvish Parveen Maryam Saleem College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates Assistant Professor Computer Scienc and IT College of Engineering Abu Dhabi University Department of Computer Science Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Science NCBAE Lahore Pakistan

The main objective of learning applications is to use modern electronic technologies to impart knowledge, communicate knowledge, and Direct learning models with their learning activities in a timely and effective manner. The preponderance of the apps were designed to up-skill the kids in the fundamentals of letters and numbers. Generally, they were grounded and practice skills in nature, based on a lack of abilities in critical thinking, favoring parrot-fashion skills, and contributing to a deep conceptual comprehension of more specific concepts are unable. By using appropriate evaluation methodologies, the purposed study observes essential characteristics and aspects that affect the usability of interfaces. An evaluation of the app's features will figure out to ensure user contentment. When a kid interacts with a learning interface, the ease of use provided by the interface is validated. Because a large number of the apps were designed to train kids’ fundamentals of alphabetic and numeric. The focus of this research is to see whether self-proclaimed learning apps for kids are built according to developmentally appropriate standards to help kids in formal and informal learning settings develop socially, emotionally, and academically.

关键词： Electronic learning User interfaces Security Usability Standards Business

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Blending Lossy and Lossless Data Compression Methods to Support Health Data Streaming in Smart Cities

SSRN

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SSRN 2024年

作者： Andrade, Alexandre da Costa, Cristiano André Roehrs, Alex Muchaluat-Saade, Debora Christina Righi, Rodrigo da Rosa Applied Computing Program Universidade do Rio dos Sinos RS São Leopoldo Brazil Computer Science Department Universidade Federal Fluminense RJ Niterói Brazil Software Innovation Laboratory - SOFTWARELAB

The digital transformation process has significantly boosted the widespread adoption of telemedicine and the utilization of wearable devices for monitoring vital signs. However, implementing a system for continuous monitoring of the population's vital signs, with data being streamed from various locations within a smart city context, faces significant challenges. These challenges are related to bandwidth, latency, and storage capacity due to the large volume of data. To overcome these challenges, a common practice consists in modeling an edge-fog-cloud layered architecture. The literature lacks software solutions capable of managing the simultaneous transmission of various vital signs data, coming from individuals geographically distributed, while maintaining the ability to promptly issue alerts and generate health notifications. in this context, we propose the VSAC (Vital Sign Adaptive Compressor) model, which combines lossy and lossless data compression algorithms in a layered architecture to support healthcare demands in a smart city. The main contribution is how we blend both strategies: we first use lossy compression to collect only valuable vital sign data for each individual, applying lossless algorithms afterwards to reduce the number of bytes before sending it to higher layers. We provide a protocol that facilitates the collection of heterogeneous data distributed across different city regions, providing real-time processing. A prototype was developed to evaluate the model and its performance in various load scenarios. The results indicate that orchestrating two data compression algorithms is more efficient than conventional data reduction methods, showing gains up to 46\% when measuring the compression rate metric. © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait

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PeerJ computer science 2025年 11卷 1-42页

作者： Naz, Anam Khan, Hikmat Ullah Alsahfi, Tariq Alhajlah, Mousa Alshemaimri, Bader Daud, Ali Department of Information Technology University of Sargodha Punjab Sargodha Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Applied of Computer Science College King Saud University Riyadh Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates

Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. in this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type indicator (MBTi) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-inverse Document (TF-iDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-iDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. Subjects Human-computer interaction, Artificial intelligence, Data Mining and Machine Learning, Natural Language and Speech © 2025 xxxx

关键词： Deep learning

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iT-Security Risk Based Approach for Secure Operation of Distributed Data Platforms in Supply Chains

IT-Security Risk Based Approach for Secure Operation of Dist...

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iEEE international Conference on industrial engineering and engineering Management

作者： Marvin Voß Jonas Kallisch Maxim Runge Tobias Theus Karl-Heinz Niemann Christoph Wunck Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Emden/Leer Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing.

关键词： industries Supply chain management Federated learning Supply chains Collaboration Particle measurements Manufacturing Security Risk analysis information exchange

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A Comparative Study of the Performance of Real time databases and Big data Analytics Frameworks

A Comparative Study of the Performance of Real time database...

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international Multi-Topic iCT Conference (iMTiC)

作者： Syed Muhammd Nabeel Mustafa Muhammad Umer Farooq Syeda Sundus Zehra Jospeh Peter Terenece Noronha Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed University of Engineering and Technology Karachi Pakistan

The amelioration of information technology and the infiltration of it almost all the sectors of world have resulted in massive efflux of data. The rampant increase in the generation of data has introduced to a new term called, Big data. Big data is letting industry set the pace of future research and development. The need rose to further refine the data and get most out of it. Scientists and researchers performed analytic on the Big data to analyze it for patterns and behaviors hidden inside the data. Big data analytics aid in understanding the data through various tools, algorithms and frameworks. it allows stakeholders to make business plans and strategies according to the revelations from data. There are various tools and technologies available to perform analytics on the data. The most popular among scientists and researchers are real-time database and Big data analytics frameworks. in the paper, we evaluate the performance of the tools and technologies of Big data analytics. Mongodb and Firebase are real-time databases implemented in this study and impala and Hive are the BDA frameworks. The parameter to perform the comparison is the query execution time. it is found out that impala and Hive gave lesser execution time than Mongodb and Firebase. The query execution time of impala on 50 mb dataset and 100 mb dataset was recorded to be 4 ms and 14 ms respectively. Hence, Big data Analytics Frameworks are better performing analytics tools for Big data Analytics.

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Segmentation and Classification of Stomach Abnormalities Using Deep Learning

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computers, Materials & Continua 2021年第10期69卷 607-625页

作者： Javeria Naz Muhammad Attique Khan Majed Alhaisoni Oh-Young Song Usman Tariq Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistanl 2College of Computer Science and EngineeringUniversity of Ha’ilHa’ilSaudi Arabial 3Department of SoftwareSejong UniversitySeoulKoreaGwangjin-guKoreal 4College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabial 5Faculty of Applied Computing and TechnologyNoroff University CollegeKristiansandNorway

An automated system is proposed for the detection and classification of Gi *** proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of Gi abnormalities by deep *** first bleeding region is segmented using a hybrid *** threshold is applied to each channel extracted from the original RGB ***,all channels are merged through mutual information and pixel-based *** a result,the image is *** and deep learning features are extracted in the proposed classification *** transfer learning(TL)approach is used for the extraction of deep *** Local Binary Pattern(LBP)method is used for texture ***,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture *** selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning *** experimental process is evaluated on the basis of two datasets:Private and *** accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASiR data *** can be confirmed that the proposed method is effective in detecting and classifying Gi abnormalities and exceeds other methods of comparison.

关键词： Gastrointestinal tract contrast stretching segmentation deep learning features selection

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Reliability Analysis of a Monitoring System for Extraterrestrial Habitats

Reliability Analysis of a Monitoring System for Extraterrest...

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iEEE international Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Saurabh Band Florian Stechmann Malavika Unnikrishnan Shadi Attarha Christiane Heinicke Andreas Willig Anna Foerster dept. Sustainable Communication Networks University of Bremen Germany Center of Applied Space Technology and Microgravity University of Bremen Germany Dept. of Computer Science and Software Engineering University of Canterbury New Zealand

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

This paper presents a robust and dependable monitoring system designed for space missions where reliability is critical. Theoretically, we use the Markov chain model to evaluate the availability of the system, considering multiple instances of backup devices. The results indicate a substantial improvement in system availability with the inclusion of backup devices. The findings highlight the significance of redundancy in enhancing system dependability while considering practical constraints. Based on this insight, we design an algorithm to deploy backup devices in the system that would improve the system's availability without putting strain on the resources (e.g., channel congestion). it uses backup devices at the sensor node and gateway levels. The algorithm involves backup devices that actively monitor and take over in case of failures, enhancing the system's ability to recover lost or faulty data. We test the system in three failure scenarios: hard failures, sensor failures, and gateway failures. We perform the experiments on our hardware prototypes. Experimental results demonstrate that using backup devices significantly improves the Packet Reception Ratio (PRR) in failure scenarios. We observed an improvement of 30 - 34% in packet reception for different failure scenarios.

关键词： Wireless communication Wireless sensor networks Space missions Redundancy Prototypes Logic gates Reliability theory Reliability engineering Monitoring Strain

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