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Correction: MSegNet: A Multi-View Coupled Cross-Modal Attention Model for Enhanced MRI Brain Tumor Segmentation

International Journal of Computational intelligence Systems 2025年第1期18卷 1-2页

作者： Wang, Yu Xu, Juan Guan, Yucheng Ahmad, Faizan Mahmood, Tariq Rehman, Amjad Shandong Research Institute of Industrial Technology Shandong China Department of Endocrinology Taian City Central Hospital Taian China College of Engineering Northeastern University San Jose USA Qiqihar Medical University Heilongjiang Province China Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh Saudi Arabia Faculty of Information Sciences University of Education Vehari Campus Vehari Pakistan

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Modeling and optimizing IoT-driven autonomous vehicle transportation systems using intelligent multimedia sensors

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年 1-15页

作者： Rehman, Amjad Saba, Tanzila Haseeb, Khalid Jeon, Gwanggil Alam, Teg CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Islamia Coll Peshawar Dept Comp Sci Peshawar 25120 Pakistan Incheon Natl Univ Coll Informat Technol Dept Embedded Syst Engn Incheon 22012 South Korea Prince Sattam bin Abdulaziz Univ Al Kharj Coll Engn Dept Ind Engn Al Kharj 11924 Saudi Arabia Azad Inst Engn & Technol Chandrawal Via Bangla Bazar & BijnourNear CRPF Ca Lucknow 226002 India

With the rapid development of the Internet of Things (IoT) communication system, a huge number of devices are interconnected to sense the unpredictable environment. These systems are needed to manage a lot of varied data in addition to facilitating human lives. With the use of sensors, vehicles exchange multimedia data in smart transportation applications and subsequently give useful information to the road. Many approaches have been proposed to overcome the route optimization problems in improving the transportation system, but a major study is still needed to address intelligence in the context of autonomous processing with lower route reconstruction. data privacy and the assurance of its integrity are also crucial components of the transportation system since vehicles use next-generation technology to carry roadway information. To cope with vehicle communication through unreliable wireless infrastructure and effectively use the resources of the communication model, this work proposed a smart vehicular algorithm employing an IoT system (SVA-IoT). Firstly, it guarantees the reliability of the intelligent transportation system (ITS) with heuristic computing and decreases the overhead on the devices. Second, route optimization is carried out to balance the load on parallel routes while transmitting highway information, as a result, it increases the intelligence of smart vehicle systems. In the end, authentic devices are identified to establish a secure and trusted communication structure. The proposed technique is tested through a variety of simulated experiments, and the findings show considerable improvement over existing work.

关键词： Route optimization Transportation problem Next-generation technologies Multimedia traffic Green computing Devices security

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Human Brain Stroke Prediction using Machine Learning Methods with Synthetic Minority Oversampling Approach

Human Brain Stroke Prediction using Machine Learning Methods...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Muhammad Mujahid Noor Ayesha Ahmad Taher Azar Tanzila Saba Zeeshan Haider Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh School of Clinical Medicine Zhengzhou University Zhengzhou China College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350395839

ISBN: (纸本)9798350395846

The brain is considered to be the principal anatomical component of the human body. A stroke is a medical condition defined by cerebral injury that occurs as a consequence of blood vessel rupture within the brain. Symptoms might become apparent when there is an interruption in the brain circulation of blood and other vital chemicals. Stroke is widely acknowledged as a significant medical disorder because to its elevated mortality rates, enduring cognitive impairments, and concomitant comorbidities. The timely recognition of the numerous prodromal manifestations linked to a stroke has the capacity to attenuate its magnitude. The primary objective of this study is to utilize machine learning methodologies in order to predict the likelihood of an early-stage occurrence of a cerebral stroke. In order to assess the efficacy of the algorithm, a dataset for stroke prediction was acquired via the Kaggle platform. Various preprocessing techniques are applied to normalize and clean the dataset for stroke prediction. This research utilized many classification models including Random Forest, Ada Boost, LightGBM,, Decision Tree (DT), Logistic Regression (LR),and Support vector machine (SVM) to perform classification tasks. The Random Forest classifier has a higher level of classification accuracy in comparison to alternative machine learning classifiers, obtaining an impressive accuracy rate of 99.45%. The results of the investigation revealed that machine learning approaches demonstrated a higher level of performance in comparison to alternative methods.

关键词： Support vector machines Deep learning Accuracy Machine learning algorithms Computed tomography Stroke (medical condition) Prediction algorithms

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Automated Image Annotation With Novel Features Based on Deep ResNet50-SLT

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IEEE ACCESS 2023年 11卷 40258-40277页

作者： Adnan, Myasar Mundher Rahim, Mohd Shafry Mohd Khan, Amjad Rehman Alkhayyat, Ahmed Alamri, Faten S. Saba, Tanzila Bahaj, Saeed Ali Univ Teknol Malaysia Fac Engn Sch Comp Skudai 81310 Johor Malaysia Islamic Univ Dept Comp Tech Engn Najaf 192122 Iraq Univ Teknol Malaysia Sch Comp Skudai 81310 Johor Malaysia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Sci Dept Math Sci Riyadh 11671 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Alkharj 11942 Saudi Arabia

Due to their vast size, the growing number of digital images found in personal archives and on websites has become unmanageable, making it challenging to retrieve images from these large databases accurately. While these collections are popular due to their convenience, they often need to be equipped with proper indexing information, making it difficult for users to find what they need. One of the most significant challenges in computer vision and multimedia is image annotation, which involves labeling images with descriptive keywords. However, computers need to possess the capability to understand the essence of images in the same way that humans do, and people can only identify images based on their visual attributes rather than their deeper semantic meaning. Therefore, image annotation requires keywords to effectively communicate the contents of an image to a computer system. However, raw pixels in an image need to provide more information to generate semantic concepts, making image annotation a complex task. Unlike text annotation, where the dictionary linking words to semantics is well established, image annotation lacks a clear definition of "words" or "sentences" that can be associated with the meaning of the image, known as the semantic gap. To address this challenge, this study aimed to characterize image content meaningfully to make information retrieval easier. An improved automatic image annotation (AIA) system was proposed to bridge the semantic gap between low-level computer features and human interpretation of images by assigning one or multiple labels to images. The proposed AIA system can convert raw image pixels into semantic-level concepts, providing a clearer representation of the image content. The study combined the ResNet50 and slantlet transform with word2vec and principal component analysis with t-distributed stochastic neighbor embedding to balance precision and recall. This allowed the researchers to determine the optimal model for the pro

关键词： Feature extraction Image annotation Image color analysis Deep learning Visualization Semantics Image segmentation Electronic learning Automatic image annotation deep learning features extraction digital learning Slantlet transform technological development

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A 7L and 11L High Step-Up SCMLI Topology With Reduced Component Voltage Stress

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IEEE ACCESS 2023年 11卷 139785-139797页

作者： Ali, Jagabar Sathik Mohamed Khan, Amjad Rehman Pandurangan, Gopinath Narayanan Ponnusamy, Prem Alamri, Faten S. Bahaj, Saeed Ali SRM Inst Sci & Technol SRMIST Dept Elect & Elect Engn Kattankulathur Campus Chennai 603203 Tamil Nadu India Prince Sultan Univ CCIS Artificial Intelligence & Data Analyt Lab AIDA Riyadh 11586 Saudi Arabia St ThomasCollege Engn &Technol Dept Elect Engn Kolkata 700023 India Princess Nourah Bint Abdulrahman Univ Coll Sci Dept Math Sci Riyadh 11671 Saudi Arabia Prince Sattam Bin Abdulaziz Univ MIS Dept Coll Business Adm Alkharj 11942 Saudi Arabia

This article proposes a new capacitor-based multilevel inverter topology (CBMLI) with fewer devices and reduced voltage stress on capacitors and switches. In addition, the proposed topology can be configured as either a 7-level (7L) or 11L circuit with a maximum voltage gain of 3 and 2.5 times, respectively. Comparisons are made between the proposed topology and existing recent CBMLI topologies, and various power loss analyses are presented. The capacitance values are determined by selecting the maximum discharging period, and the associated analysis is presented. Using the simulation software MATlab/Simulink, the performance of the proposed circuit topology is validated, and the same is tested in the hardware setup. The various dynamic performance characteristics, such as loading changes, input variations, and modulation index, are validated, and the resulting data is discussed.

关键词： Capacitors Voltage Topology Switches Stress Multilevel inverters Voltage control Switched capacitor networks Boosting Switched capacitor multilevel inverter voltage boost technological development

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Intrusion Detection in Smart City Hospitals using Ensemble Classifiers 13

Intrusion Detection in Smart City Hospitals using Ensemble C...

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13th International Conference on Developments in eSystems Engineering (DeSE)

作者： Saba, Tanzila Prince Sultan Univ Riyadh Artificial Intelligence & Data Analyt Lab CCIS Riyadh Saudi Arabia

ISBN: (纸本)9781665422383

The idea of Internet of Medical Things (IOMT) is used as health intelligence in the smart hospital to assist medical staff for diagnosis and patient care. Smart hospitals play an essential role in digitizing healthcare facilities that could enhance a smart city project's scalability and efficiency. However, in the smart healthcare environment, IoMT devices face high vulnerability. Cyber-security is an essential aspect of a smart city that could achieve a secure environment for smart healthcare. Thus, the Intrusion Detection System (IDS) is used as a protection layer of communication towards cybersecurity for the latest devices and networks systems. In this paper, principal component analysis (PCA) is used for feature reduction and ensemble-based classifiers are used to predict intrusion attacks on the networks. KDDCup'99' dataset has been employed and performance is evaluated in terms of accuracy, precision, recall and F-score.

关键词： Intrusion detection IoMT Smart city Ensemble classifiers Healthcare Health systems

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MarketTrust: blockchain-based trust evaluation model for SIoT-based smart marketplaces

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SCIENTIFIC REPORTS 2023年第1期13卷 1-17页

作者： Latif, Rabia Yakubu, Bello Musa Saba, Tanzila Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt Lab AIDA Riyadh 11586 Saudi Arabia Chulalongkorn Univ Fac Sci Dept Math & Comp Sci Bangkok Thailand

Due to the significance of trust in Social Internet of Things (SIoT)-based smart marketplaces, several research have focused on trust-related challenges. Trust is necessary for a smooth connection, secure systems, and dependable services during trade operations. Recent SIoT-based trust assessment approaches attempt to solve smart marketplace trust evaluation difficulties by using a variety of direct and indirect trust evaluation techniques and other local trust rating procedures. Nevertheless, these methodologies render trust assessment very sensitive to seller dishonesty, and a dishonest seller may influence local trust scores and at the same time pose a significant trust related threats in the system. In this article, a MarketTrust model is introduced, which is a blockchain-based method for assessing trust in an IoT-based smart marketplace. It has three parts: familiarity, personal interactions, and public perception. A conceptual model, assessment technique, and a global trust evaluation system for merging the three components of a trust value are presented and discussed. Several experiments were conducted to assess the model's security, viability, and efficacy. According to results, the MarketTrust model scored a 21.99% higher trust score and a 47.698% lower average latency than both benchmark models. Therefore, this illustrates that using the proposed framework, a potential buyer can efficiently choose a competent and trustworthy resource seller in a smart marketplace and significantly reduce malicious behavior.

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Analyzing Employee Attrition: A Visual Perspective

Analyzing Employee Attrition: A Visual Perspective

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Maya John Madawi Alharbi Norah Bin Daham Samia Abu Theeb Artificial Intelligence and Data Analytics (AIDA) Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350395839

ISBN: (纸本)9798350395846

The resignation of employees from organizations has always been a burden from the management perspective. This is owing to the fact that organizations spend money on employee training, and there is no certainty regarding how fast and well the new recruits will adapt to the organization. In this paper we examined the employee attrition on department basis. Attrition was analyzed in terms of factors such as satisfaction, business travel and some job related features. Visualization-based approach was used for analyzing the data.

关键词： Training Visualization data visualization Companies data science data mining

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An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

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IEEE ACCESS 2023年 11卷 133681-133702页

作者： Jabbar, Abdul Iqbal, Sajid Tamimy, Manzoor Ilahi Rehman, Amjad Bahaj, Saeed Ali Saba, Tanzila COMSATS Univ Islamabad CUI Dept Comp Sci Main Campus Islamabad 45550 Pakistan King Faisal Univ Coll Comp Sci & Informat Technol Dept Informat Syst Al Hufuf 31982 Saudi Arabia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab AIDA CCIS Riyadh 11586 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 11942 Saudi Arabia Hadhramout Univ Coll Engn & Petr Dept Comp Engn Mukalla 50511 Hadhramout Yemen

The exponential increase in textual unstructured digital data creates significant demand for advanced and smart stemming systems. As a preprocessing stage, stemming is applied in various research fields such as information retrieval (IR), domain vocabulary analysis, and feature reduction in many natural language processing (NLP). Text stemming (TS), an important step, can significantly improve performance in such systems. Text-stemming methods developed till now could be better in their results and can produce errors of different types leading to degraded performance of the applications in which these are used. This work presents a systematic study with an in-depth review of selected stemming works published from 1968 to 2023. The work presents a multidimensional review of studied stemming algorithms i.e., methodology, data source, performance, and evaluation methods. For this study, we have chosen different stemmers, which can be categorized as 1) linguistic knowledge-based, 2) statistical, 3) corpus-based, 4) context-sensitive, and 5) hybrid stemmers. The study shows that linguistic knowledge-based stemming techniques were widely used for highly inflected languages (such as Arabic, Hindi, and Urdu) and have reported higher accuracy than other techniques. We compare and analyze the performance of various state-of-the-art TS approaches, including their issues and challenges, which are summarized as research gaps. This work also analyzes different NLP applications utilizing stemming methods. At the end, we list the future work directions for interested researchers.

关键词： Text stemming information retrieval (IR) systems text classification stemmer evaluation technological development natural language processing (NLP)

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Analysis of Airbnb Prices and Factors in Barcelona: data Visualization Approach

Analysis of Airbnb Prices and Factors in Barcelona: Data Vis...

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Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Sara Alqahtani Maya John Amerah Alghanim College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Artificial Intelligence and Data Analytics (AIDA) Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350395839

ISBN: (纸本)9798350395846

Airbnb is an online platform where people can find peer-to-peer accommodation (homestays) for different duration of stay. A major attraction of these accommodation facilities is that they are priced much less than the hotels offering similar services. This paper deals with the analysis of various factors related to Airbnb accommodation prices in Barcelona. We also carried out a spatial analysis of the homestays. The prices during the weekend were relatively higher than on weekdays. Higher-priced homestays obtained better cleanliness ratings. No apparent relationship was found between guest satisfaction and prices. The people who stayed in accommodations where the host was a super host were more satisfied with the facilities provided.

关键词： data visualization data science Peer-to-peer computing data mining Sustainable development

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