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Energy efficient and robust node localization in WSNs using LSTM optimized DV hop framework to mitigate multihop localization errors

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Scientific Reports 2025年第1期15卷 1-23页

作者： Rehman, Amjad Mahmood, Tariq Alahmadi, Tahani Jaser Almasoud, Ahmed S. Saba, Tanzila 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 Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

Wireless Sensor Networks (WSNs) are distributed sensor nodes that sense data from their surroundings and relay it to a central network for processing and analysis. Sensor localization is a crucial technique in WSNs, enabling precise positions of target nodes based on environmental signal perception. However, achieving high accuracy in node localization remains a challenge. This study introduces an improved DV-Hop positioning algorithm that integrates Long Short-Term Memory (OLSTM-DVHop) networks to enhance node position predictions. The algorithm processes original data through filtering, analysis, and feature extraction to improve predicted node positions. The study analyzed errors using a standard DV-Hop algorithm and designed a robust architecture for WSN positioning. Simulation experiments validated the proposed improvements, aligning with the algorithm’s accuracy requirements. The proposed error correction mechanism addresses uneven error distribution in the DV-Hop algorithm, adjusting the positions of nodes with significant deviations, reducing errors, and enhancing the positioning process’s reliability and accuracy. The effectiveness of the proposed algorithm is evaluated by comparing it with other localization algorithms across different terrain types. The improved DV-Hop algorithm significantly reduces localization errors and offers superior accuracy, outperforming other algorithms in various experimental scenarios. © The Author(s) 2025.

关键词： DV-Hop Error correction LSTM Positioning accuracy

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Reinforcement learning based route optimization model to enhance energy efficiency in internet of vehicles

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SCIENTIFIC REPORTS 2025年第1期15卷 1-24页

作者： Hussain, Quadeer Noor, Ahmad Shukri Mohd Qureshi, Muhammad Mukhtar Li, Jianqiang Bakry, Aghiad Mahmood, Tariq Rehman, Amjad Beijing Univ Technol Fac Informat Technol Beijing 100024 Peoples R China Univ Malaysia Terengganu Fac Ocean Engn & Informat Kuala Nerus 21030 Terengganu Malaysia Abasyn Univ Dept Comp Sci Islamabad 45550 Pakistan Beijing Engn Res Ctr IoT Software & Syst Beijing 100124 Peoples R China Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci POB 1982 Dammam 31441 Saudi Arabia CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Univ Educ Fac Informat Sci Vehari Campus Vehari 61161 Pakistan

The Internet of Vehicles (IoV) transforms the automobile industry through connected vehicles with communication infrastructure that improves traffic control, safety and information, and entertainment services. However, some issues remain, like data protection, privacy, compatibility with other protocols and systems, and the availability of stable and continuous connections. Specific problems are related to energy consumption for transmitting information, distributing energy loads across the vehicle's sensors and communication units, and designing energy-efficient approaches to processing received data and making decisions in the context of the IoV environment. In the realm of IoV, we propose OptiE2ERL, an advanced Reinforcement Learning (RL) based model designed to optimize energy efficiency and routing. Our model leverages a reward matrix and the Bellman equation to determine the optimal path from source to destination, effectively managing communication overhead. The model considers critical parameters such as Remaining Energy Level (REL), Bandwidth and Interference Level (BIL), Mobility Pattern (MP), Traffic Condition (TC), and Network Topological Arrangement (NTA), ensuring a comprehensive approach to route optimization. Extensive simulations were conducted using NS2 and Python, demonstrating that OptiE2ERL significantly outperforms existing models like LEACH, PEGASIS, and EER-RL across various performance metrics. Specifically, our model extends the network lifetime, delays the occurrence of the first dead node, and maintains a higher residual energy rate. Furthermore, OptiE2ERL enhances network scalability and robustness, making it a superior choice for IoV applications. The simulation results highlight the effectiveness of our model in achieving energy-efficient routing while maintaining network performance under different scenarios. By incorporating a diverse set of parameters and utilizing RL techniques, OptiE2ERL provides a robust solution for the challeng

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Light microscopic iris classification using ensemble multi-class support vector machine

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MICROSCOPY RESEARCH AND TECHNIQUE 2021年第5期84卷 982-991页

作者： Rehman, Amjad CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh Saudi Arabia

Similar to other biometric systems such as fingerprint, face, DNA, iris classification could assist law enforcement agencies in identifying humans. Iris classification technology helps law-enforcement agencies to recognize humans by matching their iris with iris data sets. However, iris classification is challenging in the real environment due to its invertible and complex texture variations in the human iris. Accordingly, this article presents an improved Oriented FAST and Rotated BRIEF with Bag-of-Words model to extract distinct and robust features from the iris image, followed by ensemble multi-class-SVM to classify iris. The proposed methodology consists of four main steps;first, iris image normalization and enhancement;second, localizing iris region;third, iris feature extraction;finally, iris classification using ensemble multi-class support vector machine. For preprocessing of input images, histogram equalization, Gaussian mask and median filters are applied. The proposed technique is tested on two benchmark databases, that is, CASIA-v1 and iris image database, and achieved higher accuracy than other existing techniques reported in state of the art.

关键词： bag-of-words (BoW) ensemble multi-class-SVM (EMC-SVM) health risks health system ORB SIFT SURF

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Computer vision for microscopic skin cancer diagnosis using handcrafted and non-handcrafted features

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MICROSCOPY RESEARCH AND TECHNIQUE 2021年第6期84卷 1272-1283页

作者： Saba, Tanzila CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Riyadh 115861 Saudi Arabia

Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the riskiest is melanoma, although it starts in a few different ways. The patient is extremely unaware of recognizing skin malignant growth at the initial stage. Literature is evident that various handcrafted and automatic deep learning features are employed to diagnose skin cancer using the traditional machine and deep learning techniques. The current research presents a comparison of skin cancer diagnosis techniques using handcrafted and non-handcrafted features. Additionally, clinical features such as Menzies method, seven-point detection, asymmetry, border color and diameter, visual textures (GRC), local binary patterns, Gabor filters, random fields of Markov, fractal dimension, and an oriental histography are also explored in the process of skin cancer detection. Several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity, are compared on benchmark data sets to assess reported techniques. Finally, publicly available skin cancer data sets are described and the remaining issues are highlighted.

关键词： cancer conventional versus deep learning handcrafted versus non‐ handcrafted features health systems healthcare skin melanoma

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ConTrust: A Novel Context-Dependent Trust Management Model in Social Internet of Things

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IEEE ACCESS 2022年 10卷 46526-46537页

作者： Latif, Rabia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Coll Comp & Informat Sci CCIS Riyadh 11586 Saudi Arabia

The global population is around 7.4 billion people. This population density requires connectivity to improve the standard of living by transmitting and receiving variety of services. As a result, numerous forms of communication among objects are required for our everyday living demands, independent of their nature. Furthermore, to create a good relationship, every object that is regarded as associate of another object should have distinct criteria such as scalability, interoperability, and trustworthiness. Many security threats, however, have an impact on the social interaction between objects in a social internet of things (SIoT) context, including illegal admittance and suspicious behavior owing to a lack of verification architecture. Others include attempting to provide a proper viewpoint of a malicious object to earn the trust of other objects. As a result, there is a requirement for an acceptable method to check the behavior of objects such as capability, commitment, reliability, and previous job satisfaction before proceeding with any type of job assignment. This will aid in distinguishing between malicious and trustworthy objects by anticipating their upcoming behavior, allowing better judgments regarding service assignment to be made. This study proposes a context-dependent trust management technique (ConTrust) for choosing and allocating jobs in a SIoT environment. The feature-property match approach, as well as the combination of capability, commitment, and satisfaction, were utilized to increase the efficiency of trust assessment and the resolution of context-dependent difficulties. The proposed trust model considers job characteristics, object capabilities and honesty, and the impact of malicious conduct. The experimental results show that the proposed ConTrust model is viable and capable of ensuring the reliability and efficacy of SIoT service sharing between objects as compared to the benchmark models considered in this work.

关键词： Trust management Reliability Internet of Things Security Scalability Adaptation models Context modeling Context based trust management SIoT trust management

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Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types

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MICROSCOPY RESEARCH AND TECHNIQUE 2021年第7期84卷 1462-1474页

作者： Saba, Tanzila Abunadi, Ibrahim Shahzad, Mirza Naveed Khan, Amjad Rehman CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Riyadh Saudi Arabia Univ Gujrat Dept Stat Gujrat Pakistan

COVID-19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID-19 was emerged due to the SARS-CoV-2 that is highly infectious pandemic. Every country tried to control the COVID-19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time-series and machine learning models, named as random forests, K-nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID-19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top-ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out-of-sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID-19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID-19.

关键词： COVID-19 healthcare lockdown lungs infection machine learning models public health time series

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A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

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Computers, Materials & Continua 2022年第6期71卷 4785-4801页

作者： Tanzila Saba Mirza Naveed Shahzad Sonia Iqbal Amjad Rehman Ibrahim Abunadi Artificial Intelligence&Data Analytics Research Lab(AIDA) CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Statistics University of GujratGujrat50700Pakistan Department of Statistics University of SialkotSialkot51310Pakistan

Many countries developed and increased greenery in their country sights to attract international *** planning is now significantly contributing to their *** next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately *** accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal *** traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with *** contrast,the artificial neural network(ANN)model deals better with nonlinear time *** get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN ***,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and *** results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN ***,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.

关键词： SARFIMA hybrid model tourists’arrival forecasting ANN

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The EmpkinS-EKSpression Reappraisal Training Augmented With Kinesthesia in Depression: One-Armed Feasibility Study

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JMIR Formative Research 2025年 9卷 e65357页

作者： Keinert, Marie Schindler-Gmelch, Lena Rupp, Lydia Helene Sadeghi, Misha Richer, Robert Capito, Klara Eskofier, Bjoern M. Berking, Matthias Department of Clinical Psychology and Psychotherapy Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum Muenchen Neuherberg Germany

Background: Harboring dysfunctional depressogenic cognitions contributes to the development and maintenance of depression. A central goal of cognitive behavioral therapy (CBT) for depression is to invalidate such cognitions via cognitive reappraisal (CR). However, relatively low remission rates and high dropout rates in CBT demonstrate the need for further improvement. Potentially, the effects of CBT could be enhanced by addressing not only dysfunctional depressogenic cognitions but also body states associated with depression. This may be done, for example, by systematically pairing the invalidation of depressogenic cognitions with the performance of antidepressive kinesthesia. Objective: This study aimed to examine the feasibility and clinical potential of a smartphone-based cognitive restructuring task that required users to deliberately perform antidepressive kinesthesia in conjunction with the rejection of depressogenic statements and the affirmation of antidepressive statements. This feasibility study was conducted as a precursor to a large-scale randomized controlled trial. Methods: In total, 10 healthy participants engaged in a single 90-120-minute session of smartphone-based CR training. During the training, they completed 2 phases in which they were required to reject 20 depressogenic and affirm 20 antidepressive statements, respectively. Diagnostic assessments were conducted 1 week (T1) before and directly prior (T2) to the training, and again directly posttraining (T3) and at a 2-week follow-up posttraining (T4). Feasibility outcomes assessed at T3 included intervention safety recorded by study therapists, compliance, technical feasibility, usability assessed using the Short Version of the User Experience Questionnaire (UEQ-S), and acceptability assessed using the UEQ-S and self-developed items. Preliminary clinical potential was evaluated via single-item ratings of current depressed and positive mood assessed continuously during the training. Feasibility

关键词： cognitive reappraisal depression facial expression kinesthesia mobile phone smartphone-based intervention

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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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EnsembleFollower: A Hybrid Car-Following Framework Based on Hierarchical Planning and Reinforcement Learning

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IEEE Transactions on Vehicular Technology 2025年

作者： Han, Xu Chen, Xianda Zhu, Meixin Cai, Pinlong Zhou, Jianshan Chu, Xiaowen Data Science and Analytics Thrust Information Hub Guangzhou China Intelligent Transportation Thrust Systems Hub Guangzhou China Sensing and Computation for Ubiquitous Internet of Things Guangdong Provincial Key Lab of Integrated Communication China Southeast University School of Transportation Nanjing211189 China Shanghai Artificial Intelligence Laboratory Shanghai China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety Control State Key Laboratory of Intelligent Transportation Systems China Beihang University School of Transportation Science and Engineering Beijing100191 China

Car-following models have made significant contributions to our understanding of longitudinal driving behavior. However, they often exhibit limited accuracy and flexibility, as they cannot fully capture the complexity inherent in car-following processes, or may struggle in unseen scenarios due to their reliance on confined driving skills present in training data. It is worth noting that each car-following model possesses its own strengths and weaknesses depending on specific driving scenarios. Therefore, we propose EnsembleFollower, a closed-loop hierarchical planning framework for achieving human-like autonomous car-following. The EnsembleFollower framework involves a high-level Reinforcement Learning-based agent responsible for judiciously managing multiple low-level models (such as Intelligent Driver Model and Gipps model) according to the current state, either by selecting an appropriate car-following model to perform an action or by allocating different weights across all primitive models. Moreover, we propose a jerk-constrained kinematic model for more convincing microscopic traffic simulations. We evaluate the proposed method based on real-world driving data from the HighD dataset. The experimental results illustrate that EnsembleFollower yields an improved accuracy of human-like behavior and achieves effectiveness in combining hybrid models, demonstrating that our proposed framework can handle diverse driving conditions by leveraging the strengths of various car-following models. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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