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A novel Self-Supervised Learning network for Binocular Disparity Estimation

Computer Modeling in Engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen salm.n a.alqahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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Systemic therapy for advanced cholangiocarcinoma:new options on the horizon

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Hepatoma Research 2020年第10期6卷 45-59页

作者： Saleh a.alqahtani Massimo Colombo Liver Transplant Center King Faisal Specialist Hospital&Research CenterRiyadh 12713Saudi Arabia Division of Gastroenterology and Hepatology Johns Hopkins UniversityBaltimoreMD 21287USA Liver Center San Raffaele HospitalMilan 20132Italy

Patients with unresectable cholangiocarcinoma(CCA)face a poor prognosis,and there are few effective treatment options for the *** standard of care for patients with locally advanced or metastatic CCA is chemotherapy with a gemcitabine-based ***,the clinical benefit obtained with these regimens is modest,with a median overall survival of about one *** CCA that is chemotherapy-refractory or recurs after first-line chemotherapy,the treatment options are even more limited,and no relevant randomized controlled data are *** recent years,molecular profiling has shed light on the molecular basis of CCA and identified subgroups of patients that might benefit from a personalized treatment *** efforts resulted in the recent FDA approval of the fibroblast growth factor receptor(FGFR)inhibitor,pemigatinib,as a second-line treatment for patients with advanced CCA harboring an FGFR2-fusion or *** other targeted agents also are under evaluation in patients with CCA,of which the isocitrate dehydrogenase inhibitor has had the most promising ***,immunotherapy is being explored as a new treatment approach for advanced CCA patients;indeed,the immune checkpoint inhibitor pembrolizumab can already be used to treat CCAs that are mismatch repair *** review is a comprehensive overview of the treatment options for CCA and offers a glimpse into what the future could hold for these patients.

关键词： Cholangiocarcinoma fibroblast growth factor receptor inhibitor isocitrate dehydrogenase inhibitor immune checkpoint inhibitor

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Viral hepatitis as a risk factor for the development of hepatocellular carcinoma

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Hepatoma Research 2020年第9期6卷 21-38页

作者： Saleh a.alqahtani Massimo Colombo Liver Transplantation Unit King Faisal Specialist Hospital&Research CentreRiyadh 12713Saudi Arabia Division of Gastroenterology and Hepatology John Hopkins UniversityBaltimoreMD 21287USA Center of Translational Research in Hepatology Humanitas HospitalRozzano 20089Italy.

Hepatocellular carcinoma(HCC)is the fourth leading global cause of tumor-related *** has a high prevalence in patients with chronic liver diseases,and it mostly results from cirrhosis caused by infection with blood-borne *** the implementation of various diagnostic and prevention strategies,the rates of new HCC cases and mortality are increasing globally due to the aging and growth of the world population as well as their increased exposure to dominant risk factors like alcohol,hepatitis B and C,and clinical correlates of metabolic *** studies indicate that sanitation practices,implementation of vaccination programs against hepatitis B,and expanded recognition and treatment of patients with chronic hepatitis B and C could greatly contribute to the eradication of viral hepatitis B and *** the availability of generic antiviral drugs could partially overcome the bottleneck represented by the lack of resources in low and middle-income countries,where viral hepatitis is the leading cause of liver cancer,the enthusiasm for the prevention of liver cancer through antiviral therapy is mitigated by the risk of cancer in many patients who are treated late in the hepatitis *** present work aimed to review in detail the various types,epidemiology,and carcinogenesis mechanisms of viral infections that are associated with a significantly increased risk for the development of HCC.

关键词： Antiviral agents hepatitis viruses hepatocellular carcinoma blood-borne hepatitis cirrhosis hepatitis B vaccine

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YOLOv2PD:An Efficient Pedestrian Detection Algorithm Using Improved YOLOv2 Model

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Computers, Materials & Continua 2021年第12期69卷 3015-3031页

作者： Chintakindi Balaram Murthy Mohammad Farukh Hashmi Ghulam Muhammad salm.n a.alqahtani Department of Electronics and Communication Engineering National Institute of TechnologyWarangal506004India Research Chair of Pervasive and Mobile Computing King Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

Real-time pedestrian detection is an important task for unmanned driving systems and video *** existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such ***,the proposed algorithm YOLOv2(“YOU OnLY LOOK OnCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road *** proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction *** addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection *** proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor *** the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller *** results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian ***,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on InRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.

关键词： Computer vision K-means clustering multi-layer feature fusion strategy pedestrian detection YOLOv2PD

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Intrusion Detection System with Customized Machine Learning Techniques for nSL-KDD Dataset

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Computers, Materials & Continua 2023年第12期77卷 4025-4054页

作者： Mohammed Zakariah salm.n a.alqahtani Abdulaziz M.Alawwad Abdullilah A.Alotaibi Department of Computer Science College of Computer and Information ScienceKing Saud UniversityRiyadh11495Saudi Arabia new Emerging Technologies and 5G network and Beyond Research Chair Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Engineering College of Computer and Information ScienceKing Saud UniversityRiyadh11495Saudi Arabia

Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based *** consuming time and resources,intrusive traffic hampers the efficient operation of network *** effective strategy for preventing,detecting,and mitigating intrusion incidents will increase productivity.A crucial element of secure network traffic is Intrusion Detection System(IDS).An IDS system may be host-based or network-based to monitor intrusive network *** unusual internet traffic has become a severe security risk for intelligent *** systems are negatively impacted by several attacks,which are slowing *** addition,networked communication anomalies and breaches must be detected using Machine Learning(ML).This paper uses the nSL-KDD data set to propose a novel IDS based on Artificial neural networks(Anns).As a result,the ML model generalizes sufficiently to perform well on untried *** nSL-KDD dataset shall be utilized for both training and *** this paper,we present a custom Ann model architecture using the Keras open-source software *** specific arrangement of nodes and layers,along with the activation functions,enhances the model’s ability to capture intricate patterns in network *** performance of the Ann is carefully tested and evaluated,resulting in the identification of a maximum detection accuracy of 97.5%.We thoroughly compared our suggested model to industry-recognized benchmark methods,such as decision classifier combinations and ML classifiers like k-nearest neighbors(Knn),Deep Learning(DL),Support Vector Machine(SVM),Long Short-Term Memory(LSTM),Deep neural network(Dnn),and *** is encouraging to see that our model consistently outperformed each of these tried-and-true techniques in all *** result underlines the effectiveness of the suggested methodology by demonstrating the Ann’s capacity to accurately assess the effectiveness of the developed strategy

关键词： Artificial neural networks intrusion detection system classification nSL-KDD dataset machine and deep-learning neural network

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Energy Efficient Green Routing for UAVs Ad-Hoc network

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Intelligent Automation & Soft Computing 2023年第7期37卷 1111-1127页

作者： M.Muthukumar Rajasekar Rangasamy Irshad Hussain salm.n a.alqahtani Department of Computer Science and Engineering Sree Sowdambika College of EngineeringTamil NaduIndia Department of Computer Science and Engineering-AI&ML School of EngineeringMalla Reddy UniversityHyderabadIndia Faculty of Electrical and Computer Engineering University of Engineering and Technology Peshawar25000PeshawarPakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia

The purpose of this article is to propose Stability-based Energy-Efficient Link-State Hybrid Routing(S-ELHR),a low latency routing proto-col that aims to provide a stable mechanism for routing in unmanned aerial vehicles(UAV).The S-ELHR protocol selects a number of network nodes to create a Connected Dominating Set(CDS)using a parameter known as the Stability Metric(SM).The SM considers the node’s energy usage,connectivity time,and node’s *** the highest SM nodes are chosen to form *** node declares a Willingness to indicate that it is prepared to serve as a relay for its neighbors,by employing its own energy state.S-ELHR is a hybrid protocol that stores only partial topological information and routing tables on CDS *** of relying on the routing information at each intermediary node,it uses source routing,in which a route is generated on-demand,and data packets contain the addresses of the nodes the packet will transit.A route recovery technique is additionally utilized,which first locates a new route to the destination before forwarding packets along *** simulation for various network sizes and mobility speeds,the efficiency of S-ELHR is *** findings demonstrate that S-ELHR performs better than Optimized Link State Routing(OLSR)and Energy Enhanced OLSR(EE-OLSR)in terms of packet delivery ratio,end-to-end delay,and energy consumption.

关键词： Connected dominatingset hybridrouting UAVAdhocnetworks link state stability

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Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3101-3123页

作者： Mohammad Mehedi Hassan salm.n a.alqahtani Mabrook S.AlRakhami Ahmed Zohier Elhendi Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Science Technology and Innovation Department King Saud UniversityRiyadh11543Saudi Arabia

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly *** its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is *** bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning *** integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional neural network(Cnn)models through transfer learning and data *** approach leverages the refined Densenet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model *** pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s *** unique combination of an optimized Deep neural network(Dnn)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic *** method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare *** innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

关键词： Explainable AI COVID-19 CT images deep learning

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A novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor networks with Hybrid Ann

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Computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad salm.n Qamar Ihsan ulHaq Amil Daraz Atif MAlamri salm.n a.alqahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor networks(WSns)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor nodes(Sns)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSns,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial neural network”(RkM-Ann)and“Delay Bound Reduced kmeans with Ann”(DBRkM-Ann)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor networks(WSns) mobile sink(MS) rendezvous point(RP) machine learning Artificial neural networks(Anns)

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nPBMT: A novel and Proficient Buffer Management Technique for Internet of Vehicle-Based DTns

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Computers, Materials & Continua 2023年第10期77卷 1303-1323页

作者： Sikandar Khan Khalid Saeed Muhammad Faran Majeed salm.n a.alqahtani Khursheed Aurangzeb Muhammad Shahid Anwar Department of Computer Science Shaheed Benazir Bhutto University SheringalDir(U)Khyber Pakhtunkhwa18200Pakistan Department of Computer Science Kohsar University MurreePunjab47150Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-si13120South Korea

Delay Tolerant networks(DTns)have the major problem of message delay in the network due to a lack of endto-end connectivity between the nodes,especially when the nodes are *** nodes in DTns have limited buffer storage for storing delayed *** instantaneous sharing of data creates a low buffer/shortage ***,buffer congestion would occur and there would be no more space available in the buffer for the upcoming *** address this problem a buffer management policy is proposed named“A novel and Proficient Buffer Management Technique(nPBMT)for the Internet of Vehicle-Based DTns”.nPBMT combines appropriate-size messages with the lowest Time-to-Live(TTL)and then drops a combination of the appropriate messages to accommodate the newly arrived *** evaluate the performance of the proposed technique comparison is done with Drop Oldest(DOL),Size Aware Drop(SAD),and Drop Larges(DLA).The proposed technique is implemented in the Opportunistic network Environment(OnE)*** shortest path mapbased movement model has been used as the movement path model for the nodes with the epidemic routing *** the simulation results,a significant change has been observed in the delivery probability as the proposed policy delivered 380 messages,DOL delivered 186 messages,SAD delivered 190 messages,and DLA delivered only 95 messages.A significant decrease has been observed in the overhead ratio,as the SAD overhead ratio is 324.37,DLA overhead ratio is 266.74,and DOL and nPBMT overhead ratios are 141.89 and 52.85,respectively,which reveals a significant reduction of overhead ratio in nPBMT as compared to existing *** network latency average of DOL is 7785.5,DLA is 5898.42,and SAD is 5789.43 whereas the nPBMT latency average is *** reveals that the proposed policy keeps the messages for a short time in the network,which reduces the overhead ratio.

关键词： Delay tolerant networks buffer management message drop policy OnE simulator nPBMT

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DnBP-CCA:A novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area network

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Computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed noradila nordin salm.n a.alqahtani Atif M.Alamri InternetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area networks (BAns) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BAns is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic next Backoff Period and Clear ChannelAssessment (DnBP-CCA) schemes to address these issues. The DnBP-CCA schemes leverage a combination ofthe Dynamic next Backoff Period (DnBP) scheme and the Dynamic next Clear Channel Assessment (DnCCA)scheme. The DnBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DnCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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