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| 1 | 基于模糊语言变量的动态目标无线网络选择技术(英文)显示文摘Even though various wireless Network Access Technologies(NATs)with different specifications and applications have been developed in the recent years,no single wireless technology alone can satisfy the anytime,anywhere,and any service wireless-access needs of mobile users.A real seamless wireless mobile environment is only realized by considering vertical and horizontal handoffs together.One of the major design issues in heterogeneous wireless networks is the support of Vertical Handoff(VHO).VHO occurs when a multi-interface enabled mobile terminal changes its Point of Attachment(PoA)from one type of wireless access technology to another,while maintaining an active session.In this paper we present a novel multi-criteria VHO algorithm,which chooses the target NAT based on several factors such as user preferences,system parameters,and traffic-types with varying Quality of Service(QoS)requirements.Two modules i.e.,VHO Necessity Estimation(VHONE)module and target NAT selection module,are designed.Both modules utilize several'weighted'users’and system’s parameters.To improve the robustness of the proposed algorithm,the weighting system is designed based on the concept of fuzzy linguistic variables. | Faisal Kaleem Abolfazl Mehbodniya Arif Islam Kang K.Yen Fumiyuki Adachi | 2013 | China Communications2013，10，1： | 6 |
| 2 | A fuzzy extension of VIKOR for target network selection in heterogeneous wireless environments 显示文摘 | Abolfazl Mehbodniya Faisal Kaleem Kang K Yen | 2013 | Physical Communication2013，7，11： | 1 |
| 3 | Sparse signal recovery with OMP algorithm using sensing measurement matrix显示文摘 | G Gui A Mehbodniya Q Wan F Adachi | 2011 | IEICE Electronics Express2011，8，5： | 1 |
| 4 | Sparse signal re- covery with OMP algorithm using sensing measurement ma- trix 显示文摘 | GUI G MEHBODNIYA A WAN Q | 2011 | IEI CE Electronics Express2011，8，5： | 1 |
| 5 | Sparse signal recovery with OMP algorithm using sensing measurement matrix显示文摘 | GUI Guan MEHBODNIYA A WAN Qun | | 0，，05： | 1 |
| 6 | Sparse LMS/F algorithms with application to adaptive system identification 显示文摘 | Gui G Mehbodniya A Adachi F | 2015 | Wireless Communications and Mobile Computing2015，15，12： | 1 |
| 7 | Dynamic target wireless network selection technique using fuzzy linguistic variables 显示文摘 | KALEEM F MEHBODNIYA A | 2013 | Communications China2013，10，1： | 1 |
| 8 | Wireless network access selection scheme for heterogeneous multimedia tr'c 显示文摘 | MEHBODNIYA A KALEEM F YEN K K | 2013 | Networks IET2013，2，4： | 1 |
| 9 | A location-aware vertical handoff algorithm for hybrid networks显示文摘 | MEHBODNIYA A AISSA S CHITIZADEH J | 2010 | Journal of com- munications2010，5，7： | 1 |
| 10 | Predicting Lumbar Spondylolisthesis: A Hybrid Deep Learning Approach显示文摘Spondylolisthesis is a chronic disease,and a timely diagnosis of it may help in avoiding surgery.Disease identification in x-ray radiographs is very challenging.Strengthening the feature extraction tool in VGG16 has improved the classification rate.But the fully connected layers of VGG16 are not efficient at capturing the positional structure of an object in images.Capsule network(CapsNet)works with capsules(neuron clusters)rather than a single neuron to grasp the properties of the provided image to match the pattern.In this study,an integrated model that is a combination of VGG16 and CapsNet(S-VCNet)is proposed.In the model,VGG16 is used as a feature extractor.After feature extraction,the output is fed to CapsNet for disease identification.A private dataset is used that contains 466 X-ray radiographs,including 186 images displaying a spine with spondylolisthesis and 280 images depicting a normal spine.The suggested model is the first step towards developing a web-based radiological diagnosis tool that can be utilized in outpatient clinics where there are not enough qualified medical professionals.Experimental results demonstrate that the developed model outperformed the other models that are used for lumbar spondylolisthesis diagnosis with 98%accuracy.After the performance check,the model has been successfully deployed on the Gradio web app platform to produce the outcome in less than 20 s. | Deepika Saravagi Shweta Agrawal Manisha Saravagi Sanjiv K.Jain Bhisham Sharma Abolfazl Mehbodniya Subrata Chowdhury Julian L.Webber | 2023 | Intelligent Automation & Soft Computing2023，37，8： | 0 |
| 11 | Deep learning:Applications,architectures,models,tools,and frameworks:A comprehensive survey显示文摘Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new knowledge.By using DL,the extraction of advanced data representations and knowledge can be made possible.Highly effective DL techniques help to find more hidden knowledge.Deep learning has a promising future due to its great performance and accuracy.We need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively.A survey on DL ways,advantages,drawbacks,architectures,and methods to have a straightforward and clear understanding of it from different views is explained in the paper.Moreover,the existing related methods are compared with each other,and the application of DL is described in some applications,such as medical image analysis,handwriting recognition,and so on. | Mehdi Gheisari Fereshteh Ebrahimzadeh Mohamadtaghi Rahimi Mahdieh Moazzamigodarzi Yang Liu Pijush Kanti Dutta Pramanik Mohammad Ali Heravi Abolfazl Mehbodniya Mustafa Ghaderzadeh Mohammad Reza Feylizadeh Saeed Kosari | 2023 | CAAI Transactions on Intelligence Technology2023，8，3： | 0 |
| 12 | A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19显示文摘The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID-19 since its outbreak.The Internet of Things(IoT)along with other technologies like Machine Learning can revolutionize the traditional healthcare system.Instead of reactive healthcare systems,IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services.In this study,a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection.The proposed framework is based on edge computing to provide personalized healthcare facilities with minimal latency,short response time,and optimal energy consumption.In this paper,the COVID-19 primary novel dataset has been used for experimental purposes employing various classification-based machine learning models.The proposed models were validated using kcross-validation to ensure the consistency of models.Based on the experimental results,our proposed models have recorded good accuracies with highest of 97.767%by Support Vector Machine.According to the findings of experiments,the proposed conceptual model will aid in the early detection and prediction of COVID-19 suspects,as well as continuous monitoring of the patient in order to provide emergency care in case of medical volatile situation. | Mahmood Hussain Mir Sanjay Jamwal Ummer Iqbal Abolfazl Mehbodniya Julian Webber Umar Hafiz Khan | 2023 | Computer Modeling in Engineering & Sciences2023，137，12： | 0 |
| 13 | Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM显示文摘In the recent days, the segmentation of Liver Tumor (LT) has beendemanding and challenging. The process of segmenting the liver and accuratelyspotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of theliver create difficulties during liver segmentation. The manual segmentation doesnot provide an accurate segmentation because the results provided by differentmedical experts can vary. Also, this manual technique requires a large numberof image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed. In this proposed Multi-AngleTexture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from itsneighbouring soft tissues with related strength. Then, the filtered images are givenas the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection(CED) technique, which segments the Liver Region (LR) from the given CTimages. An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error(VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST). Finally, the Liver Tumor Segmentation (LTS) is developed by applyingthe MAT-ACM, which accurately segments the LR from the segmented LRs. Theevaluation of the proposed method is compared with the existing LTS methodsusing various performance measures to prove the superiority of the proposedMAT-ACM method. | S.Priyadarsini Carlos Andrés Tavera Romero Abolfazl Mehbodniya P.Vidya Sagar Sudhakar Sengan | 2022 | Computer Systems Science & Engineering2022，43，12： | 0 |
| 14 | Proportional Fairness Based Energy Efficient Routing in Wireless Sensor Network显示文摘Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions,device monitoring, and collection of information. Due to limited energy resourcesavailable at SN, the primary issue is to present an energy-efficient framework andconserve the energy while constructing a route path along with each sensor node.However, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routingwith minimal energy consumption in WSN. This paper presents an energy-efficientrouting system called Energy-aware Proportional Fairness Multi-user Routing(EPFMR) framework in WSN. EPFMR is deployed in the WSN environment usingthe instance time. The request time sent for the route discovery is the foremost stepdesigned in the EPFMR framework to reduce the energy consumption rate. Theproportional fairness routing in WSN selects the best route path for the packet flowbased on the relationship between the periods of requests between different SN.Route path discovered for packet flow also measure energy on multi-user route pathusing the Greedy Instance Fair Method (GIFM). The GIFM in EPFMR developsnode dependent energy-efficient localized route path, improving the throughput.The energy-aware framework maximizes the throughput rate and performs experimental evaluation on factors such as energy consumption rate during routing,Throughput, RST, node density and average energy per packet in WSN. The RouteSearching Time (RST) is reduced using the Boltzmann Distribution (BD), and as aresult, the energy is minimized on multi-user WSN. Finally, GIFM applies aninstance time difference-based route searching on WSN to attain an optimal energyminimization system. Experimental analysis shows that the EPFMR framework canreduce the RST by 23.47% and improve the throughput by 6.79% compared withthe state-of-the-art works. | Abolfazl Mehbodniya Surbhi Bhatia Arwa Mashat Mohanraj Elangovan Sudhakar Sengan | 2022 | Computer Systems Science & Engineering2022，41，6： | 0 |
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