检索结果-内蒙古大学图书馆

Sustainable data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things

SENSORS 2022年第20期22卷 7876-7876页

作者： Saba, Tanzila Rehman, Amjad Haseeb, Khalid Bahaj, Saeed Ali Damasevicius, Robertas CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Islamia Coll Peshawar Dept Comp Sci Peshawar 25120 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Alkharj 11942 Saudi Arabia Silesian Tech Univ Fac Appl Math PL-44100 Gliwice Poland

The development of smart applications has benefited greatly from the expansion of wireless technologies. A range of tasks are performed, and end devices are made capable of communicating with one another with the support of artificial intelligence technology. The Internet of Things (IoT) increases the efficiency of communication networks due to its low costs and simple management. However, it has been demonstrated that many systems still need an intelligent strategy for green computing. Establishing reliable connectivity in Green-IoT (G-IoT) networks is another key research challenge. With the integration of edge computing, this study provides a Sustainable data-driven Secured optimization model (SDS-GIoT) that uses dynamic programming to provide enhanced learning capabilities. First, the proposed approach examines multi-variable functions and delivers graph-based link predictions to locate the optimal nodes for edge networks. Moreover, it identifies a sub-path in multistage to continue data transfer if a route is unavailable due to certain communication circumstances. Second, while applying security, edge computing provides offloading services that lower the amount of processing power needed for low-constraint nodes. Finally, the SDS-GIoT model is verified with various experiments, and the performance results demonstrate its significance for a sustainable environment against existing solutions.

关键词： sustainable computing optimization Internet of Things blockchain edge computing green process technological development

来源：评论

学校读者我要写书评

暂无评论

Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture

引用

SENSORS 2022年第17期22卷 6676-6676页

作者： Abunadi, Ibrahim Rehman, Amjad Haseeb, Khalid Parra, Lorena Lloret, Jaime Prince Sultan Univ Coll Comp & Informat Sci Riyadh 11586 Saudi Arabia CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Islamia Coll Peshawar Dept Comp Sci Peshawar 25000 Pakistan Univ Politen Valencia Inst Invest Gest Integrada Zonas Costeras Valencia 46022 Spain

In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks;however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network's resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works.

关键词： agriculture system Internet of Things fog system soil monitoring green energy

来源：评论

学校读者我要写书评

暂无评论

: Quantum artificial intelligence for the Automotive Industry

引用

KI - Künstliche Intelligenz 2024年第4期38卷 351-359页

作者： Stollenwerk, Tobias Bhattacharya, Somtapa Cattelan, Michele Ciani, Alessandro Compostella, Gabriele Headley, David Klepsch, Johannes Klusch, Matthias Leder, Markus Macaluso, Antonio Michielsen, Kristel Nabok, Dmytro Papanikolaou, Anestis Rausch, Alexander Schumann, Marco Skolik, Andrea Yarkoni, Sheir Wilhelm, Frank K. Institute for Quantum Computing Analytics (PGI-12) Forschungszentrum Jülich Jülich Germany Volkswagen Data:Lab Volkswagen Group Munich Germany Machine Learning Research Lab Volkswagen Group Munich Germany Mercedes-Benz AG Stuttgart Germany BMW AG München Germany German Research Center for Artificial Intelligence (DFKI) Saarbrüecken Germany Jülich Supercomputing Centre (JSC) Forschungszentrum Jülich Jülich Germany Robert Bosch GmbH Corporate Research Renningen Germany

来源：评论

学校读者我要写书评

暂无评论

Brain tumor detection and multi-classification using advanced deep learning techniques

引用

MICROSCOPY RESEARCH AND TECHNIQUE 2021年第6期84卷 1296-1308页

作者： Sadad, Tariq Rehman, Amjad Munir, Asim Saba, Tanzila Tariq, Usman Ayesha, Noor Abbasi, Rashid Univ Cent Punjab Dept Comp Sci Lahore Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Riyadh Saudi Arabia Int Islamic Univ Dept Comp Sci & Software Engn Islamabad Pakistan Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Alkharj Saudi Arabia Zhengzhou Univ Sch Clin Med Zhengzhou Henan Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China

A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, DenseNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.

关键词： brain tumor cancer health risks healthcare NASNet WHO

来源：评论

学校读者我要写书评

暂无评论

Query-by-visual-search: multimodal framework for content-based image retrieval

引用

JOURNAL OF AMBIENT intelligence AND HUMANIZED COMPUTING 2020年第11期11卷 5629-5648页

作者： Bibi, Ruqia Mehmood, Zahid Yousef, Rehan Mehmood Saba, Tanzila Sardaraz, Muhammad Rehman, Amjad Univ Engn & Technol Dept Software Engn Taxila 47050 Pakistan Univ Engn & Technol Dept Comp Engn Taxila 47050 Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh 11586 Saudi Arabia COMSATS Univ Islamabad Dept Comp Sci Attock Campus Islamabad 43600 Pakistan

Content-based image retrieval (CBIR) states the procedure of recovering images having similar visual content against a query image from image datasets. In CBIR, the selection of redundant and irrelevant features from images results in the semantic gap issue, which occurs during feature representation and machine learning process. The robust image representation for effective and efficient image retrieval mainly depends upon robust feature selection and classification, which also reduces the semantic gap problem of CBIR. This paper proposed an innovative method for effective and efficient CBIR. The method uses sparse complementary features for vigorous image representation, optimal feature selection based on locality-preserving projection, fuzzy c-means clustering, and soft label support vector machine for robust image classification. In CBIR, smaller and larger sizes of codebook improve the recall and precision (accuracy) of the system, respectively. Due to this reason, the proposed method introduces complementary features based on a larger size codebook, which is assembled using two small sizes of codebooks to increase CBIR performance. The three well-known image datasets (i.e. Corel-1000, Corel-1500, and Holidays) are used to assess the performance of the proposed method. The experimental evaluation highlights promising results as compared to recent methods of CBIR.

关键词： Content-based image retrieval Multifractal features Soft label SVM Salient objects Bag-of-words model

来源：评论

学校读者我要写书评

暂无评论

Optimized Embedded Healthcare Industry Model with Lightweight Computing Using Wireless Body Area Network

引用

WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2022年第0期2022卷

作者： Saba, Tanzila Rehman, Amjad Haseeb, Khalid Bahaj, Saeed Ali Lloret, Jaime CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia Islamia Coll Peshawar Dept Comp Sci Peshawar Pakistan Prince Sattam bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 16273 Saudi Arabia Univ Politen Valencia Valencia Spain Staffordshire Univ Stoke England

Wireless technology is offering numerous growth to develop communication systems. The Internet of Things (IoT) is combined with the sensing ecosystem to transfer and process the physical environment. Recently, IoT devices have collaborated with wireless devices to improve embedded medical applications. Many solutions are proposed to decrease the power consumption of the sensing ecosystem and support the health industry. However, optimizing the transformation of collected data with lightweight power consumption is still a burning research issue. Moreover, uncontrolled network devices and healthcare professionals are remotely accessed by such embedded systems. Thus, securing sensitive information is also a significant factor for mobile communications. Therefore, this research presents an optimized embedded healthcare industry model with lightweight computing using a wireless body area network (WBAN), aiming to lessen the control overheads and improve the power consumption in mobile e-health services. To begin, it employs an optimal learning algorithm to lower the management costs of embedded systems in order to transform and administer the electronic health record (EHR) more efficiently. Second, with the help of trustworthy gateways, it delivers a safe EHR algorithm as well as lightweight computing resources for embedded systems. The proposed model is tested with a variety of experiments and demonstrates its significant improvement over state-of-the-art techniques.

关键词： Electric power utilization

来源：评论

学校读者我要写书评

暂无评论

Radar-based Recognition of Activities of Daily Living in the Palliative Care Context Using Deep Learning

Radar-based Recognition of Activities of Daily Living in the...

引用

IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Johanna Braeunig Desar Mejdani Daniel Krauss Stefan Griesshammer Robert Richer Christian Schuessler Julia Yip Tobias Steigleder Christoph Ostgathe Bjoern M. Eskofier Martin Vossiek Department Electrical Engineering Institute of Microwaves and Photonics Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Palliative Medicine Universitätsklinikum Erlangen Erlangen Germany

The accurate detection and quantification of activities of daily life (ADL) are crucial for assessing the health status of palliative patients to allow an optimized treatment in the last phase of life. Current evaluation methods heavily rely on subjective self-reports or external observations by clinical staff, lacking objectivity. To address this limitation, we propose a radar-based approach for recognizing ADLs in a palliative care context. In our proof of concept study, we recorded five different ADLs relevant to palliative care, all occurring within a hospital bed, from N=14 healthy participants (57% women, aged 28.6 ± 5.3years). All movements were recorded using two frequency-modulated continuous wave radar systems measuring velocity, range, and angle. A convolutional neural network combined with long short-term memory achieved a classification accuracy of 99.8 ± 0.4% across five cross-validation folds. Furthermore, we compare our initial approach, which takes into account all dimensions of the available radar data, to a simplified version, where only velocity information over time is fed into the network. While these results demonstrate the high potential of radar-based sensing to automatically detect and quantify activities in a palliative care context, future work is still necessary to assess the applicability to real-world hospital scenarios.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Secure Edge-Based Energy Management Protocol in Smart Grid Environments with Correlation Analysis

引用

SENSORS 2022年第23期22卷 9236-9236页

作者： Rehman, Amjad Haseeb, Khalid Jeon, Gwanggil Bahaj, Saeed Ali Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh 11586 Saudi Arabia Islamia Coll Peshawar Dept Comp Sci Peshawar 25120 Pakistan Incheon Natl Univ Dept Embedded Syst Engn Incheon 22012 South Korea Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Alkharj 11942 Saudi Arabia

For the monitoring and processing of network data, wireless systems are widely used in many industrial applications. With the assistance of wireless sensor networks (WSNs) and the Internet of Things (IoT), smart grids are being explored in many distributed communication systems. They collect data from the surrounding environment and transmit it with the support of a multi-hop system. However, there is still a significant research gap in energy management for IoT devices and smart sensors. Many solutions have been proposed by researchers to cope with efficient routing schemes in smart grid applications. But, reducing energy holes and offering intelligent decisions for forwarding data are remain major problems. Moreover, the management of network traffic on grid nodes while balancing the communication overhead on the routing paths is an also demanding challenge. In this research work, we propose a secure edge-based energy management protocol for a smart grid environment with the support of multi-route management. It strengthens the ability to predict the data forwarding process and improves the management of IoT devices by utilizing a technique of correlation analysis. Moreover, the proposed protocol increases the system's reliability and achieves security goals by employing lightweight authentication with sink coordination. To demonstrate the superiority of our proposed protocol over the chosen existing work, extensive experiments were performed on various network parameters.

关键词： smart grid machine learning communication privacy conservation cloud storage

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Identifying Patients With PTSD Utilizing Resting-State fMRI data and Neural Network Approach

引用

IEEE ACCESS 2021年 9卷 107941-107954页

作者： Shahzad, Mirza Naveed Ali, Haider Saba, Tanzila Rehman, Amjad Kolivand, Hoshang Bahaj, Saeed Ali Univ Gujrat Dept Stat Gujrat 50700 Pakistan Prince Sultan Univ Artificial Intelligence & Data Analyt Lab AIDA CCIS Riyadh 12435 Saudi Arabia Liverpool John Moores Univ Sch Comp Sci & Math Liverpool L2 2QP Merseyside England Staffordshire Univ Sch Comp & Digital Technol Stoke On Trent ST4 2DE Staffs England Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Alkharj 16278 Saudi Arabia

Purpose: The primary aim of the study is to identify the existence of post-traumatic stress disorder (PTSD) in an individual and to detect the dominance level of each affected brain region in PTSD using rs-fMRI data. This will assist the psychiatrists and neurologists to distinguish impartially between PTSD individuals and healthy controls for the brain-based treatment of PTSD. Methods: Twenty-eight individuals (14 with PTSD, 14 healthy controls) were assessed to obtain rs-fMRI data of their six brain regions-of-interest. The rs-fMRI data analyzed by the artificial Neural Network (ANN), adopting the training-validation-testing approach to classify PTSD and to identify the most affected brain region due to PTSD. The classification accuracy is justified by a variety of different methods and metrics. Results: Three ANN models were established to attain the study's purpose using the susceptible regions in the right, left, and both hemispheres and the classification accuracy of ANN models achieved 79%, 93.5%, and 94.5%, respectively. The prediction accuracy even increased in the independent holdout sample using trained models. The developed models are reliable, intellectually attractive, and generalize. Additionally, the most dominant region in the PTSD individuals was the left hippocampus and the least was the right hippocampus. Conclusion: The present investigation achieved high classification accuracy and identified the brain regions that highly contributed to differentiating PTSD individuals from healthy controls. The results indicated that the left hippocampus is the most affected brain region in PTSD individuals. Therefore, our findings are helpful for practitioners for diagnostic, medication, and therapy of the affected brain regions by knowing the strength of infected regions.

关键词： Brain modeling Hippocampus Brain Support vector machines Functional magnetic resonance imaging Training Predictive models artificial neural network amygdala calibration plot health-care hippocampus medial prefrontal cortex PTSD psychological harm rs-fMRI healthcare

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：