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A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI

computers, Materials & Continua 2022年第11期73卷 3983-4002页

作者： Abdullah AAsiri Tariq Ali Ahmad Shaf Muhammad Aamir Muhammad Shoaib Muhammad Irfan Hassan A.Alshamrani Fawaz F.Alqahtani Osama M.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia

Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to *** resonance imaging methods capture skull images that contain healthy and affected *** checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic ***,auto segmentation of the affected part is needed to facilitate ***,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor *** CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region *** proposed method was evaluated on a publicly available BraTS2020 *** statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice ***,our method achieved an accuracy value of 0.98,which is most prominent than existing ***,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.

关键词： Brain tumor support vector machine convolutional neural network BraTS classification

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RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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The GraphTempo Framework for Exploring the Evolution of a Graph through Pattern Aggregation

arXiv

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arXiv 2024年

作者： Tsoukanara, Evangelia Koloniari, Georgia Pitoura, Evaggelia Triantafillou, Peter Department of Applied Informatics University of Macedonia Greece Department of Computer Science & Engineering University of Ioannina Greece Department of Computer Science University of Warwick Coventry United Kingdom

When the focus is on the relationships or interactions between entities, graphs offer an intuitive model for many real-world data. Such graphs are usually large and change over time, thus, requiring models and strategies that explore their evolution. We study the evolution of aggregated graphs and introduce the GraphTempo model that allows temporal and attribute aggregation not only on node level by grouping individual nodes, but on a pattern level as well, where subgraphs are grouped together. Furthermore, We propose an efficient strategy for exploring the evolution of the graph based on identifying time intervals of significant growth, shrinkage or stability. Finally, we evaluate the efficiency and effectiveness of the proposed approach using three real graphs. © 2024, CC BY.

关键词： Graphic methods

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Increasing Reliability in Hybrid RF-Optical Networks: A Cross-Band Modulation Design

Increasing Reliability in Hybrid RF-Optical Networks: A Cros...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Oikonomou, Thrassos K. Tegos, Sotiris A. Diamantoulakis, Panagiotis D. Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece University of Macedonia Department of Applied Informatics Thessaloniki54636 Greece

ISBN: (纸本)9798350368369

This paper presents a novel hybrid cross-band radio frequency (RF)-optical system that utilizes coherent RF detection with M-QAM modulation and optical intensity modulation and direct detection (IM/DD) to fully utilize the diversity gains of cross-band architectures. By performing joint signal processing in the RF and optical domains, the proposed system achieves significant improvements in reliability, spectral efficiency, and data rates compared to conventional approaches. A key feature of the system is an optimized linear mapping between the RF and optical signals, which ensures seamless signal integration and improved performance. To evaluate the system, we derive the theoretical mutual information (MI) and a tight closed-form approximation for the symbol error probability (SEP) of the proposed modulation scheme. Numerical results validate the theoretical analysis and show that the proposed scheme significantly outperforms state-of-the-art cross-band modulations in terms of MI and SEP. The proposed scheme, with its low-complexity design and superior performance, is a promising candidate for next-generation wireless networks. © 2025 IEEE.

关键词： Quadrature amplitude modulation

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A survey and recent advances in black widow optimization: variants and applications

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Neural Computing and Applications 2024年 1-21页

作者： Shehab, Mohammad Shambour, Mohd Khaled Yousef Abu Hashem, Muhannad A. Al Hamad, Husam Ahmad Shannaq, Fatima Mizher, Manar Jaradat, Ghaith Sh. Daoud, Mohammad Abualigah, Laith College of Computer Sciences and Informatics Amman Arab University Amman11953 Jordan The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia College of Engineering Al Ain University Abu Dhabi United Arab Emirates Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied science research center Applied science private university Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

Artificial Intelligence techniques, such as optimization algorithms, have become essential for success in many fields. Therefore, most researchers, especially in computer and engineering sciences, focused their efforts and abilities on adapting the optimization algorithms for solving various problems. This review introduces one of the recent nature-inspired optimization algorithms, called black widow optimization (BWO). This review highlights the main procedures of BWO, its strengths and weaknesses, the growth of BWO, the variants of BWO (i.e., binary, modification, and hybridization), and the efficiency of BWO in different fields and applications (i.e., feature selection, medical, image processing, engineering, IoT, power and energy, and networks). As a result, the review seeks to assist researchers who are interested in using the BWO to address their challenges. For instance, it could help manage large pilgrim crowds, enabling smoother ritual performances and overcoming potential difficulties they encounter. Also, it suggests new possible ways of enhancing the efficiency of the basic BWO. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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Fake News Detection Regarding COVID-19 Tweets Using Machine Learning Approaches 8

Fake News Detection Regarding COVID-19 Tweets Using Machine ...

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8th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Hussin, Muhammad Hazimmuddin Mahmud, Yuzi Mohd Hanafiah, Nurul Izzah Azma Nasruddin, Zan Mohd Ariffin, Nor Hapiza Ince, Murat Senel, Fatih Ahmet Universiti Teknologi MARA School of Computing Sciences College of Computing Informatics and Mathematics Shah Alam Malaysia Sohar University Faculty of Business MIS Department Sohar Oman Isparta University of Applied Sciences Department of Computer Technologies Isparta Turkey Suleyman Demiral University Department of Computer Engineering Isparta Turkey

ISBN: (纸本)9798350315516

The pervasiveness of misinformation surrounding the COVID-19 pandemic has garnered heightened attention due to its implications, as a noteworthy proportion of the populace is being exposed to spurious and unsubstantiated narratives concerning the crisis. This research utilizes a dataset sourced from Codalab, comprising 8,560 tweets, with 4,480 labelled as real and 4,080 as fake. The research explores the effectiveness of different machine learning models, including logistic regression (LR), random forest (RF), and deep learning models such as Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). In addition to model comparison, experiments were conducted to analyze the impact of different data splits (70:30, 80:20, and 90:10), batch sizes (16, 32, and 64), and the number of epochs (5, 10, and 15) on model performance. The experiments provided insights into the optimal configurations for the models. The results showcase the model's capabilities, with high accuracy achieved across the different models. Specifically, logistic regression achieved an accuracy of 92%, random forest 91%, Bi-LSTM 93%, and CNN 95%. These findings highlight the potential of deep learning models, particularly CNN, in accurately detecting fake news from COVID-19-related tweets. © 2023 IEEE.

关键词： COVID-19

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Robust Transmission Design in Multiobjective RIS-Aided SWIPT IoT Communications

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IEEE Internet of Things Journal 2024年第10期11卷 18605-18618页

作者： Sharma, Vaibhav Allu, Raviteja Singh, Sandeep Kumar Singh, Keshav Duong, Trung Q. Tsiftsis, Theodoros A. Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Motilal Nehru National Institute of Technology Allahabad Electronics and Communication Engineering Department Prayagraj211004 India Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided Internet of Things (IoT) communications under imperfect channel state information (CSI). We formulate a multiobjective optimization problem (MOOP) to design a transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that jointly maximizes energy efficiency (EE) and harvested power (HP) under the norm-bounded CSI error model. Due to the conflicting objective functions and nonconvex nature of the above optimization problem, the MOOP is simplified using the ϵ-constraint method and subsequently adopting advanced optimization tools, such as the Dinkelbach method, S-procedure, general sign-definiteness, semidefinite programming and convex-concave procedure. Thereafter, we propose an alternating optimization-based algorithm which determines optimal TPV and PSM iteratively that jointly maximizes the EE and HP of the considered system. Through numerical simulations, we validate the robustness, optimality, convergence, accuracy, and effectiveness of our proposed algorithm. Furthermore, we assess the impact of several key parameters, such as the number of RIS elements, available transmit power at BS, and the minimum HP on the performance of the considered system. © 2014 IEEE.

关键词： Energy efficiency

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YoNet: A Neural Network for Yoga Pose Classification

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SN computer Science 2023年第2期4卷 198页

作者： Ashraf, Faisal Bin Islam, Muhammad Usama Kabir, Md Rayhan Uddin, Jasim Department of Computer Science and Engineering University of California Riverside CA United States School of Computing and Informatics University of Louisiana at Lafayette Lafayette LA United States Department of Computing Science University of Alberta Edmonton AB Canada Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff Wales United Kingdom

Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model’s performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image individually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% precision. © 2023, The Author(s).

关键词： Deep learning Image classification Neural network Pose recognition Yoga pose

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Trustworthy and Load-Balancing Routing Scheme for Satellite Services with Multi-Agent DRL

Trustworthy and Load-Balancing Routing Scheme for Satellite ...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Song, Jiaxin Ju, Ying Liu, Lei Pei, Qingqi Wu, Celimuge Jan, Mian Ahmad Mumtaz, Shahid School of Telecommunications Engineering Xidian University Xi'an710071 China Graduate School of Informatics and Engineering The University of Electro-Communications Chofu182-8585 Japan Abdul Wali Khan University Mardan Department of Computer Science Mardan23200 Pakistan Silesian University of Technology Department of Applied Informatics Akademicka16 44-100 Poland Nottingham Trent University Engineering Department Nottingham999020 United Kingdom

ISBN: (纸本)9781665494274

Massive computing tasks of various applications have been generated in 6G space-air-ground integrated networks, and need to be transmitted securely and reliably. Nevertheless, the mobility of satellites and the untrusted nodes bring new challenges to the routing scheme design in low earth orbit (LEO) satellite networks. To improve the system trust and elevate the service quality, this paper proposes a fully distributed trustworthy load-balancing routing scheme for satellite services with a multi-agent dueling double deep Q network (D3QN)-based learning algorithm. Our scheme organizes multiple agents to generate hop-by-hop routes and makes decisions based on the trust value of the nodes, which has good scalability to deploy on various satellite constellations and can meet the trust requirements of the services. Besides, we add a variable delay constraint into the load minimization objective to meet various delay-sensitive satellite quality of service (QoS) requirements. We demonstrate that the proposed scheme dramatically reduces the link queue utilization rate and enhances the system capability of handling delay-sensitive services. The packet loss rate of our scheme is 24% lower than that of the benchmark scheme when the system has 30% malicious nodes. © 2023 IEEE.

关键词： Satellites

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Secure Energy Aware Power Control in Consumer Internet of Things With Semi Grant Free NOMA

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Abbas, Sohail Fayaz, Muhammad Ghandoura, Abdulrahman Khan, Muhammad Zahid Rehman, Ateeq Ur Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah UAE Department of Computer Science and IT University of Malakand Pakistan Applied College Department of Engineering and Applied Sciences Umm Al-Qura University Makkah Saudi Arabia Department of computing Staffordshire University UK

The Consumer Internet of Things (CIoT), a key aspect of the IoT, aims to integrate smart technologies into everyday life. In order to improve the spectral efficiency and provide massive connectivity to IoT networks, non-orthogonal multiple access (NOMA) variants like semi-grant-free (SGF) NOMA are employed. This paper aims to maximize secrecy energy efficiency (EE) for SGF-NOMA enabled CIoT in the presence of untrusted users (eavesdroppers) by utilizing a single-agent multi-agent deep reinforcement learning (SAMA-DRL) algorithm to overcome scalability and expensive learning issues. Given the limited long-distance transmission capabilities of CIoT devices, which typically have low transmit power, relay nodes are used to decode and forward data from grant-free (GF) users to the base station. Moreover, to enhance the coverage for GF users, the K-nearest neighbors (KNN) algorithm is utilized to place the relay nodes at an optimal positions. Furthermore, we design a collaborative contribution reward system to discourage user (agent) laziness. Simulation results show that the proposed SAMA-DRL-based SGF-NOMA algorithm for CIoT networks is more effective than baseline algorithms, achieving a 20% increase in secrecy EE compared to DRL-based SGF-NOMA without KNN. Moreover, the proposed scheme outperforms benchmark schemes in terms of EE across different radii. Additionally, we show that the proposed algorithm with quality of service based successive interference cancellation (SIC) is more power efficient as compared to conventional SIC decoding order. IEEE

关键词： Quality of service

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