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Automated approach to predict cerebral stroke based on fuzzy inference and convolutional neural network

Multimedia Tools and Applications 2024年 1-22页

作者： Alrowais, Fadwa Jamjoom, Arwa A. Karamti, Hanen Umer, Muhammad Alsubai, Shtwai Abate, Andrea F. Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Cerebral stroke indicates a neurological impairment caused by a localized injury to the central nervous system resulting from a diminished blood supply to the brain. Today, stroke stands as a global menace linked to the premature mortality of millions of people globally. Consequently, it is crucial to simulate how different risk factors impact the incidence of strokes and artificial intelligence is emerging as a suitable tool for achieving this goal. This study aims to construct dependable learning prediction models for stroke illness. The proposed approach can handle the inherent difficulty posed by a significant imbalance in classes, where the number of stroke patients is notably smaller compared to the healthy class. The study also provides a model based on an adaptive neuro-fuzzy inference system logic and convolutional neural networks (CNN) for accurate stroke prediction. An adaptive neuro-fuzzy inference system logic approach is adopted as it incorporates the capabilities of artificial intelligence and fuzzy inference, thereby having the potential to yield superior outcomes. The efficacy of the suggested method is extensively analyzed involving machine and deep learning classifiers and considering metrics relevant to encompass both the capacity for generalization and the accuracy in predicting. The proposed fuzzy-CNN model outperforms with the most considerable accuracy of 98.97% when using the original dataset, resampled dataset, and data imputation. K-fold cross-validation also shows superior results with an average accuracy of 99.6% for five folds. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Hybrid Algorithm for Secure Routing in MANET Using Cryptographic Applications

Hybrid Algorithm for Secure Routing in MANET Using Cryptogra...

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2024 IEEE International Conference on Electronic systems and Intelligent Computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure Routing framework (CMNDSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%. © 2024 IEEE.

关键词： Encryption algorithms

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Dermoscopic Skin Lesion Classification using Color Features Based on Fine-Tuning of Resnet50 and Densenet121 Transfer Learning Models

Dermoscopic Skin Lesion Classification using Color Features ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Kujani, T. Kumar, V. Dhilip Bhuvanya, R. Kavitha, A. Sathya, T. Sathiya Priya, S. Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Chennai India Government Polytechnic College Computer Engineering Karur India Sri Sairam Engineering College Computer Science and Business Systems Chennai India

ISBN: (纸本)9798350360660

Skin cancer, which stands as the most prevalent form of human malignancy, is primarily identified through visual examination. The procedure for identifying skin lesions typically starts with an initial clinical assessment, which might be followed by dermoscopic analysis, a biopsy, and a subsequent histopathological checkup. Image-based Skin lesion classification presents a significant challenge due to the subtle and intricate variations in their appearance. Deep Convolutional Neural Network (DCNN) show promising results in managing diverse and intricate tasks involving a wide array of finely detailed object categories. Skin lesions Classification is shown in this work initially using a six-layered CNN performed on the skin cancer dataset to classify seven classes of lesions with 10015 images. Exploratory data analysis of the dataset is performed to identify any outliers and to verify the balance of samples in each class. It was observed after 50 epochs, the training accuracy was 98.03% and the testing accuracy was 67.40%. To avoid the overfitting observed in the CNN approach, the pre-trained transfer learning models, Resnet50 and Densenet121 were finetuned with data augmentation to handle the imbalance of samples present in seven classes. The results obtained prove that the Densenet121 model achieves high performance compared with the other Resnet50 model after augmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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FOOTBALLTrace: An AI-Based System for Football Player Tracking with Occlusion Detection and Trajectory Correction 11

FOOTBALLTrace: An AI-Based System for Football Player Tracki...

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11th International Conference on Sport sciences Research and Technology Support, icSPORTS 2023

作者： Mostafa, Abdelrahman H. Rushdi, Muhammad A. Basha, Tamer A. Sayed, Khaled Department of Systems and Biomedical Engineering Faculty of Engineering Cairo University Giza Egypt Department of Computer Science New Giza University Giza Egypt Department of Electrical and Computer Engineering and Computer Science University of New Haven West HavenCT United States

ISBN: (纸本)9789897586736

Data analytics have had a significant impact on tactical and workload planning in football. Football data is divided into two categories: event data, which captures on-the-ball events like passes and shots, and tracking data, which captures off-the-ball movements. However, traditional methods of collecting tracking data are expensive and inconvenient. Recently, AI solutions have emerged as low-cost and user-friendly alternatives to track players' movements from video streams. This paper introduces FOOTBALLTrace, an end-to-end AI system for tracking multiple football players from a panoramic game view. The system also incorporates a novel algorithm that detects potential occlusion events and ensures trajectory continuity for occluded players. The workflow involves five stages: panoramic view creation, player detection, player ID association, occlusion detection, and trajectory correction. The system utilizes YOLOv7 for multiple object detection and employs a pre-trained deep affinity network to assign unique IDs to players throughout the game. Occlusion detection and trajectory correction are achieved by extracting geometric features from discontinuous player trajectories. The system's performance was evaluated on full-length video data of a football game, with occlusion events manually extracted for training and testing the occlusion detection and trajectory correction algorithm. The system achieved an 87.5% trajectory correction rate for occluded trajectories. © 2023 by SCITEPRESS – science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Trajectories

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Feed-Forward Probabilistic Error Cancellation with Noisy Recovery Gates 17

Feed-Forward Probabilistic Error Cancellation with Noisy Rec...

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17th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Kurosawa, Leo Saito, Yoshiyuki Lee, Xinwei Yan, Xinjian Xie, Ningyi Cai, Dongsheng Shin, Jungpil Asai, Nobuyoshi University of Aizu School of Computer Science and Engineering Aizu-Wakamatsu Fukushima Japan Singapore Management University School of Computing and Information Systems Singapore University of Tsukuba School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9798331530471

Probabilistic Error Cancellation (PEC) aims to improve the accuracy of expectation values for observables. This is accomplished using the probabilistic insertion of recovery gates, which correspond to the inverse of errors. However, the inserted recovery gates also induce errors. Thus, it is difficult to obtain accurate expectation values with PEC since the estimator of PEC has a bias due to noise induced by recovery gates. To address this challenge, we propose an improved version of PEC that considers the noise resulting from gate insertion, called Feed-Forward PEC (FFPEC). FFPEC provides an unbiased estimator of expectation values by cancelling out the noise induced by recovery gates. We demonstrate that FFPEC yields more accurate expectation values compared to the conventional PEC method through analytical evaluations. Numerical experiments are used to evaluate analytical results. © 2024 IEEE.

关键词： Inverse problems

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A new method for human activity identification using convolutional neural networks

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International Journal of Cloud Computing 2023年第2-4期12卷 191-200页

作者： Prakash, P.S. Balakrishnan, S. Venkatachalam, K. Balasubramanian, Saravana Balaji Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tiruchengode637205 India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Department of Applied Cybernetics Faculty of Science University of Hradec Králové Hradec Králové50003 Czech Republic Department of Information Technology Lebanese French University Erbil Iraq

Body fitness monitoring applications are using mobile sensors to identify human activities. Human activity identification is a challenging task because of the wide availability of human activities. This paper proposes a novel technique that extracts the discriminative dimensions for human activity identification. Particularly, a novel technique with convolutional neural networks (CNN) is used for catching dependency. A deep convolutional neural network (DCNN) consists of two different types of layers, convolutional and pooling are used. The depth of each filter increases from left to right in the network. Three activities like walking, running, remaining still are collected from smart mobile sensors. The axis like x, y, and z information was transferred with column vector magnitude information and utilised for studying or training CNN. Experimental results show that CNN-based method achieves 93.67% accuracy than the baseline random forest approach's 89.20%. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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A Review of Key Elements for Underwater Wireless Sensor Networks

A Review of Key Elements for Underwater Wireless Sensor Netw...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Al-Dulaimi, Aymen Mohammed Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Aliesawi, Salah A. Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq

ISBN: (纸本)9798350369106

Underwater Wireless Sensor Networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications. © 2024 IEEE.

关键词： Wireless sensor networks

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Bipartite Graph-Based Recommendation systems: Introducing the Enhanced Graph Convolutional Neural Network Method for Indonesian Tourism 7

Bipartite Graph-Based Recommendation Systems: Introducing th...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Kurniadi, Felix Indra Sutomo, Rudi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia Universitas Multimedia Nusantara Information Systems Faculty of Engineering & Informatics Banten15810 Indonesia

ISBN: (纸本)9798350307504

Indonesia's tourism sector, a cornerstone of its economy, has seen significant growth with both domestic and international tourists, drawn by its diverse landscapes and cultural sites. In 2022, the country welcomed around 10 million international visitors, indicating the industry's potential. In this context, employing artificial intelligence for personalized travel recommendations is crucial. This study proposes an innovative approach, converting tourism data into a bipartite format focused on the user-place relationship. We introduce the Enhanced Graph Convolutional Neural Network (Enhanced GCN), tailored for the tourism sector's complexities. Compared to the traditional Graph Convolutional Network (GCN), the Enhanced GCN shows superior predictive accuracy, reducing the Mean Squared Error (MSE) to 2.38. This advance represents a significant step in providing more accurate, customized travel suggestions. However, the current MSE suggests room for further improvement. Future research will aim to refine the Enhanced GCN, enhancing its ability to meet and predict tourists' needs in Indonesia's dynamic tourism environment. Ultimately, the goal is to use artificial intelligence to transform how visitors experience Indonesia's cultural and natural offerings, thereby bolstering the tourism industry's growth.. © 2023 IEEE.

关键词： Recommender systems

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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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A Machine Learning Approach for Predicting Microsatellite Instability using RNA-seq

A Machine Learning Approach for Predicting Microsatellite In...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Simões, Miguel Pereira, Tania Silva, Francisco MacHado, José Carlos Oliveira, Hélder P. University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Porto Portugal University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal University of Porto Ipatimup - Institute of Molecular Pathology and Immunology i3S - Instituto de Investigação e Inovação em Saúde Fmup - Faculty of Medicine Porto Portugal

ISBN: (纸本)9798350337488

Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability. © 2023 IEEE.

关键词： Artificial Neural Networks Cancer Immunotherapy Microsatellite Instability Prediction RNA-seq Data

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