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Computational Microarray Gene Selection Model Using Metaheuristic Optimization Algorithm for Imbalanced Microarrays Based on Bagging and Boosting Techniques 11th

Computational Microarray Gene Selection Model Using Metaheur...

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11th International Conference on Model and Data engineering, MEDI 2022

作者： Elden, Rana Hossam Ghoneim, Vidan Fathi Hadhoud, Marwa M. A. Al-Atabany, Walid Biomedical Engineering Department Faculty of Engineering Helwan University Cairo Egypt Information Technology and Computer Science School Nile University Giza Egypt

ISBN: (纸本)9783031215940

Genomic microarray databases encompass complex high dimensional gene expression samples. Imbalanced microarray datasets refer to uneven distribution of genomic samples among different contributed classes which can negatively affect the classification performance. Therefore, gene selection from imbalanced microarray dataset can give rise to misleading, and inconsistent nominated genes that would alter the classification performance. Such unsatisfactory classification performance is due to the skewed distribution of the samples across the microarrays toward the majority class. In this paper, we propose a modified version of Emperor Penguin Optimization (EPO) algorithm combined with Random Forest (RF) of Bagging and Boosting Classification named by EPO-RF to select the most informative genes based on classification accuracy using imbalanced microarray datasets. The modified version of EPO was built to be based on decision trees that takes in consideration the criterion of tree splitting weights to handle the imbalanced microarray datasets. Average gene expression binary values are used as a preliminary step for exploring disease trajectories with the aid of metaheuristic optimization feature selection algorithms. Results show that the proposed model revealed its superiority compared to well-known established metaheuristic optimization algorithms, e.g., Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Salp Swarm Optimization (SSO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GA’s) using several pediatric sepsis microarray datasets for patients who admitted to the Intensive Care Unit (ICU) for the first 24 h. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Gene expression

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Dimensional Measurement System Using Canny Edge Detection Algorithm

Dimensional Measurement System Using Canny Edge Detection Al...

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Bhatele, Priyanka Shivankar, Ishan Chogule, Shrey Saraswat, Shivansh Patel, Shraddha Sabut, Shreya Nale, Shreya Vishwakarma Institute of Technology Depatment of Information Technology Pune India Vishwakarma Institute of Technology Depatment of Computer Engineering Pune India Vishwakarma Institute of Technology Department of Electronics & Telecomminication Enginnering Pune India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vishwakarma Institute of Technology Depatment of Chemical Engineering Pune India

ISBN: (纸本)9798350306927

In the realm of industrial and scientific applications, accurate dimensional measurement plays a pivotal role in ensuring quality control and process optimization. This is a vital topic of computer vision problems. This study presents a project that uses reference markers to measure height and width of objects from their pictures rather than using conventional methods. Project model managed to achieve around 99.9% accuracy, whereas this was not provided in other works. © 2023 IEEE.

关键词： Python

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A Way to Design Fog Computing Model for 5G Network using Vanet 4

A Way to Design Fog Computing Model for 5G Network using Van...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Maruthamuthu, R. Patel, Neeraj Yawanikha, T. Jayasree, S. Alsalami, Zaid Subbarao, S.P.V. Madanapalle Institute of Technology & Science Computer Applications Andhrapradesh Madanapalle India Ies Institute of Technology and Management Ies University Computer Science & Engineering Department M.P Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College India Karpagam Institute of Technology Department of Information Technology Coimbatore641105 India The Islamic University of Babylon College of Technical Engineering Department of Computers Techniques Engineering Babylon Iraq Sreenidhi Institute of Science and Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350360165

Intelligent cities rely heavily on smart automobiles, providing portable computing as well as communication capabilities via a wireless link. By Mobile spontaneous groups, computer fog acts as a middleman between automobiles and the web, giving them access to storing, retrieving and abilities to network. With VANET, which stands processing the request to the vehicle takes fewer minutes since it is a latency-sensitive tech. The most well-known problems with fogged and VANETs are congestion and delays. In this study, we created a priority-based fogged approach to intelligent metro automobile journeys, which lowers fog compute time and postpone, to address such issues. 5G localised Multi-Access edge information workstations was also used to enhance the computer fog infrastructures in meeting timely and The caliber of Level standards, which has greatly reduced the latency and wait. In lieu of the study conducted utilizing simply a web-based design, we achieved a 20% reduction in data time. Additionally, we achieved a 35% reduction in CPU usage as compared to the use of a public cloud platform. © 2024 IEEE.

关键词： 5G mobile communication systems

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Enhanced Breast Cancer Relapse Prediction using Ensemble Learning Techniques 4

Enhanced Breast Cancer Relapse Prediction using Ensemble Lea...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Tripathi, Mukesh Kumar Ananya, Meka Murthy, V.N.L.N. Shashank, Tumma Reddy, Jale Subash Attaluri, Kaushal Vardhaman college of Engineering Department of Computer Science & Engineering Hyderabad India Vardhaman college of Engineering Department of Information Technology Hyderabad India Pegasystems Worldwide India Pvt Ltd Machine Learning/AI Team Hyderabad India

ISBN: (纸本)9798331540364

Breast cancer remains a significant health concern for women, and accurate prediction of recurrence is vital for effective treatment and improved outcomes. This research investigates the application of advanced ensemble learning techniques to enhance the reliability of breast cancer recurrence predictions. By combining Deep Neural Networks (DNN) and Artificial Neural Networks (ANN) with traditional machine learning methods, the proposed approach demonstrates significant improvements in accuracy, precision, sensitivity, specificity, F1-scores, and areas under the curve (AVCs). The study utilizes two breast cancer relapse datasets, UMCIO and WPBC, to evaluate the performance of the ensemble learning models. The results highlight the effectiveness of the proposed approach in accurately predicting breast cancer recurrence. This research contributes to the advancement of breast cancer diagnosis and treatment by providing a valuable tool for clinicians to assess the risk of recurrence and tailor treatment plans accordingly. © 2024 IEEE.

关键词： Prediction models

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MCFTNet: Multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12803-12818页

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Reviewing Facial Insight - A Smart Approach to Identity Unveiling Through Active Facial Patches and Multi-Task Cascaded Convolutional Networks 5

Reviewing Facial Insight - A Smart Approach to Identity Unve...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kavin, F. Pradeepa, K. Selvalakshmi, B. Anitha, P. Subashini, K. Vijayalakshmi, P. Cyient Maharashtra Navi Mumbai India St. Michael College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Sivaganga India Tagore Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sai Renganathan Engineering College Department of ECE Tamil Nadu Coimbatore India Tagore Engineering College Dept. of Information Technology Tamil Nadu Chennai India Knowledge Institute of Technology Department of Computer Science and Engineering Salem Tamil Nadu Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

This research work proposes an innovative method for enhancing facial recognition accuracy and efficiency by leveraging active facial patches and a Multi-Task Cascaded Convolutional Network (MTCNN). The review assesses the paper's contributions, methodology, experimental results, and potential impact on advancing facial recognition technology. It highlights the strengths of the proposed approach, including its clear problem statement, thorough methodology, compelling experimental results, and potential for real-world application. Additionally, the review offers recommendations for improvement, such as further exploration of potential biases and scalability considerations. Overall, Facial Insight presents a promising advancement in identity unveiling within facial recognition systems, contributing to the broader field of biometrics and computer vision. © 2024 IEEE.

关键词： Biometrics

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Proposed Different Signal Processing Tools for Efficient Optical Wireless Communications

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computers, Materials & Continua 2022年第5期71卷 3293-3318页

作者： Hend Ibrahim Abeer D.Algarni Mahmoud Abdalla Walid El-Shafai Fathi E.Abd El-Samie Naglaa F.Soliman Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh 11586Saudi Arabia

Optical Wireless Communication(OWC)is a new trend in communication systems to achieve large bandwidth,high bit rate,high security,fast deployment,and low *** basic idea of the OWC is to transmit data on unguided media with *** system requires multi-carrier modulation such as Orthogonal Frequency Division Multiplexing(OFDM).This paper studies optical OFDM performance based on Intensity Modulation with Direct Detection(IM/DD)*** system requires a non-negativity *** paper presents a framework for wireless optical OFDM system that comprises(IM/DD)with different forms,Direct Current biased Optical OFDM(DCO-OFDM),Asymmetrically Clipped Optical OFDM(ACO-OFDM),Asymmetrically DC-biased Optical OFDM(ADO-OFDM),and *** also considers channel coding as a tool for error control,channel equalization for reducing deterioration due to channel effects,and investigation of the turbulence *** evaluation results of the proposed framework reveal enhancement of *** performance of the IM/DD-OFDM system is investigated over different channel models such as AWGN,log-normal turbulence channel model,and ceiling bounce channel *** simulation results show that the BER performance of the IM/DD-OFDM communication system is enhanced while the fading strength is *** results reveal also that Hamming codes,BCH codes,and convolutional codes achieve better BER ***,two algorithms of channel estimation and equalization are considered and *** algorithms include the Least Squares(LS)and the Minimum Mean Square Error(MMSE).The simulation results show that the MMSE algorithm gives better BER performance than the LS algorithm.

关键词： Optical communication systems OWC IM/DD OFDM MMSE LS ADO-OFDM DCO-OFDM ACO-OFDM

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Performance Evaluation of LoRa 923 MHz for the Internet of Things 10

Performance Evaluation of LoRa 923 MHz for the Internet of T...

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10th IEEE information technology International Seminar, ITIS 2024

作者： Juledi, Angga Putra Huda, Moh. Khoridatul Dwi Putra, Muhammad Taufik Delsi Samsumar, Lalu Prasetyo Adi, Puput Dani Nurdiansyah, Yanuar Universitas Labuhanbatu Faculty of Science and Technology Department of Information Technology Labuhanbatu City Indonesia Malang East Java Malang Indonesia Universitas Pendidikan Indonesia Department of Computer Engineering Bandung Indonesia Universitas Teknologi Mataram Department of Information Technology Mataram City Indonesia Research Center for Telecommunication Bandung Indonesia University of Jember Faculty of Computer Science Indonesia

ISBN: (纸本)9798331521295

This research focuses on enhancing the performance of data transmission in the Internet of Things (IoT) by leveraging LoRa technology. Specifically, it utilizes the LILYGO 923 MHz LoRa32 board, which integrates the ESP32 microcontroller with a LoRa module and additional devices like a 0.96-inch OLED display. This board also features a serial converter interface, simplifying the programming process, and uses a micro USB connection for both power and uploading code. One of the goals of this research is to enable the transmission of images using LoRa, though currently, only image conversion during transmission is implemented. The study includes reliability testing of the LoRa module over a distance of 10 km. The results show that the Packet Delivery Ratio (PDR) for Spreading Factor (SF) 7 is 50%, while for SF 12, it improves to 85%. Additionally, the Received Signal Strength Indicator (RSSI) measurements at a distance of 1000 meters with 3 Non-Line-of-Sight (NLoS) conditions yielded values of -120 dBm, -90 dBm, and -78 dBm. These results highlight the potential of LoRa technology in improving IoT data transmission, especially for long-range applications. © 2024 IEEE.

关键词： Image enhancement

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On the Deployment and Operation of Correlated Data-Intensive vNF-SCs in Inter-DC EONs 32

On the Deployment and Operation of Correlated Data-Intensive...

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32nd Wireless and Optical Communications Conference, WOCC 2023

作者： Zhu, Zuqing Zhang, Liang Jabbari, Bijan School of Information Science and Technology University of Science and Technology of China China George Mason University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350337150

In this work, we study the problem of deploying and operating correlated data-intensive vNF-SCs in inter-datacenter elastic optical networks. Requiring for a set of correlated data-intensive vNF-SCs, the service completion time (SCT) of a network service is related to the maximum branch completion time of those vNF-SC branches, making the correlation awareness of importance for the problem optimization. Being aware of it, we propose a dynamic programming based optimization scheme for the deployment and operation of single vNF-SC branch and two correlation-aware service provisioning algorithms to minimize the average SCT of network services. Simulation results verified that the proposed algorithms can effectively reduce the average SCT of network services compared with a benchmark algorithm that ignores the importance of correlation awareness. © 2023 IEEE.

关键词： Dynamic programming

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A Review of Clustering Algorithms: Comparison of DBSCAN and K-mean with Oversampling and t-SNE

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Recent Patents on engineering 2022年第2期16卷 17-31页

作者： Bajal, Eshan Katara, Vipin Bhatia, Madhulika Hooda, Madhurima Department of Computer Science and Engineering Amity University Uttar Pradesh Noida India Department of Information Technology Amity University Uttar Pradesh Noida India

The two most widely used and easily implementable algorithm for clustering and classification-based analysis of data in the unsupervised learning domain are Density-Based Spatial Clustering of Applications with Noise and K-mean cluster analysis. These two techniques can handle most cases effectively when the data has a lot of randomness with no clear set to use as a parameter as in the case of linear or logistic regression algorithms. However, few papers exist that pit these two against each other in a controlled environment to observe which one reigns supreme and the conditions required for the same. In this paper, a renal adenocarcinoma dataset is analyzed and thereafter both DBSCAN and K-mean are applied on the dataset with subsequent examination of the results. The efficacy of both the techniques in this study is compared and based on them the merits and demerits observed are enumerated. Further, the interaction of t-SNE with the generated clusters are explored. © 2022 Bentham Science Publishers.

关键词： Cluster analysis

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