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Progressive Innovations in Intelligent Systems and Data science (ICPIDS), International Conference on

作者： Neha Kaushik Harish Kumar Vinay Raj Dept. of Computer Engineering JC Bose University of Science & Technology Faridabad India Dept. of Computer Applications NIT Tiruchirappalli Tiruchirappalli India

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

Microservices is an architectural style used for developing modular and scalable applications. The distributed and de centralised architectural property of microservices applications makes it difficult to manage unpredictable workload variations and sometimes lead to degraded QoS of the applications. Accurate workload prediction is important for efficient resource allocation and maintaining high QoS of the MSA based applications. This paper proposes an LSTM-RNN based model to predict the workload of each microservice instance and after that an analysis is done to identify the effects of proactive workload management on the QoS metrics such as response time, throughput, and CPU utilisation. The results show that the workload prediction accuracy significantly improves the latency, resource utilisation and performance of microservices applications.

关键词： Technological innovation Accuracy System performance Microservice architectures Quality of service Predictive models Throughput Time measurement Resource management Time factors

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Visual Exploratory Data Analysis of the Covid-19 Pandemic in Nigeria: Two Years after the Outbreak 5

Visual Exploratory Data Analysis of the Covid-19 Pandemic in...

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5th International Conference on Information Technology for Education and Development, ITED 2022

作者： Orji, Ugochukwu. E. Ukwandu, Elochukwu Obianuju, Ezugwu. A. Ezema, Modesta. E. Ugwuishiwu, Chikaodili. H. Egbugha, Malachi. C. University of Nigeria Dept. of Computer Science Nsukka Enugu Nigeria Cardiff School of Technologies Cardiff Metropolitan University Dept. of Applied Computing and Engineering United Kingdom Ahmadu Bello University Dept of Computer Engineering Zaria Nigeria

ISBN: (纸本)9781509064229

The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far. © 2022 IEEE.

关键词： Data Analytics

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Predicting the Likelihood of a Recurrent Febrile Seizure Using Thermographic Images and Deep Learning with Rules Generation

Predicting the Likelihood of a Recurrent Febrile Seizure Usi...

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AIML-Applications for engineering & Technology (ICAET), International Conference on

作者： Pratima Patil Deepa S Deshpande Dept. of Computer Science and Engineering Jawaharlal Nehru Engineering College MGM University Chhatrapati Sambhajinagar India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Recurrent febrile seizures are a common concern in children, often triggered by rapid changes in body temperature during fever episodes. For rapid action and care, early and accurate seizure recurrence prediction is crucial. Limited techniques were developed for predicting the likelihood of a Recurrent Febrile Seizure using a Thermographic image. Several drawbacks were considered for recurrent febrile seizure prediction such as lower performance, high time consumption, and error rate. Hence, the proposed technique is introduced to provide an efficient Deep Learning (DL)-based technique. Initially, the data is gathered from datasets, and the Intra-Attentional Convolutional ResNet (InAtt-CRNet) with the Yolov5-Face model detects the face and reduces the processing time by tacking the face with a bounding box. The patient's forehead or nose temperature is monitored using the Region of Interest (ROI) finding approach following face detection and tracking in each frame. Then, the temperature is predicted using the Enhanced Convolutional Capsule Network (EC2Net). The body temperature, age, duration of fever, and family history are used to construct the clinical decision rule. The Twin-directional Gated Long Short-term memory (TdG-LSTM) is used to predict the likelihood of a subsequent febrile seizure. The proposed technique achieves 98.16% accuracy, 98.45% precision, 0.12 RMSE, and 29 seconds as processing time.

关键词： Deep learning YOLO Temperature sensors Accuracy Forehead Nose Face detection History Long short term memory Faces

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Assessment of IT Governance Maturity Level for Yemeni Telecom Sector Using COBIT 2019 Framework 1

Assessment of IT Governance Maturity Level for Yemeni Teleco...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Ghallab, Abdullatif Alswari, Tahani Alhazbi, Saleh Al-Fuhaidi, Belal Hadwan, Mohammed Al-Fahaidy, Farouk Al-Dowail, Mohammed Maqtary, Naseebah Department of Computer Science University of Science and Technology Sana’a Yemen Department of IT Open University Malaysia Sana’a Yemen Dept. of Computer Science & Eng. Qatar University Doha Qatar Department of IT Qassim University Buraydah Saudi Arabia Department of Electrical Engineering Ibb University Ibb Yemen Department of Information Systems University of Science and Technology Sana’a Yemen

ISBN: (纸本)9798331533557

Information technology (IT) plays a crucial role in business operations most organizations rely on IT resources for gaining competitive advantages and creating innovative and continuous values. This increased the importance of enterprise IT governance (EGIT) and developed the recent version of the control objectives for information and related technology (COBIT), COBIT 2019 framework. It identifies the seven components of an effective EGIT system: processes, organizational structures, information flows and items, people, skills and competencies, policies and procedures, culture, ethics and behavior, services, infrastructure, and applications. The EGIT maturity level (ITGML) assessment helps to align the EGIT with strategic objectives. This study aims to assess the ITGML of the Yemeni telecom sector using COBIT 2019 framework. A quantitative method was applied, with a 73.6% response rate (148 out of 201 questionnaires collected). The study revealed that the current ITGML is 3.48, reflecting a defined maturity level. The findings suggested that IT governance practices based on COBIT 2019 are well documented and standardized within the Yemeni telecom sector. © 2024 IEEE.

关键词： Telecommunication industry

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Advanced Image Ciphering: 1-D Group Cellular Automata and S-Box Strategies

Advanced Image Ciphering: 1-D Group Cellular Automata and S-...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Biswarup Yogi Satyabrata Roy Umashankar Rawat Shweta Redkar Dept. of Computational Sciences Brainware University Kolkata India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Data Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

This work explores advanced methodologies for encrypting images by utilizing one-dimensional Group Cellular Automata (1-D GCA) and S-Box techniques, with a focus on their pivotal role in ensuring the security of digital images. The proposed approach amalgamates Cellular Automata and S-Box mechanisms within a framework of symmetric-key cryptography to augment the strength of encryption. Through the generation of pseudorandom sequences using 1-D GCA and the utilization of S-Box for substitution, the method accomplishes efficient pixel diffusion and confusion. The assessment criteria demonstrate noteworthy enhancements: Number of Pixels Change Rate (NPCR) at 99.9864%, Unified Average Changing Intensity (UACI) at 33.6130%, Peak Signal-to-Noise Ratio (PSNR) at 9.2336 dB, and Mean Squared Error (MSE) at 0.1193. These findings underscore the robustness of the algorithm against statistical and differential attacks. The study justifies the promise of integrating GCA and S-Box strategies to reinforce cryptographic systems, proposing future avenues for enhancing computational efficiency and broadening applicability across diverse digital contexts.

关键词： PSNR Digital images Instruments Automata Information processing Robustness Encryption S Box Resilience Random sequences

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Comparative Study of Cancer Classification by Analysis of RNA-seq Gene Expression Levels 13

Comparative Study of Cancer Classification by Analysis of RN...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Amruth, A. Ramanan, R. Paul, Rhea Jayan, Sarada Thakur, Amrita Nidhin Prabhakar, T.V. Amrita School of Computing Dept. of Computer Science and Engineering India Dept. of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Chemistry Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Cancer classification is in the spotlight of research in the medical domain;genetic inheritance plays a significant role in causing this condition. Certain similarities in DNA sequence can be prevalent in individuals who have cancer. The foremost intention of this paper is to put forth efficient cancer classification techniques that provide steady and substantial accuracy. An unconventional factor that is RNA-seq gene expression levels are considered here as opposed to familiar factors such as physical features or results of various imagery techniques. The classification is conducted by analyzing RNA-seq Gene Expression levels measured by a powerful sequencing system called the Illumina Hiseq platform. A comparative study of the application of various machine learning algorithms - Decision Tree, Random Forest, SVM, KNN, Naïve Bayes, Multinomial Regression is conducted in this paper. This method significantly reduces the computational complexity as against deep neural network approach which is used with various imagery techniques. Experimental observations suggest that SVM provides higher accuracy, cross-validation score, nearly ideal AUC-ROC curves, and better performance concerning the time required to fit the model and subsequently predict the cancer type. © 2022 IEEE.

关键词： Principal component analysis

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Predicting Customer Purchase Intent Using Logistic Regression: A Data-Driven Approach

Predicting Customer Purchase Intent Using Logistic Regressio...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： C. Rajarajachozhan G. Karthika A. Rajakrishnammal M. Mano P. Nithish Kumar E. Jayahari Dept. of Electronics and Communication Engineering Saveetha Engineering College Chennai Tamil Nadu Dept. of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu Dept. of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Predicting customer purchase intent is crucial for modern businesses in order to improve engagement, refine marketing strategies, and increase revenue. Conventional rule-based systems encounter difficulties with dynamic and extensive customer data, limiting their flexibility and precision. This project employs a machine learning methodology utilizing logistic regression to forecast customer intent based on demographic, transactional, and behavioral data. The proposed model analyses input features including age, browsing history, and previous purchases, employing sophisticated data preprocessing methods such as One-Hot Encoding and feature scaling to guarantee data integrity. Through hyperparameter tuning and the assessment of critical metrics including accuracy, precision, recall, and Fl-score, the system provides dependable and actionable insights. Real-time predictive capabilities enable businesses to customize customer interactions, facilitating targeted marketing and enhancing customer experience. This project underscores the capacity of predictive analytics to tackle real-world issues, facilitating data-driven decision-making and sustainable business development.

关键词： Logistic regression Adaptation models Accuracy Pipelines Companies Predictive models Real-time systems Data models Predictive analytics Business

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Real-Time Seed Detection and Germination Analysis in Precision Agriculture: A Fusion Model With U-Net and CNN on Jetson Nano

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2023年第2期1卷 145-155页

作者： Donapati, Ramesh Reddy Cheruku, Ramalingaswamy Kodali, Prakash National Institute of Technology Warangal Department of Electronics and Communication Engineering Warangal506004 India Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Dept. of Ece Hyderabad500090 India National Institute of Technology Warangal Department of Computer Science and Engineering Warangal506004 India

Precision agriculture involves the strategic utilization of resources, precise application of inputs, and continuous monitoring of crop health with the aim of enhancing productivity and sustainability in the field of agriculture. However, seed quality is difficult since natural differences among seed batches may affect germination rates, vigor, and crop performance. Hence, in this article, a novel fusion model for seed detection and germination is proposed. The proposed model combines the U-Net and CNN architectures for seed segmentation and classification, respectively. By harnessing U-Net's capabilities in image segmentation and CNN's strengths in classification, the proposed approach enables effective seed germination analysis. In addition, the model is specifically optimized for real-time processing and applications by implementing it on the NVIDIA Jetson Nano embedded GPU platform. The proposed fusion model achieved 0.91 pixel accuracy, 0.84 intersection over union, and 0.90 precision. The proposed model demonstrated excellent predictive ability when compared with the ResNet50, Inception, and LeNet. In addition, the proposed model requires less number of trainable parameters after LeNet. Further, the proposed model tested in real time and achieved 0.26 ms latency. © 2023 IEEE.

关键词： Crops

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Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity and Lightweight Models with Knowledge Distillation 17

Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Raha, Avi Deb Gain, Mrityunjoy Saha, Sudip Kumar Rahman, Md Shohanur Adhikary, Apurba Debnath, Rameswar Bairagi, Anupam Kumar Biswas, Sujit Khulna University Khulna Bangladesh AI Innovation Lab Khulna Bangladesh United International University Dhaka Bangladesh KUET Dept. of Mechatronics Engineering Khulna Bangladesh Noakhali Science and Technology University Dept. of ICE Noakhali Bangladesh City University of London Computer Science Department EC1V 0HB United Kingdom

ISBN: (纸本)9798331531195

Monkeypox, a novel zoonotic disease akin to smallpox, necessitates prompt and accurate diagnosis for effective treatment. Conventional diagnostic techniques, such as Polymerase Chain Reaction (PCR), offer high precision but require specialized equipment and trained personnel, rendering them impractical in resource-limited settings. Existing deep learning approaches for monkeypox diagnosis have predominantly relied on single, resource-intensive models, prioritizing accuracy over deployment feasibility across diverse computing platforms. In this study, we present a resource-conscious model deployment strategy that balances diagnostic accuracy with computational efficiency, enabling precise diagnosis in both resource-rich environments, such as hospitals, and resource-constrained contexts. We utilize a pretrained ConvNeXt-B model, trained on the extensive ImageNet-22K dataset, for deployment in resource-abundant scenarios, and a SqueezeNet model, optimized for resourcelimited devices using the ImageNet-1K dataset. To enhance the performance of the lightweight SqueezeNet model without increasing computational complexity, we apply Knowledge Distillation. Experimental results demonstrate that the ConvNeXt-B model achieves an accuracy of 95.75%, which is 3.47% higher than the previous studies. Similarly, the Knowledge Distillation-enhanced SqueezeNet model attains an accuracy of 91.89%, representing a 2.32% improvement over the baseline. This dualmodel approach ensures that accurate monkeypox diagnostics are accessible across a wide range of computational environments, thereby supporting more effective outbreak management and contributing to improved public health outcomes. © 2025 IEEE.

关键词： Distillation equipment

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Advanced Optimized Counter based Hierarchal Model to Predict Cancer's Disease from Cancer Patients Neurological Features

Advanced Optimized Counter based Hierarchal Model to Predict...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Laxminarayanamma, K. Krishnaiah, R.V. Sammulal, P. Institute of Aeronautical Engineering Dept. of Information Technology Hyderabad500043 India Electronics and Communication Engineering AP Guntur522212 India Jntuh Computer Science and Engineering Kondagattu Karimnagar Telangana Jagtial505501 India

ISBN: (纸本)9781665474511

Cancer disease prediction based on neurological characteristics of cancer patients is gaining a significant research attention in recent times. The role of data in the processing and analysis of neurological features is critical, and the main goal is to efficiently extract neurological features from cancer patients' data. Random extraction of neurological features from cancer patient data is a new research initiative. Convolutional Neural Networks (CNN) is a promising approach in various healthcare applications to efficiently perform the data processing tasks. Some CNN-based approaches have been proposed to perform efficient cancer disease prediction using remotely sensed neurological features. Cancer disease extraction based on MPDCNN is one of the best CNN approaches used for extracting features and perform disease prediction from Geo-Fan-2 (GF-2) sensing cancer patient data. However, due to its sparse arrangement of optimal boundary, exact neurological features and high amount of training time, it is insufficient to investigate and automate the neurological feature extraction process from the cancer patient's data. A Novel Optimized Multi Feature Contour based Hierarchical Neural Network (NOMFCHNN) is proposed to improve the automatic neurological feature prediction process. NOMFCHNN is made up of expanding neural network features and layers related to inception, which contains the data about network localization, and this approach uses optimal and exact neurological feature matching with extended feature extraction. This method also employs contour map optimization to identify contours based on globalization of cancer patient data along with the output of the identified contour being transmitted to the next identified contour in the selected hierarchical region. Furthermore, the proposed approach evaluates the low- resolution term in cancer patient's data to gain knowledge from the cancer patient's data by obtaining the prediction results of neighbouring optimal

关键词： Feature extraction

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