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7th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Suchithra, M. Shashwat, Kumar Shoaib Khan, Mohammed Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9783031699856

Length of stay (LoS) in a hospital is an important metric in the healthcare management system, with profound implications for resource allocation, patient outcomes, and cost reduction. This paper reviews the literature on approaches for predicting hospital length of stay with a R2 score of 96.14 per cent using Light Gradient Boosting Machine regressor. We critically assess the merits and limitations of various methods and propose a unified framework for the generalized prediction of length of stay. Our framework includes investigating the types of routinely collected data and recommendations for robust knowledge modelling. © IFIP International Federation for Information Processing 2024.

关键词： Adaptive boosting

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A Reinforcement Learning Based Approach to Partition Testing

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计算机科学技术学报（英文版） 2025年第1期40卷 99-118页

作者：孙昌爱肖明俊代贺鹏刘淮 1School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China 2Department of Computing Technologies Swinburne University of Technology Hawthorn VIC 3122 Australia

Partition testing is one of the most fundamental and popularly used software testing *** first di-vides the input domain of the program under test into a set of disjoint partitions,and then creates test cases based on these *** by the theory of software cybernetics,some strategies have been proposed to dynamically se-lect partitions based on the feedback information gained during *** basic intuition of these strategies is to assign higher probabilities to those partitions with higher fault-detection potentials,which are judged and updated mainly ac-cording to the previous test *** a feedback-driven mechanism can be considered as a learning process—it makes decisions based on the observations acquired in the test ***,advanced learning techniques could be leveraged to empower the smart partition selection,with the purpose of further improving the effectiveness and efficiency of partition *** this paper,we particularly leverage reinforcement learning to enhance the state-of-the-art adaptive partition testing *** algorithms,namely RLAPT_Q and RLAPT_S,have been developed to implement the proposed *** studies have been conducted to evaluate the performance of the proposed approach based on seven object programs with 26 *** experimental results show that our approach outperforms the existing partition testing techniques in terms of the fault-detection capability as well as the overall testing *** study demonstrates the applicability and effectiveness of reinforcement learning in advancing the performance of software testing.

关键词： partition testing reinforcement learning temporal-difference learning intelligent software engineering

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Hausa Handwriting Character Recognition Using CNN and Tesseract 2

Hausa Handwriting Character Recognition Using CNN and Tesser...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Dauda, Muhammad Khamis Musa, Umar Al-Barhamtoshy, Hassanin Faculty of Computing and Information Technology Department of Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

The Hausa language, spoken by over 64 million people, is a vital part of West Africa's cultural and social fabric. Despite its importance, handwritten Hausa script recognition has little concern due to its tonal nature, unique characters, and limited datasets. This study tackles these challenges by creating a dataset and evaluating two models commonly used in handwritten recognition: a Convolutional Neural Network (CNN) and Tesseract Optical Character Recognition (OCR). The custom CNN, trained on data from 30 volunteers with varied writing styles, achieved an accuracy of 96%, with strong precision, recall, and F1-score metrics. In contrast, Tesseract OCR struggled, particularly with Hausa-specific characters like , , , and , achieving lowest accuracy for these classes. Although it had non-zero recognition in some characters, its performance was inconsistent, indicating difficulties in generalization. This work provides a scalable solution for handwritten Hausa character recognition, contributing to research on low-resource African languages and paving the way for better access to Hausa handwritten literature and integration into natural language processing applications. © 2025 IEEE.

关键词： Optical character recognition

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A Multi-path Droplet Routing Protocol for Digital Microfluidic Biochip

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第7期48卷 95-102页

作者： Swain, Jyotiranjan Pyne, Sumanta School of Computing and Information Technology REVA University Bangalore India Department of Computer Science and Engineering National Institute of Technology Rourkela India

Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It involves challenges like moving droplets without violating the fluid constraints. This article proposed a multi-path droplet routing protocol. Multiple routes are explored and validated using special packets. These routes are then classified based on user-defined heuristics. A single route is sequentially selected for each droplet. The routes are then compacted to generate a parallel moving sequence. The simulation result shows 2.087% and 4.952% improvement in the latest arrival time for free and virtual topology, respectively. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Biochips

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AMAD:Adaptive Mapping Approach for Datacenter Networks,an Energy-Friend Resource Allocation Framework via Repeated Leader Follower Game

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Computers, Materials & Continua 2024年第9期80卷 4577-4601页

作者： Ahmad Nahar Quttoum Muteb Alshammari Department of Computer Engineering Faculty of EngineeringThe Hashemite UniversityZarqa13133Jordan Department of Information Technology Faculty of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

Cloud Datacenter Network(CDN)providers usually have the option to scale their network structures to allow for far more resource capacities,though such scaling options may come with exponential costs that contradict their utility ***,besides the cost of the physical assets and network resources,such scaling may also imposemore loads on the electricity power grids to feed the added nodes with the required energy to run and cool,which comes with extra costs ***,those CDNproviders who utilize their resources better can certainly afford their services at lower price-units when compared to others who simply choose the scaling *** utilization is a quite challenging process;indeed,clients of CDNs usually tend to exaggerate their true resource requirements when they lease their *** providers are committed to their clients with Service Level Agreements(SLAs).Therefore,any amendment to the resource allocations needs to be approved by the clients *** this work,we propose deploying a Stackelberg leadership framework to formulate a negotiation game between the cloud service providers and their client *** this,the providers seek to retrieve those leased unused resources from their *** is not expected from the clients,and they may ask high price units to return their extra resources to the provider’s ***,to motivate cooperation in such a non-cooperative game,as an extension to theVickery auctions,we developed an incentive-compatible pricingmodel for the returned ***,we also proposed building a behavior belief function that shapes the way of negotiation and compensation for each *** to other benchmark models,the assessment results showthat our proposed models provide for timely negotiation schemes,allowing for better resource utilization rates,higher utilities,and grid-friend CDNs.

关键词： Data center networks energy-aware resource management resource utilization game-theory mechanisms

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Advancing Breast Cancer Prediction and Early Detection with Advanced Deep Learning Models 4

Advancing Breast Cancer Prediction and Early Detection with ...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Garg, Parth Sharma, Pulkit Prakash, U.M. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India

ISBN: (纸本)9798350343670

Breast cancer is a common disease that predominantly affects women worldwide, with the potential to be fatal. Histopathologists utilize various criteria to examine tissue samples under a microscope for diagnosing cancer through histopathological imaging. However, pathologists often disagree during the diagnostic process. To address this issue, Deep Neural Networks are employed for supervised classification, given the considerable time required for manual categorization of histological pictures. In our classification task, we utilized the Breast Histology dataset, comprising 241 training and 21 test pictures. Effective preprocessing of the dataset is crucial for successful categorization. Images were classified into four categories (Normal, Benign, Insitu carcinoma, and Invasive cancer) using transfer learning based on GoogleNet, AlexNet, and ResNet. Notably, our method achieved the highest accuracy, with ResNet reaching 97.11%. Ongoing research aims to enhance the technique's effectiveness and reduce reliance on human involvement. Furthermore, this promising approach can be adapted to automate additional medical imaging modalities, potentially advancing the automation of various aspects of medical diagnosis through improvements to the suggested framework. © 2024 IEEE.

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SecuDroid : Android Malware Detection using ML classifier on Static Features 2

SecuDroid : Android Malware Detection using ML classifier on...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vanusha, D. Singh, Shashank Jha, Abhijeet Kumar Delsi Robinsha, S. School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350365269

The Android operating system's widespread use has unfortunately made it a target for malware attacks, underlining the necessity for robust detection strategies. Existing literature has explored the potential of machine learning in combating Android malware, often employing a mix of static and dynamic analyses. However, a major portion of these malwares are zero-day vulnerabilities. In this context, our study makes a significant contribution by focusing on using static features extracted from Android application package (APK) files to differentiate between benign and malicious applications. Specifically, all training and testing in this study were performed on the Drebin-215 dataset, a comprehensive collection known for its relevance in Android security research. Improving malware detection using static features allows for detection of malwares before they are run on devices. We analyzed five ML classification algorithms, beginning with a Decision Tree, followed by SVM (Support Vector Machine) with an RBF kernel, then moving to Logistic Regression and K-Nearest Neighbor, and finally, we introduced our proposed model, the SecuDroid Neural Network - employing a methodology that employs precise hyper-parameter tuning, and evaluation through stratified 4-fold cross-validation. The evaluation on an unseen test dataset, based on accuracy, F1-score, and AUC metrics, revealed the SecuDroid Neural Network surpassed its counterparts by reaching an accuracy of 0.993, an F1-score of 0.991, and an AUC of 0.999. Our findings not only offer a comparative analysis of different machine learning models for malware detection but also introduce an efficient approach, thereby enriching the arsenal against Android malware threats, with all empirical validations grounded in the Drebin-215 dataset. © 2024 IEEE.

关键词： Android malware

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Leveraging HPC with GAN-CNN Integration for Enhanced Tuberculosis Detection using Synthetic Image Generation 3

Leveraging HPC with GAN-CNN Integration for Enhanced Tubercu...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Manasa, Pokala Veeramalla, Vaishnavi Vanusha, D. Vathana, D. Robinsha, Delsi S. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603203 India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603203 India

ISBN: (纸本)9798331532420

Tuberculosis (TB) remains a significant global health challenge, especially in remote areas with limited medical resources. Early and accurate diagnosis is crucial to control its spread. This project harnesses high-performance computing (HPC) to address two key issues: managing unreliable medical data and improving TB diagnosis. Given the imbalance between healthy and TB-affected samples, Generative Adversarial Networks (GANs) are used to generate synthetic images, enriching the dataset and making it more diverse. This helps train Convolutional Neural Networks (CNNs), which are highly effective in medical image analysis. By leveraging HPC, we accelerate the training of CNNs on large, augmented datasets, significantly reducing processing time while maintaining accuracy. This system improves TB detection by combining GAN-enhanced data and CNN models, enabling faster and more reliable diagnosis. © 2024 IEEE.

关键词： X ray apparatus

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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Face Recognition CCTV for Automated Attendance Tracking

Face Recognition CCTV for Automated Attendance Tracking

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Kola, Sai Sindhu Kenchi, Harsha Rama, P. Angayarkanni, V. Kalaivani, J. Sivakumar, S. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies TN Kattankulathur603203 India Srm Institute of Science and Technology Computing TechnologiesCollege of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Faculty of Engineering and Technology Srmist Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798331529833

This study investigates the design and execution of an automated attendance tracking system using facial recognition CCTV based. Facial recognition technology and CCTV cameras are integrated in this system to provide a seamless, frictionless way to track and record attendance in real time. In addition to boosting attendance record accuracy, the approach reduces the administrative burden associated with using traditional methods. The suggested system is designed to recognise people when they enter or leave a monitored area by leveraging developments in computer vision. It accomplishes this automatically and requires no interaction from humans. Furthermore, the system's use of the Internet of Things makes it flexible and scalable to a variety of business situations, including offices, schools, and large-scale events. This is made feasible via real-time data synchronising and reporting. © 2025 IEEE.

关键词： Face recognition

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