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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, P. Varalakshmi, I. Priyadharshini, S. Pradeepa, R. Sathvika, A. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India St. Joseph's College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331510022

This paper intends to propose a new robot which overcomes the hurdles of an AED(Automated External Defibrillator) at the nearest Ambubot: A robot, ambulance robot designed and developed that carries an AED for assisting lay rescuers to save patients' lives, as well as Time taken before the ambulance is located in the case of a sudden cardiac arrest. The working of Ambubot could go searching for the victims and also obedient to the human instructions. Population is growing daily, and with it, so is the risk of heart disease also increasing. Even though traffic on our roads is normal for people, it is crucial to admit heart attack victims to hospitals in case of emergency because it can be easy for them to receive an automated external defibrillator at such a young age. Its independent purpose is to save the sufferer. © 2024 IEEE.

关键词： Defibrillators

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Scheduling and Optimization of Wireless Sensor Networks Lifetime using Soft Computing 13

Scheduling and Optimization of Wireless Sensor Networks Life...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Alqahtani, Abeer College of Computer Science and Engineering University of Jeddah Department of Computer Science and Artificial Intelligence Saudi Arabia

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305463

The paper presents a novel approach to address the problem of lifetime maximization in Wireless Sensor Networks (WSNs) with limited initial energy constraints. The authors introduce a scheduling approach called Non-disjoint Set Cover (NDSC) and propose a method based on the Particle Swarm Optimization (PSO) algorithm to optimize the lifetime of randomly deployed WSNs. The approach involves dividing the problem into two sub-problems. The first sub-problem focuses on finding the maximum number of NDSCs that achieve full coverage of the targets in the area. PSO, using a heuristic method, is employed to address this sub-problem. The second sub-problem deals with finding an optimal scheduling approach to maximize the WSN lifetime while considering the limited initial energy reserve in each sensor. Again, PSO, based on a heuristic method, is used to tackle this sub-problem. The NDSCs obtained from the first sub-problem serve as inputs for the scheduling in the second sub-problem. The solutions obtained by the proposed NDSC-PSO method are compared with those obtained by the NDSC-GA (Genetic Algorithm) method from the literature. Additionally, the quality of the NDSC-PSO solutions is evaluated by comparing them with exact solutions obtained using an Integer Linear Programming (ILP) method from the literature. The experimental results demonstrate that the NDSC-PSO method outperforms the NDSC-GA method in terms of finding optimal solutions for the same instances. The proposed method effectively maximizes the lifetime of WSNs while considering limited initial energy constraints. The paper contributes to the existing literature by presenting a novel approach that utilizes the PSO algorithm for finding NDSCs and scheduling them in WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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A Case Study on CALVEASE: AI Driven IoT Framework for Dystocia Prediction

A Case Study on CALVEASE: AI Driven IoT Framework for Dystoc...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Sharma, Anmol Hooda, Nishtha Hu, Yu Hen Singh Rana, Prashant University of Wisconsin-Madison Electrical and Computer Engineering Department Madison United States Indian Institute of Information Technology Department of Computer Science Una India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798350308167

The ability to predict cow calving easiness cost-effectively, especially in the dairy industry where cattle suffer from a variety of unpredictable deadly illnesses and high breeding expenses assist farmers in improving reproductive performance and, thereby, farmers profitability. In this research work, machine learning and Internet-of-Things (IOT) based CALVEASE framework for monitoring dairy herd and predicting calving easiness is studied as a multi-classification problem. The CALVEASE framework collects activity information through sensors deployed in cow's collar belt in the first stage. The collected information stored at the cloud platform is utilized to train machine learning models for predicting the cow calving easiness in the second stage. On empirically testing the various models during field testing, random forest outperforms the other models showing an accuracy of 99.58% hence, the tested framework was found to be efficacious for boosting the dairy herd productivity. © 2023 IEEE.

关键词： Calving ease score Data analytics Machine learning Multi-class Classification

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Secured Release through an Agile Security Growth Framework 29

Secured Release through an Agile Security Growth Framework

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29th ISSAT International Conference on Reliability and Quality in Design, RQD 2024

作者： Bhattacharya, Saikath Williams, Laurie Singh, Munindar P. Electrical Engineering and Computer Science Milwaukee School of Engineering Milwaukee United States Dept. of Computer Science North Carolina State University Raleigh United States

ISBN: (纸本)9798986576138

Software development teams need to produce and assess secure software products so that they can perform critical functionality. Because no software product can ever be "perfectly secure, " development teams need to decide when a software product is "secure enough" to be released, that is, to make a decision on the product’s release readiness. The goal of this paper is to aid software practitioners in predicting whether a software product is "secure enough" to be released through the evolution and evaluation of software security growth models (SSGM). In this paper, we adapt and evolve software reliability growth models (SSGMs) for the purpose of SSGM using empirical software vulnerability analysis. We quantitatively compare the quality of agile software versions by comparing the security correction and detection process over testing time and software testing cost. The framework is generic to incorporate software vulnerability data into different time frames and multiple agile software versions. We evaluated our SSGM using data from Google Chromium, which uses an agile process with releases every 50 days. © Conference Proceedings of the 29th ISSAT International Conference on Reliability and Quality in Design, RQD 2024.

关键词： Software quality

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI 4

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swarna Sudha, M. Manjula, S. Bharathi, Indira Krishnasamy, Valarmathi Vijayalakshmi, K. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India School of Computer Science and Engineering Vellore Institute of Technology Chennai India PSR Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798331523923

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions. © 2025 IEEE.

关键词： Contrastive Learning

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Lighting the Smart Way: A Technological Odyssey in Urban Street Illumination 10

Lighting the Smart Way: A Technological Odyssey in Urban Str...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vinu, M.S. Priya, P Kanaga Thilakaveni, P. Hermina, J. M. Ashforn Prashanthini, K. Misba, M. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Psg Polytechnic College Department of Computer Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Ramakrishna Engineering College Department of Robotics and Automation Coimbatore India Bethlahem Institute of Engineering Department of Computer Science and Engineering Nagercoil Karungal India

ISBN: (纸本)9798350384369

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure © 2024 IEEE.

关键词： Air quality

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Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

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computer Systems science & engineering 2023年第5期45卷 1655-1677页

作者： G.Nallasivan V.Ramachandran Roobaea Alroobaea Jasem Almotiri Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21974Saudi Arabia

An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver *** of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s *** changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis,suggesting that the cause may be alcohol instead of liver *** Two-Dimensional(2D)ultrasound data sets contain an accuracy rate of 85.9%and a 2D Computed Tomography(CT)data set of 91.02%.The most recent work on designing a Three-Dimensional(3D)ultrasound imaging system in or close to real-time is *** this article,a Deep Learning(DL)model is implemented and modified to fit liver CT segmentation,and a semantic pixel classification of road scenes is *** architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers.A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training *** the normal class,we obtained 100%precision for chronic cirrhosis hepatitis(73%),offset cirrhosis(59.26%),and offensive cirrhosis(91.67%)for chronic hepatitis or cirrhosis(73,0%).The aim is to develop a computer-Aided Detection(CAD)screening tool to detect *** results proved 98.33%exactness,94.59%sensitivity,and 92.11%case with Convolutional Neural Networks(CNN)*** the classifier’s performance did not differentiate so clearly at this level,it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve(AUC)and accuracy.

关键词： Deep learning liver tumor CNN CT accuracy

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PV Powered Smart Irrigation System

PV Powered Smart Irrigation System

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Sagor, Nur Elahi Hassan, Jahid Ahmed, Syed Udoy Jabir Ahmed Jony, Md Khan, Md Obaydullah Haque Anik, Nazmul Pabna University of Science and Technology Department of Electrical and Electronic Engineering Pabna Bangladesh University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798331529765

The research conducted in this study focuses on examining the viability of renewable energy sources as an alternative means of generating electricity in off-grid areas. This study demonstrates the optimal design of a photovoltaic (PV) drip irrigation system, emphasizing key considerations for tailoring the system to a specific geographic location. The design involves calculations of motor power, solar irradiance, and meteorological factors. A simulation study assesses potential power generation by optimizing tilt angles and orientations of PV modules, accounting for monthly and annual variations, overall shading, and internal shading used between component rows. The water pump and motor are selected based on the water table depth, intended discharge, and head for effective irrigation. The paper introduces a solar-powered portable water pump (SPWP) for a smart irrigation system. The system oversees and manages the water pump using Arduino, moisture sensors, and floating sensors. A user-friendly LCD takes center stage, prominently featuring a counter for automatic pump activations. This counter is specifically linked to the float sensor, which triggers the pump to fill the tank during automatic mode. Each activation increments the counter, giving users a clear count of automatic pump operations. The pump is powered by a solar panel, contributing to energy efficiency and reduced operational costs. Integration of portable water pump usage efficiency enables convenient and real-time monitoring and control. The system has undergone practical testing in a real-world setting, affirming its performance and validating its efficacy in enhancing agricultural operations. © 2024 IEEE.

关键词： Electronics packaging

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A Robust Random Forest Model for Classifying the Severity of Partial Discharges in Dielectrics 4

A Robust Random Forest Model for Classifying the Severity of...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Kameli, Sayed Mohammad Abuelrub, Abdelaziz Saleh, Mohammad Alshaikh Refaat, Shady S. Olesz, Marek Guzinski, Jaroslaw Texas A&M University Department of Electrical and Computer Engineering College Station United States Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering United Kingdom Gdańsk University of Technology Department of Mechatronics and High Voltage Engineering Gdańsk Poland

ISBN: (纸本)9798350306262

Partial Discharges (PDs) are a common source of degradation in electrical assets. It is essential that the extent of the deterioration level of insulating medium is correctly identified, to optimize maintenance schedules and prevent abrupt power outages. Temporal PD signals received from damaged insulation, collected through the IEC-60270 method is the gold standard for PD detection. Temporal signals may be transformed to the frequency domain, introducing new spectral features that may be beneficial in certain circumstances. Consequently, time delays are introduced, due to the high utilization of computational resources within the signal processing pipeline. Moreover, some microprocessors struggle with the excess computational burden demanded by resource-heavy mathematical transformations. To rectify these issues, an alternative approach is utilized, where Machine learning (ML) algorithms are directly used for the classification of PD severity. Cylindrically-shaped air cavities with lengths ranging from 1mm-6mm are introduced to a resin-based polyethylene terephthalate (PET) insulation material. The cavities are partitioned based on size, to obtain different classes of PD severity. A comparative analysis is performed on various ML algorithms, to determine which algorithm correctly determined the severity of PDs, with highest efficacy. Random Forest was determined to be the most performant, with an accuracy of 98.33%. The high performance illustrates the model's potential success in accurately determining the hazard level of PDs in real-time, based on merely time-domain signals. © 2024 IEEE.

关键词： Machine learning

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