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Recent Advances in Analysis and Detection of Tuberculosis System in Chest X-Ray Using Artificial Intelligence (AI) Techniques: A Review

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Current Materials Science 2023年第1期16卷 43-51页

作者： Suchitra, Shanmugam Ibrahim, S. Jafar Ali Sathya, Mariappan Sahini, Varsha Chakravarthy, N. Surya Kalyan Kumar, Vaneet Saruchi Department of Computer Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT SCOPE Vellore Institute of Technology Tamilnadu Vellore India AMC Engineering College Karnataka Bengaluru India Department of Computer Science Engineering CT Institute of Engineering Management and Technology Punjab Jalandhar India Data Science QIS College of Engineering and Technology Andhra Pradhesh Ongole India Department of Applied Sciences CT Institute of Engineering Management and Technology Shahpur Campus Punjab Jalandhar India Department of Biotechnology CT Institute of Pharmaceutical Sciences Shahpur Campus Punjab Jalandhar India

Mycobacterium tuberculosis causes tuberculosis (TB), a bacterial illness. Although germs are most typically found in the lungs, they can affect other sections of the body as well. Tuberculosis is one of the primary causes of mortality in both developed and developing nations, necessitating worldwide attention. Even though TB may be prevented in the majority of instances if discovered and treated early, the number of deaths caused by the disease is quite high. There has been a significant increase in interest and research activity in TB detection in recent years. The new advancement in the field of AI technology may be able to assist them in overcoming these development gaps. computer-Aided Detection and Diagnosis (CADD) aids in the diagnosis of diseases by analysing symptoms and X-ray images of patients. Many solutions are currently be-ing developed to improve the effectiveness of TB diagnosis classification using AI and DL ap-proaches. Although a variety of TB detection techniques have been developed, there is no common-ly acknowledged method. The purpose of this study is to give a survey on Tuberculosis Detection. It also emphasises the difficulty and complexity of the Tuberculosis Detection System's design. © 2023 Bentham Science Publishers.

关键词： computer vision

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Improved Yolov7 Tiny with Global Attention Mechanism for Camouflage Object Detection

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Journal of The Institution of Engineers (India): Series B 2024年 1-16页

作者： Mahajan, Chaitali N. Jadhav, Ashish Department of Computer Engineering Ramrao Adik Institute of Technology Nerul D Y. Patil University Maharashtra Mumbai400008 India Department of Information Technology Ramrao Adik Institute of Technology Nerul D Y. Patil University Maharashtra Mumbai400008 India

Detecting camouflaged objects presents a greater challenge in comparison to normal object detection as an object is highly similar to a complex background. Due to the similarities of an object with the background, it is very difficult to extract complete feature information of that target, causing an increase in false and missed detection. To analyse these issues, this research article introduces an enhanced algorithm built upon Yolov7 tiny. Firstly, a GAM was introduced in the backbone of Yolov7 tiny for the extraction of intricate features of an object. Also, three attention layers were introduced in the head section to increase prediction confidence. The parameters of the GAM module were adjusted and the position of the GAM was properly selected to ensure that the proposed method offered the best accuracy in camouflage detection. For training purposes, a Moving Camouflaged Animal dataset was considered which was initially available in video format and was later converted into images. It may be noted that seven classes have been considered to have a high camouflage environment. The problem of false and missed detection for camouflage images with complex dynamic backgrounds was improved giving better detection results by the introduced approach. The findings obtained from the experimentation revealed that the proposed model has attained an increase in mean Average Precision by 10.7% and 7.2% for mAP at 0.5 and mAP at 0.5:0.95 respectively for validation images for all classes and testing images there was an increase in mAP by 10.5% and 9.2% respectively. © The Institution of Engineers (India) 2024.

关键词： Camouflage

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Unveiling Threats: A Comprehensive Taxonomy of Attacks in In-Vehicle Networks 16

Unveiling Threats: A Comprehensive Taxonomy of Attacks in In...

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16th International Conference on Security of Information and Networks, SIN 2023

作者： Rai, Ritu Grover, Jyoti Pandey, Soumya Malaviya National Institute of Technology Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350343212

Innovative automobiles are typically outfitted with numerous electrical and intelligent gadgets to offer users a better driving experience. Technological advancements have also raised severe safety and security issues despite their apparent advan-tages. This article summarizes the various attack surfaces of connected automobiles as they relate to technology advancements from the standpoint of the in-vehicle network. In particular, we described how attacks are launched through the physical interface using On-board Diagnostic (OBD-II) and USB ports and through the remote surface using various components such as ECU, sensors, infotainment system, Global Positioning Sys-tem (GPS), data centers, and cellular data. Given the importance of automobiles in terms of safety, more practical and effective efforts should be made within the connected vehicle network to secure linked vehicles and protect drivers and passengers. © 2023 IEEE.

关键词： Taxonomies

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SVM Algorithm based Fitness App Development with Age Classification using Andriod Studio 7

SVM Algorithm based Fitness App Development with Age Classif...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Flavya, L. Shreya, V. Sardar Maran, P. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

People's lives have been evolving due to the advancement of technology, especially in the aspect of mobile internet which has promoted the usage of fitness apps. With this app, users can exercise at anytime and anywhere. These fitness apps changed people's exercise and habits radically. With the help of this good exercise participation, people have gotten accustomed to making use of this fitness app as it has also brought additional attributes like advising on health tips which have made this app ever so special, which acts as an element of construction for obtaining the *** drastic improvement in the information gathering at online portals has helped fitness apps like springing to cope with the latest updates and help to act accordingly. Although public guidance has been crucial in decision-making, Studies show it is not favorable to follow those particular pieces of advice. Studies show that there are too many problems with fitness apps. A deficiency in time management, and no proper development, people do not refer to technical obstacles and relevant databases. The other major complication is that it is not able to keep up with the specification of different age groups. © 2023 IEEE.

关键词： Decision making

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Comparative analysis of detecting over-claim permissions from android apps

Comparative analysis of detecting over-claim permissions fro...

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Majethiya, Raj J Shah, Monika Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad India

ISBN: (纸本)9798350399387

Android devices are popular nowadays due to their rich functionality and good performance. Android provides a permission model that supports user privacy by protecting restricted data access and actions on the Android operating system. Based on the app's defined functionality, any app must get users' permission to access different types of restricted data or resources. However, some developers request additional permissions other than the app's functionality, known as over-claim permissions. These over-claim permissions may steal sensitive information from users, Which is one of the leading privacy issues. Looking towards its criticality, many researchers have started working on detecting over-claim permission. This paper conducts a comprehensive survey of various approaches to identify and handle over-claim permissions. It also discusses the limitations of existing techniques and further research scope in this direction, which may help researchers define a path for designing new methods or optimizing existing strategies to resolve over-claim permission problems satisfactorily. © 2023 IEEE.

关键词： Android (operating system)

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Efficient Liver and Tumor Segmentation from CT Images Using a Hybrid ResUNet Model 3

Efficient Liver and Tumor Segmentation from CT Images Using ...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Singh, Aditya Pratap Mishra, Santosh Kumar Nidhi Singh, Jagendra Agrawal, Madhup Joshi, Prakash Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350364569

In this paper, we investigated deep learning models for automated segmentation of liver and tumor regions from CT images to improve the diagnostic accu-racy as well as treatment planning in case of liver cancer. The research used a dataset of 3200 CT scans, with about 70% serving as the testing set and 30% for training. Preprocessing steps like Auto Windowing HU (Hounsfield Units) and Histogram Equalization to retain better clarity of the images. Based on the metrics of performance such as accuracy, Dice coefficient, IoU(Intersection over Union), precision and recall were calculated used three types of CNN models (ResUNet, VGG16 and VGG19). Its hybrid architecture combining U-Net and ResNet, called as ResUNet indicated better results with an accuracy of 97.3%, against VGG16 at 93.5% and VGG19 at 91.5%. The model also demonstrated good data loss reduction from 0.35 to about 0.10 over the course of a total for around an epoch angle in enduring caching past phase. VGG16 and VGG19 outperformed the baseline performances with reduced data losses while higher accuracies, although their convergence rate was slightly slower than ResUNet. The results confirmed the high precision of liver/tumor segmentation that ResUNet provides, which would be useful for clinical practice to achieve sufficient diagnostic improvement. The architecture of ResUNet specifically designed for the segmentation tasks in medical imaging from this study, demonstrates to be a versatile framework favoring its potential integration into automated tools within oncological diagnostics and could thus radically transform liver cancer detection. © 2024 IEEE.

关键词： computerized tomography

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Generative Adversarial Networks (GANs) for Artistic Style Transfer in Images

Generative Adversarial Networks (GANs) for Artistic Style Tr...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jamalpur, Bhavana Kiruthiga, S. Ranjitha, B. Lefty Joyson, J. Jayaram, K. Soundar, K. Ruba S R University Department of Computer Science and Artificial Intelligence India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Mohan Babu University Department of Mathematics India Jp College of Engineering Department of Computer Science and Engineering Ayikudi India College of Engineering Adoor Department of Computer Science and Engineering Pathanamthitta India Department of Computer Science and Engineering Sivakasi India

ISBN: (纸本)9798350375442

Many in computer vision and computational imaging consider Generative Adversarial Networks (GANs) a big advance. Style transfer converts semantic visual content into creative styles. Recently, GANs for style transfer have gained popularity. Adversarial training of GANs to create realistic counterfeits has yielded encouraging results. High-quality style transfer results have been achieved using this method. Mode collapse is a common problem in classical GANs. This causes unsteady training and poor style transmission. Additionally, the GAN generator only supports one style. Therefore, numerous GANs must be trained to allow users to shift styles. This study examines artistic style transfer in photographs using depth extraction generative adversarial network (DE-GAN). This research aims to improve photo aesthetics by adding famous artworks' distinctive qualities. This study investigates DE-GANs' mechanics and how they modify text while keeping a style's creative qualities. By studying adversarial training, this work hopes to understand how DE-GAN achieves this dual goal. This research analyzes DE-GAN-based style transfer models to evaluate their effectiveness, identify their limits, and recommend areas for improvement. The experiments show that DE-GAN creates realistic and attractive stylized visuals. This study also discusses DE-GAN's practical applications, problems, and future possibilities in artistic style transfer. © 2024 IEEE.

关键词： Generative adversarial networks

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20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Deep, Kumar Shikhar Akhtar, Md Shad Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta India

ISBN: (纸本)9783031243394

Expressing the polarity of sentiment as ‘positive’ and ‘negative’ usually have limited scope compared with the intensity/degree of polarity. These two tasks (i.e. sentiment classification and sentiment intensity prediction) are closely related and may offer assistance to each other during the learning process. In this paper, we propose to leverage the relatedness of multiple tasks in a multi-task learning framework. Our multi-task model is based on convolutional-Gated Recurrent Unit (GRU) framework, which is further assisted by a diverse hand-crafted feature set. Evaluation and analysis suggest that joint-learning of the related tasks in a multi-task framework can outperform each of the individual tasks in the single-task frameworks. © 2023, Springer Nature Switzerland AG.

关键词： Learning systems

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A Coherent Way of Detecting Learner's Academic Emotions via Live Camera Using CNN and Deep LSTM 4

A Coherent Way of Detecting Learner's Academic Emotions via ...

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4th International Conference for Emerging technology, INCET 2023

作者： Rathi, Snehal Oswal, Samkit Ahuja, Ayushi Vishwakarma Institute of Information Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350335750

Academic Emotion Detection is fundamentally a system for detecting emotions. The system's main goal was to identify feelings expressed while attending online lectures during the COVID-19 epidemic. The topic Academic Emotion Detection using Machine Learning focuses on utilizing machine learning and deep learning to identify human face emotions in light of the shift to online *** research has a limited scope, it focuses on four academic emotions: confusion, boredom, engagement, and frustration. A person may experience a wide range of other emotions as well. Here, we have used CNN and Deep LSTM for the prediction of said four emotions and it has been observed it increases the accuracy of prediction and effectiveness. We even incorporated a portion of a questionnaire into our research to compare our results with genuine human *** Neural Network (CNN), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) are three different algorithms from the deep learning area that we have used in this study to examine how they operate and identify similarities and differences. © 2023 IEEE.

关键词： Emotion Recognition

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Challenges Issue and Application of Wireless Sensor Networks: A Systematic Literature Review

Challenges Issue and Application of Wireless Sensor Networks...

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2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Kannaujaiya, Deepak Dwivedi, Anshu Kumar Buddha Institute of Technology Department of Computer Science & Engineering Gida Gorakhpur India

ISBN: (纸本)9798350302820

Wireless sensor networks (WSNs) have become integral to the Internet of Things (IoT) industry. In WSN, power consumption is essential as most sensor nodes are battery-operated and need to be replaced or recharged periodically. Therefore, energy-efficient protocols are essential to prolong the lifetime of WSNs and ensure their optimal performance. This article also describes an overview of the energy-saving wireless sensor network protocols that have been proposed and implemented in recent years and also describes future research directions in this area. Challenges and applications of WSN are described efficiently so that a group of research scholars can understand the advantage and disadvantages of WSN. © 2023 IEEE.

关键词： Energy efficiency

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