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One-Dimensional Dilated Hypothesized Learning Method for Intrusion Detection System Under Constraint Resource Environment 2

One-Dimensional Dilated Hypothesized Learning Method for Int...

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Shuriya, B. Umamaheswari, S. Rajendran, A. Sivaprakash, P. Ppg Institute of Technology Dept of Computer Science and Engineering Coimbatore India Kumaraguru College of Technology Dept of Electronics and Communication Engineering Coimbatore India Rathinam College of Arts and Science Dept of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350306811

When a breach is discovered, intrusion detection systems can alert server administrators and researchers and channel packets for cyber security incidents. In complex systems, these reports are becoming uncontrollable. To create a flexible and effective system for detecting intrusions for unforeseen threats, a number of challenges must be overcome. Systems that detect intrusions have frequently used advanced machine learning approaches, but they frequently require large amounts of computational power and processing time. In this article, the classification model is an IDS based on a one-dimensional-dilated hypothesized learning algorithm (1-DDHL). In order to account for the greatest possible pooling layer's complexity and eliminate the data redundancies brought on by pooling as well as down-sampling, dilated hypothesized inversion with a dilatation rate that includes two is initiated. In order to gather morecontexts, the dilated hypothesized convolution can widen its convolution layers. To analyze the effectiveness of the suggested solution, experiments were conducted using the CIC-IDS2017( Dataset 1)& CSE-CIC-IDS2018 (Dataset 2) sets of data, 2 well-known datasets that are publicly accessible. The 1-DDHL derived method is more accurate than some other deep learning methods currently in use, according to simulation results. With a problematic occurrence and avoidance rate of approximately 99.8% over dataset 1 and 99.97% for dataset 2, the suggested method was remarkably accurate. © 2023 IEEE.

关键词： Learning algorithms

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Detection of Coal Quarry and Coal Dump Regions Using the Presence of Mine Water Bodies from Landsat 8 OLI/TIRS Images 1

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7th International Conference on computer Vision and Image Processing, CVIP 2022

作者： Mukherjee, Jit Mukherjee, Jayanta Chakravarty, Debashish Dept. Computer Science and Engineering Birla Institute of Technology Ranchi Mesra India Dept. Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. Mining Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

Surface mining has major environmental, social, and economical adversities, which makes it an active area of research in remote sensing. Surface coal mining has additional adversities of coal seam fires. Thus, the detection, classification, and monitoring of such regions have various research challenges. The surface coal mining land classes cover smaller areas compared to mid-resolution satellite images making them challenging to detect. Coal quarry and coal dump regions are such kinds of smaller land classes. They can be detected as a single land class as discussed in the literature. However, these land classes are observed to be difficult to detect separately as they follow near similar spectral characteristics. Hence, this paper proposes a novel technique to separate these regions using the presence of water bodies. Coal dump regions do not have water bodies, whereas some coal quarry regions may have water bodies. Such quarry regions are detected at first and further, they are used to train an unsupervised single class support vector machine (SVM ). This model is used to detect the coal dump regions by detecting the outliers. The proposed technique provides average precision and recall for coal quarry, and coal dump regions as [84.88%, 61.44%], and [70.91%, 52.79%], respectively over the seasons. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Quarries

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An Ensemble Learning Approach to Detect the Presence of Diabetic Retinopathy based on Inception Module 2

An Ensemble Learning Approach to Detect the Presence of Diab...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Devi, T. Aswini Yamana, Sathwik Harshankar Reddy, G. Sangita, Vedh Sivaji Kumar Kulkarni, Swapneel Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665456302

In recent times, Diabetic Retinopathy (DR) has become a complication of major degree for diabetic patients, in which the blood vessels present in the eye retina are severely damaged. This in most cases has lead to a loss of vision as observed from records of DR patients, and if left untreated, can cause blindness. As per the estimates of the World Health Organization, DR will impact around 224 million people by 2040. This research study presents a convolutional neural network (CNN) model, followed by employing ensemble learning over various ML algorithms through max voting, for the task of image classification for detecting Diabetic Retinopathy using color fundus images, with the goal of improving the accuracy and efficiency of the diagnostic process. The proposed model is designed to address the challenges of real-world data, such as variability in the appearance of images that belong to the same class and class imbalance. The dataset used for this study is EYEPACS, which is widely used for training and testing models for detecting Diabetic Retinopathy. The Metrics that are used for measuring the performance of the proposed model are accuracy, precision, and F1-S core. This study obtains an accuracy of 95.4 percent. © 2023 IEEE.

关键词： Blood vessels

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A Lightweight CNN-SVM Explainable AI Approach for Classification and Visualization of Grape Leaf Disease 3

A Lightweight CNN-SVM Explainable AI Approach for Classifica...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Peyal, Hasibul Islam Leion, Zannatul Mawa Abdal, Md. Nazmul Islam, Md. Injamul Miraz, Shafiun Remon, Mahmudur Rashid Kontho, Md. Mahin Rahaman Tasnim, Nishat Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Rajshahi Bangladesh North South University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Sher-e-Bangla Agricultural University Dept. of Agroforestry and Environmental Science Dhaka Bangladesh

ISBN: (纸本)9798350388282

Grape is highly esteemed as a significant agricultural crop in Bangladesh. Plant diseases primarily result from the presence of pathogens and pest insects, leading to a significant decline in productivity if not properly addressed. The fundamental aim of this research is to employ a streamlined CNN-SVM architecture, utilizing deep learning techniques, to accurately categorize grape leaves into three distinct disease classes and one healthy class. The proposed model surpasses the accuracy of the previously trained transfer learning models VGG-16 and VGG-19 while having approximately 257× to 267× times fewer parameters (0.537 M). On average, the proposed model achieves a classification accuracy of 99.18%, which is significantly higher than the 93.42% and 91.94% achieved by the transfer learning models, respectively. With a precision, recall, and F1 score close to 99%, the suggested model provides excellent results. The model's outstanding performance is further validated by its remarkable Area Under Curve (AUC) score of 99.98%. In addition to using less disc space (about 6 MB), the suggested model because of being lightweight shows a significant decrease in parameters. To visually display the disease identified by the proposed model, a transparent AI methodology has been utilized, specifically the Gradient Weighted Class Activation Mapping (Grad-CAM) technique. To better understand which area was responsible for the classification, a heatmap has been created. © 2024 IEEE.

关键词： Deep learning

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Improving Classification using IS-SMOTE on Imbalanced Heart Failure Data 5

Improving Classification using IS-SMOTE on Imbalanced Heart ...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Jannath, Nahary Masud, Md Abdul Hossain, Mohammad Jamal Samsuzzaman, Md. Pstu Dept of Computer Science and Information Technology Dhaka Bangladesh Pstu Dept of Computer and Communication Engineering Dhaka Bangladesh

ISBN: (纸本)9798350394290

Heart failure is a fatal cause of mortality globally. One in every four individuals is afflicted by and succumbs to heart disease. Given the considerable complexity of heart disease classification, this research primarily centers on the classification of heart failure patients and strives to predict heart failure survival with high accuracy. Because of class imbalance problem, the classification result is prone to bias towards the dominant majority class. An existing popular technique for handling class imbalance issues is the Synthetic Minority Oversampling Technique (SMOTE). SMOTE gives equal importance to every minority instance of oversampling. Furthermore, a significant but unresolved problem is the generation of minority synthetic instances, the number of which is fixed at five for each instance. This creates overlapping samples between closer instances. In this research, a modified variant of SMOTE is proposed, namely, Instance Selection based SMOTE (IS-SMOTE), to mitigate the problem of SMOTE. In our proposed IS-SMOTE, oversam- pling rates are not fixed at five, and resembling instances are oversampled based on their distance from other instances. The closest instances are given maximum importance over the furthest ones and generate a smaller number of synthetic instances. The most difficult issues, particularly oversampling rates and figuring out how many neighbors there are for each sample in SMOTE, can be resolved by the proposed IS-SMOTE. ISSMOTE outperforms existing SMOTE in terms of average performance metrics, including Accuracy, Precision, Recall, and F-Score, across five base classifiers: SVM, GNB, RF, DT, and XGBoost, as demonstrated in experiments conducted on heart failure data. © 2023 IEEE.

关键词： Feature Selection

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LightNN: An Optimized Deep Learning Framework for Enhanced Fruit Classification

LightNN: An Optimized Deep Learning Framework for Enhanced F...

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

作者： Afroj, Maharin Jahan, Israt Islam, Md Polash Saifur Rahman, Md. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Stockholm University Dept. of Computer and Systems Sciences Stockholm Sweden

ISBN: (纸本)9798331529765

The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and sorting of various fruit types, these technologies enhance efficiency, accuracy, and scalability, benefiting processes from farm production to market distribution. The integration of sophisticated algorithms and neural networks not only improves the speed and reliability of fruit classification but also offers the potential for real-time quality control and inventory management, revolutionizing traditional methods and paving the way for more innovative, data-driven approaches in these sectors. This study introduces LightNN, a novel and effective paradigm with a lightweight architecture that tackles the complex problems related to computational efficiency. Combining a small classifier with a simplified feature extractor, LightNN achieves the ideal balance between simplicity and accuracy. The feature extractor is capable of extracting hierarchical features from RGB images by using convolutional layers with ReLU activations and max-pooling procedures. The flattened output is then processed by the classifier, which consists of fully connected layers with ReLU activations and dropout regularisation. Remarkably, LightNN, with just 298,835 parameters, manages to get a respectable 96.37% test accuracy in fruit categorization tasks. The results highlight LightNN's promise as an effective and efficient fruit classifying method, particularly in situations with constrained computational resources. © 2024 IEEE.

关键词： Fruits

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Unveiling Insights: Vision-Based Data Mining Analysis of Webpages in Transition from HTML 3 to HTML 5 5

Unveiling Insights: Vision-Based Data Mining Analysis of Web...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Chaudhary, Meena Pooja Chandwani, Gunjan Manav Rachna University Dept. of Computer Science and Technology Faridabad India Galgotias University Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Deep web material is accessed with web database requests, and returned information is packaged into dynamically generated web pages known as deep Web pages. Due to their intricate structure, it is tedious task to retrieve organized data from deep web pages. A great many approaches to address this problem are yet to be proposed, but all of them have inherent drawbacks because they are based on the website and the programming language. The contents of Sites are always shown for users to search daily, like the common two-dimensional media. This motivates us to search for an alternative way to extract deep Web information, by using some interesting popular visual features from deep Webpages, to address the limitations of past works. A novel blog vision approach is proposed in this paper to overcome these issues. This technique uses visual features in deep web pages specifically to conduct deep web data extraction. This paper also recommends a new appraisal measure to capture the required human activity to make maximum mining. The proposed methodology is highly efficient in the deep network data extraction, according to our studies with a large set of web-based data bases. © 2024 IEEE.

关键词： Data mining HTML 3 HTML5 Vision based technique Web Content Mining

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A Highly Scalable Automated Universal Binary Malware Analysis Framework for Healthcare and Medical SaaS 24

A Highly Scalable Automated Universal Binary Malware Analysi...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Cheng, Shin-Ming Fan, Chin-Chi Chien, Bo-Han Chang Chen, Chia-Yi Lin, Wei-Hung Liao, Chen-Chien Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei City106335 Taiwan Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei City106335 Taiwan

ISBN: (纸本)9798400716874

In recent years, there has been a growing emphasis on information security, with major companies introducing cybersecurity teams to ensure the safety of data. However, there is a lack of convenient channels for general users to engage in cybersecurity protection, leaving them with no choice but to rely on various antivirus software installations to safeguard their privacy and financial assets. This phenomenon is particularly evident in the digital transformation of healthcare and medical information systems, where the substantial amount of digitized patient data has become a crucial asset within hospital systems. Unfortunately, it has also made healthcare systems potential targets for cyberattacks. The databases of major hospitals have become vulnerable to malicious virus invasions, posing significant threats to patient privacy and the operational integrity of healthcare institutions. Faced with such threats, there is an increasing need for comprehensive cybersecurity protection mechanisms. To address this issue, we have developed the Universal Binary Malware Analysis Framework (UBMAF), an easily accessible binary file analysis framework for the general public. UBMAF integrates multiple open-source static and dynamic analysis tools into an automated module, deployed as Software as a Service (SaaS) in the cloud for healthcare and medical systems. This eliminates the need for users to install applications, and the framework interface is optimized for intuitive usability. During the usage process, users can freely choose module combinations. After uploading files to UBMAF, the framework conducts corresponding tool analyses or file processing based on the selected modules. Ultimately, it provides users with downloadable results and analysis reports. This design enables large healthcare and medical systems to quickly and conveniently enhance their cybersecurity defenses while ensuring the security of digital medical data, effectively addressing the challenges brought abo

关键词： Cybersecurity

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Generative AI Models for Virtual Interviewers: Applicability and Performance Comparison

Generative AI Models for Virtual Interviewers: Applicability...

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2023 International Conference on Innovation, Communication and engineering, ICICE 2023

作者： Si, Jongwook Song, Jaeyong Woo, Minsik Kim, Daemin Lee, Yonghwan Kim, Sungyoung Dept. of Computer AI Convergence Engineering Kumoh National Institute of Technology Korea Republic of Dept. of Computer Engineering Kumoh National Institute of Technology Korea Republic of Dept. of Electronic Engineering Kumoh National Institute of Technology Korea Republic of

ISBN: (纸本)9781839539954

Interviewing processes are considered crucial steps in job hunting or college admissions, and effective practice plays a significant role in successfully navigating these stages. Although various platforms have recently emerged for practicing virtual interviews, they often lack the tension and realism of actual interviews due to repetitive and formal content. This study aims to analyze and compare the performance of different generative AI models for creating a diverse set of virtual interviewers. Specifically, we examine the characteristics and applicability of each model, as well as the differences and advantages between them, and evaluate the performance of the generated virtual interviewers. Through this analysis, we aim to propose solutions for enhancing the practicality and efficiency of virtual interviews. © The Institution of engineering & technology 2023.

关键词： Virtual environments

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FACE UNLOCK SYSTEM FOR VEHICLES 2

FACE UNLOCK SYSTEM FOR VEHICLES

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Navya, Soleti Suresh, Juttu Sangeetha, A. Praveen, G. Nabi, Shaik Abdul Kumar, Dilip Mlr Institute of Technology Department of Computer Science and Information Technology India Malla Reddy Engineering College and Management Sciences Department of Information Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Cse Dept Avniet Jntuh India Mlr Institute of Technology Dept. of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350370188

Currently, all vehicles are key operated. Our intention is therefore to develop an intelligent unlocking system based on facial recognition technology. In Today's era of automation and smart devices, privacy and security are important issues in information technology so to information system, so it is urgent to change system security measures. In previous systems, many vehicle doors had mechanical locks with a limited number of keys. To enhance security, we propose an intelligent vehicle door unlocking system based on facial recognition the system uses camera sensor to recognize faces and image matching algorithms to recognize authenticated faces. Only people with matching faces can open the door this system not only provides additional security but also useful for systems without physical keys. © 2024 IEEE.

关键词： Network security

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