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Convolutional Neural Network-Based Anomaly Detection for Cyber Security: A Survey 3

Convolutional Neural Network-Based Anomaly Detection for Cyb...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Agarwal, Neha Kaur, Amandeep Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

There are many security risks as computer and communication technologies continue to advance. To reduce these worries, several different kinds Numerous processes with cyber-security enabled have been developed. Anomaly detection is one of these methods. Finding anomalies involves employing a variety of approaches and procedures to identify various patterns that do not adhere to predetermined characteristics of the entire body of data. Deep learning techniques have recently been accepted as an adequate option due to their capacity to take data properties directly from the data. Convolution neural network (CNN) is employed primarily because it can handle input with many dimensions. This study offers a thorough analysis of the use of CNN as a primary tool for anomaly identification. The majority of the known remedies have been compiled and categorized in the literature. Additionally, this research provides a bound together cross framework that mimics complete anomaly detection methods that were previously offered examinations, as per the information source. In-depth assessment from a brought together cross system upgrades this paper's top to bottom conversation of how CNN is utilized in the writing's response to the irregularity identification issue. Finally, this article makes a number of recommendations for future study areas that the audience may find useful in their own further activities in this area. © 2023 IEEE.

关键词： Anomaly detection

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Groundwater Evaluation with Classification Techniques

Groundwater Evaluation with Classification Techniques

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Dogra, Yakesh Khan, Mizan Ali Chandigarh School of Business Department of Master of Computer Application India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350322309

We has researched that your earth area contains % comprised of water and the remaining 28 part also with soil. From the 28 per cent, % water is seawater namely it is excessively salted for consuming, for agricultural production and more critical items. That's because the freshwater which seems utilised is extremely small in bulk and its condition is every day growing worse. The condition of the water must be kept since it is a crucial life factor not just the individuals as well as other creatures and flora. Humans demand a suitable level sustained liquid to consume and employ in varied daily tasks Assessments of groundwater recharge and assessment are thus essential for the development of economy and society. Photogrammetry is now a crucial technique for ground component assessment as a result of advances. So to assure if the groundwater we get is excellent or wouldn't we have gathered a set and used multiple Ml techniques to assess the auc. So, the research intends to determine the prognosis of water whether it is of good standard or not by utilizing two algorithms, rbfnn and perceptron and to demonstrate which outperforms. © 2023 IEEE.

关键词： Support vector machines

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A Multi-Objective Hyper-Heuristic Improved Configuration of Svm Based on Particle Swarm Optimization for Big Data Cyber Security 3

A Multi-Objective Hyper-Heuristic Improved Configuration of ...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Shekhawat, Jyoti Tajpal Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

Big Data Cyber security analytics is a growingly significant field of study and application that uses big data techniques and technology to analyses security event data in order to safeguard networks, computers, and data from illegal access. There have been several reports on Big Data Cybersecurity Analytic Systems in the literature, but an exhaustive and systematic analysis of the research from an architectural viewpoint is lacking. When In this paper, the SVM design process is organized as a bi-objective optimization problem with accuracy and model complexity as two competing goals. The system presents a novel, problem-independent hyper-heuristic framework for bi-objective optimization. It is novel to create a hyper heuristic for this situation. The hyper-heuristic structure proposed comprises of low-level heuristics and a significant level system. In light of the pursuit execution, the significant level system figures out which low-level heuristic ought to be utilized to create another SVM arrangement. Every one of the low-level heuristics look through the SVM design search space really utilizing an alternate arrangement of rules. To estimate the Pareto set of SVM arrangements, the suggested design adaptively combines the benefits of degradation with Pareto-based strategies for dealing with bi-objective enhancement. Microsoft's anomalous intrusion detection and big data malware classification have been used to gauge the efficiency of the suggested methodology. © 2023 IEEE.

关键词： Cybersecurity

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A Machine Learning Approach to Compare Causal Inference Modelling Strategies in the Digital Advertising Industry 2

A Machine Learning Approach to Compare Causal Inference Mode...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Singh, Sanskriti Sanjay Kumar Kumar Sinha, Apoorv Pandey, Trilok Nath Acharya, Biswa Mohan School of Computer Science and Engineering Vellore Institute of Technology Chennai India SOA University ITER Dept. of Computer Application Bhubaneswar India

ISBN: (纸本)9798350344882

Cause and effect relationships are not extensively explored in machine learning but prior to creating a system that understands causal relationships, there needs to be a general ability to grasp and inculcate cause-and-effect statistically. The way of choice for the proposed model is causal inference where the causes are inferred from the data. The study of causality, unlike correlation, falls in the domain of prescriptive analytics, that is, the likelihood of an occurrence given that another occurrence has happened. As a result, the study of causality and predictions based on causal inference are highly important when it comes to the domain of marketing and sales, largely because many practices in the domain of advertising try to fundamentally mimic causal inference such as A/B testing, primarily used in e-commerce and the digital advertising industry. For the purposes of the study, the treatment will be exposure to multiple advertisements and the uplift modelling will be used to direct marketing efforts to the most agreeable customers. Uplift modelling is a causal learning approach that estimates the dataset's individual treatment effect and using the same, calculates the incremental impact of a direct treatment on the individual behavior of the customer, which is why the advertising use-case felt particularly suitable. The usual way causal inference is inculcated in machine learning models is that one attribute is taken as a treatment, that is, the intervention or policy that has been done, while the target label is the desired outcome while accounting for the confounders which are the rest of the attributes. However, for the crux of this study, the proposed model will be taking two treatment attributes so as to see how the causality of two outcomes improves or worsens the impact of a rudimentary machine learning model. At the same time, this comparative study will be in tandem with two other models featuring one causal relationship as well as no causal relations

关键词： Sales

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Multimodal Depression Detection Using Audio Visual Cues 1

Multimodal Depression Detection Using Audio Visual Cues

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Tiwary, Gyanendra Chauhan, Shivani Goyal, Krishan Kumar Computer Science and Engineering Bhagwant University Ajmer India Raja Balwant Singh Management Technical Campus Computer Application Department Agra India

ISBN: (纸本)9798350341737

Millions of people all around the world suffer from depression, which is a common mental health issue. Effective intervention and therapy depend on early and precise identification of depression. Multimodal techniques that incorporate audio and video data have recently demonstrated promising outcomes in the detection of depression. In this article, authors suggest a convolutional neural network (CNN) model for multimodal depression identification based on audio and video. The proposed model makes use of audio and visual elements to gather comprehensive and complementary data about depression. Pitch, intensity, and spectral information, among others, are derived from voice recordings for the audio modality. The video modality uses face landmarks, optical flow, and pose estimation techniques to combine facial expressions, head movements, and body gestures. The parallel branches of the CNN architecture process the audio and visual inputs individually. To learn discriminative audio representations, the audio branch applies three convolutional layers, followed by pooling and dense layers. To extract video-specific information, the video branch uses five convolutional layers with different filter widths and depths, followed by fully connected and pooling layers. Further a late fusion technique has been adopted for multimodal fusion, concatenating learned features from the two modalities and passing them through additional thick layers for depression prediction. Authors also used regularization strategies, including dropout and batch normalization, during training to alleviate the overfitting problem. Authors evaluated the proposed multimodal CNN model on a DAIC-WOZ dataset consisting of audio and video recordings of individuals with and without depression. The proposed model achieved 77% accuracy and hence demonstrate that the proposed multimodal CNN model achieves superior performance compared to unimodal approaches. © 2023 IEEE.

关键词： Neural network models

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An Analysis of Edge Computing with Multi Access in 5-G Technology 3

An Analysis of Edge Computing with Multi Access in 5-G Techn...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Gupta, Sitaram Sonia Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

The demands of the rapidly expanding internet. However, since most end users have confined storage space and limiting computing power, it lately evolved into a natural issue how to execute computationally complex apps on resource-bound consumers. The emerging fifth generation (5G) corporation can benefit from Mobile Edge Computing (MEC) innovation upgrade portable assets by facilitating figure concentrated applications, handling a lot of information prior to sending them to the cloud, finding distributed computing assets inside the radio access organization (RAN) close to versatile clients, and giving setting mindful administrations utilizing RAN information. In reality, the paradigm shift from 4G to 5G is accompanied by the introduction of new technical concepts. We first give a comprehensive introduction of MEC technology and some of its possible applications. The most recent research on MEC integration with upcoming technologies that will be employed in 5G and beyond is then is summarized. © 2023 IEEE.

关键词： Mobile edge computing

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Enhancing Dynamic Abstract Generation for Code Summarization using CODE-OAN Models and DBWTFF Frames 2

Enhancing Dynamic Abstract Generation for Code Summarization...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Bhatt, Chandradeep Sharma, Manish Sharma, Sanjay Gupta, Shefali Singh, Teekam Kumar, Mukesh Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Sgrr Univeristy Computer Application Department Dehradun India Chitkara University Institute of Engineering and Tech Chitkara University Punjab India

ISBN: (纸本)9798350306415

This research presents a robustness code abstraction generation method based on an attention mechanism. The method involves extracting high-quality code and its corresponding description language material from the programming community, specifically focusing on the text of query specifications and code responses. Redundant code and its description language material are filtered out, and the query specifications are transformed into declarative sentences for code conversion. Finally, a series model based on the attention mechanism is utilized to generate code summaries. The method effectively eliminates redundancy and noise in the code, leading to improved accuracy rates in automatic judgment, artificial evaluation, and testing. The evaluation results demonstrate superior performance compared to existing baseline methods. © 2024 IEEE.

关键词： accuracy rate attention mechanism code abstraction code summarization declarative sentences description language high-quality code redundancy filtering robustness

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Bengali language-based Disease diagnosis system for rural people using Bidirectional Encoder Representations from Transformers

Bengali language-based Disease diagnosis system for rural pe...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Mandal, Kailash Pati Mukherjee, Prasenjit Chakraborty, Baisakhi Ganguly, Souvik National Institute of Technology Computer Science and Engineering Durgapur India Dr. B.C. Roy Engineering College Bachelor of Computer Application Durgapur India

ISBN: (纸本)9781665492942

Machine intelligence is an essential branch of computer science that imitates human behavior. A human recognizes every real-world object and incident by their distinguishable features. If a computerized system incorporates human cognitive abilities, the system will be able to classify many real-world phenomena accurately. Therefore, an automated system that detects the disease based on symptoms has been addressed. Bidirectional Encoder Representations from Transformers (BERT) has been used to design this system. The BERT model has been developed based on the Transformers concept, forecasting a token by looking at every token in the series. The proposed system recognizes each and every individual's illness in the Bengali language. Punctuation marks are stripped from the user-supplied query which is in form of disease symptoms. Tokens are obtained from the individual's query. Tokens that have been filtered are sent to the proposed system. The proposed method employs filtered tokens to diagnose the disorder. The BERT model is trained using a dataset of diseases and their symptoms obtained from Kaggle. Precision, recall, fl-score, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) have been used to describe the proposed system's performance. The accuracy of the proposed system is 98.12% whereas RMSE, MSE, and MAE exhibit 1.8618, 1.0852, and 1.0826 respectively. © 2023 IEEE.

关键词： Machine learning

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Improving Efficiency and Accuracy in Clinical Diagnostic Decision Making with Deep Learning

Improving Efficiency and Accuracy in Clinical Diagnostic Dec...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kharbas, Vikas Kumar Gobi, N. Kumar, Dinesh Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Deep learning is a modern form of synthetic intelligence (AI) based on large quantities of statistics and algorithms that allow computer systems to approximate features and solutions, ensuing in more excellent, efficient, and accurate selections. In the scientific field, deep gaining knowledge is increasingly used to help clinicians with diagnostic decision-making, decreasing the value and attempt associated with diagnostic approaches. This paper discusses the potential of deep learning to enhance the performance and accuracy of medical diagnostic selection-making. The paper provides an overview of the cutting-edge nation of the field, with a particular awareness of picture processing and its application to improve accuracy and decrease the price associated with scientific diagnoses. It also provides a few capacity challenges and pitfalls and outlines the issues essential to ensure a hit implementation and successful results. Eventually, it offers a few recommendations for similar studies to increase deep getting to know algorithms further. Deep gaining knowledge has been broadly explored in recent years as a powerful tool for enhancing the accuracy and efficiency of clinical diagnostic decision-making. Deep studying models can investigate patterns in medical datasets, deliberating the traits of affected person information and ailment signs to generate extra specific and reliable diagnoses. By incorporating extra facts such as transcripts from affected person-clinician interactions and medical imaging, those fashions may be trained to assist clinicians in identifying illnesses early and provide well-timed and correct analysis. Moreover, deep learning allows for incorporating case-primarily based reasoning and presents the capacity to combine scientific knowledge in real-time. By having extra sturdy and accurate models, clinicians can reduce the time required to diagnose a condition and make treatment choices. Deep gaining knowledge also lets in for the integration o

关键词： disorder enhance healthcare value

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Constructing Conditional Random Fields for Automated Land Cover Mapping from Hyper Spectral Images

Constructing Conditional Random Fields for Automated Land Co...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Sunil Prabha, S. Singh, Vaishali Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper proposes an automatic land cover mapping technique using Conditional Random Fields (CRFs) from hyper-spectral snapshots. It introduces a hierarchical feature vector method to encode the labels' spatial relationships. The paper opinion associated paintings on land cover mappings and introduces the idea of CRFs inside the context of the land cowl category. A description of the facts, pre-processing techniques, and the parameters used for schooling and checking out the model is supplied. The paper develops and evaluates the CRF model, evaluating the results of other land cowl class strategies. Finally, the authors discuss the capacity packages of this technique in the context of other land cowl mapping obligations. This painting is innovative in its method to land cowl mapping and offers a successful implementation of the CRF model for this hassle. It explores using Conditional Random Fields (CRFs) to facilitate automatic land cover mapping from hyperspectral photos (HSI). The CRF method was implemented to estimate the land cover labels in an unsupervised gaining knowledge of style. To this stop, the pixel-level land cowl information for a given area changed into modeled as a graphical version, permitting place-degree marginal probability computations for a dense prediction. Experimental outcomes validated that the proposed technique ought to obtain a sizeable development in the accuracy of land cover mapping for the tested records set compared to its baseline. The proposed CRF-primarily based land cowl mapping method exhibited a higher convergence price and better nearby minima convergence. Moreover, the proposed approach was located to be sturdy in phrases of sensitivity to preliminary land cowl labels. © 2024 IEEE.

关键词： category Conditional evaluating hyper spectral schooling

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