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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Singh, Jagendra Kaliyar, Rohit Kumar Kumari, Roshan Sharma, Namita Manjunath, S. Prasad, P. Dharani School of Computer Science Engineering and Technology Bennett University Greater Noida India Noida Institute of Engineering & Technology Greater Noida India Smt. & Sri Y.E.R Government First Grade College Department of Computer Science Pavagada India MB School of Pharmaceutical Sciences Mohan Babu University Dept. of Pharmacology Tirupati India

ISBN: (纸本)9798350376470

This study looks into the efficacy of machine learning (ML) models for early diagnosis of cervical cancer utilizing cervical imaging. They were meticulously built using a dataset of 2322 photos from hospitals and online archives. Following preprocessing and grayscale modification to separate cancer areas, features such as intensity, entropy, cluster shadow, grayscale variance, size, shape, and intensity were retrieved. Three machine learning models (ML) were trained on the retrieved features: Convolutional neural networks (CNN), VGG 16, and Recurrent neural network (RNN). The dataset was separated into 70% testing and 30% training, which produced reliable findings. The CNN model performed the best, with an accuracy of 98.77%, followed by VGG 16 (94.65%) and RNN (87.65%). These models also displayed great sensitivity and specificity, validating their ability to effectively identify malignant and nonmalignant cervical lesions. When trials are compared to existing studies, significant improvements are regularly observed, highlighting the potential of machine learning to revolutionize cervical cancer research beyond what has previously been developed. This work represents a big step forward in the use of computer intelligence for early identification, enabling prompt therapies, and improving outcomes in cervical cancer patients. The findings highlight the impact of ML techniques in increasing changes in diagnostic accuracy and accessibility, and present interesting possibilities for further research and practical application. © 2024 IEEE.

关键词： Adversarial machine learning

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Identifying Hacking Failures in the IaaS, PaaS, and SaaS Networks to Secure Cloud Applications

Identifying Hacking Failures in the IaaS, PaaS, and SaaS Net...

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engineering and Technology (PICET), Parul International Conference on

作者： Rakesh Bhavsar Vishvjit Thakar Dept. of Computer Science Engineering School of Engineering Indrashil University Mahesana Gujarat India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

From small businesses to major corporations, Cloud computing is quickly taking over as the preferred platform. due to the obligation to guarantee that cloud apps are safe and can shield data from unauthorized attackers. According to the joint responsibility model of Cloud Security, organizations who utilize these services are also responsible for maintaining their degree of security while service providers are responsible for making sure that their services are safe. A shared responsibility Paradigm for identity and access management transfers part of the client's responsibility for application security. Setting up procedures for identity management and access control as well as carrying out ethical hacking and penetration testing are required to guarantee the greatest degree of data security. Since Cloud Security has developed into a shared duty between the user and the service provider, clients are now required to take accountability for the security of their applications in the cloud. IaaS and PaaS services are the major beneficiaries of this. Advanced security procedures like ethical hacking and penetration testing are necessary to provide a safe virtual environment. Organizations will be able to avoid harmful assaults and data leaks thanks to these efforts. Raising risk visibility, identifying vulnerabilities, minimizing risks, and gathering crucial telemetry data are the greatest ways to enhance security. The primary goal of this study article is to assess the techniques for designing a framework or checklist to identify associated risks & vulnerabilities while at the same time attacking cloud-based applications. As a consequence, malicious actors will be kept out of cloud-based programs.

关键词： Cloud computing Ethics Scalability Computational modeling Cloud computing security Companies Safety Security computer crime Penetration testing

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Software Defined Network Based Next Generation Mobile Communication Network Architecture

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第23期18卷 138-148页

作者： Kumar, Sumit Ben Sujin, B. Bhavani, K. Srilatha, Doddi Anand, Ketan Dept. of CSE Haridwar University Uttarakhand Roorkee India University of Technology and Applied Sciences Nizwa Oman Department of Computer Science and Business Systems Panimalar Engineering College Tamil Nadu Chennai India Department of CSE Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Sreenidhi Institute of Science and Technology Telangana Hyderabad India

As mobile networks and network speeds become more prevalent, the demand for marketing strategies increases. The operators are thinking about this, and the development of 5G communication networks is one of their main concerns. As the need for higher transmission speeds increases, 5G networks face challenges of scalability and adaptability. The next-generation mobile network (NMCN-SDN) architecture proposed in this study is based on Software-Defined Networks (SDN). A new network model called computer-defined networking allows for dynamic network definition and programming. A network simulator is created to examine the efficiency of the built infrastructure under different network conditions, including throughput, latency, and resource consumption. In this paper, the comparison of end-toend latency between the standard communication architecture and the proposed NMCN-SDN architecture is done. The results show that the proposed architecture has less space in various conditions compared to existing communication architectures in various conditions. © 2024 by the authors of this article.

关键词： 5G end-to-end latency mobile communication resource utilization Software-Defined Networks (SDN) throughput

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Enhancing Brain Tumor Detection in MRI Images Through Fine-Tuning of VGG 16 Networks: A Precision Diagnostic Approach

Enhancing Brain Tumor Detection in MRI Images Through Fine-T...

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2024 International Conference on Communication, Computing and Energy Efficient Technologies, I3CEET 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Yadav, Satya Prakash Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798331541583

This paper explores the impact of fine-tuning on brain tumour diagnosis using MRI data and a pre-trained VGG 16 model on ImageNet. Our objectives in fine-tuning the model were to improve its generalisation ability and diagnostic accuracy. Our results demonstrate a considerable improvement in the model's performance, with decreased validation loss and increased validation accuracy. The findings show that, despite the possibility of overfitting, fine-tuning is a helpful technique for deep learning model optimisation in medical image analysis. By enhancing diagnostic skills in healthcare, particularly in the early detection of brain tumours, this tactic may enhance patient outcomes. © 2024 IEEE.

关键词： Deep learning

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Recognition of Identifying Malicious Cyber Attack Intention in Cyber Physical System

Recognition of Identifying Malicious Cyber Attack Intention ...

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International Conference on computer and Communications Technologies (ICCCT)

作者： P Vasanthan S. Srinivasan Dept. of Information Technology Sri Sairam Engineering College Dept. of Computer Science and Engineering R.M.D Engineering College

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

As the threat over the cyber-attack is predominantly increasing in every aspects such as based on network traffic, spoofing, phishing attacks the intrusion detection system is used. The network layer is maliciously attacked by the intruder. The intruder tries to capture network packets of the connection between client and server. Based upon the encryption standard, the intruder tries to comprise the user system. Intrusion cannot be easily detected since it is a manual process. In this novel approach, deep learning reinforcement technique is implemented. The bidirectional end to end communication along with the Bayesian inverse reinforcement learning is used to detect the malicious cyber intention in cyber physical system. Inverse Bayesian reinforcement learning technique is initiated to identify the behavior of the pattern recognition and to resample the dataset. This deals with the risk factor and determines whether the connection is comprised. In this work, the novel application is introduced with deep reinforcement learning to identify the intruder. The intrusion detection is labeled in dataset. These dataset implements reward functions to detect the intrusion over the cps.

关键词： Uncertainty Phishing Intrusion detection Telecommunication traffic Probability distribution Bayes methods Pattern recognition Servers Cyberattack Standards

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Visual Exploratory Data Analysis of the Covid-19 Pandemic in Nigeria: Two Years after the Outbreak 5

Visual Exploratory Data Analysis of the Covid-19 Pandemic in...

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5th International Conference on Information Technology for Education and Development, ITED 2022

作者： Orji, Ugochukwu. E. Ukwandu, Elochukwu Obianuju, Ezugwu. A. Ezema, Modesta. E. Ugwuishiwu, Chikaodili. H. Egbugha, Malachi. C. University of Nigeria Dept. of Computer Science Nsukka Enugu Nigeria Cardiff School of Technologies Cardiff Metropolitan University Dept. of Applied Computing and Engineering United Kingdom Ahmadu Bello University Dept of Computer Engineering Zaria Nigeria

ISBN: (纸本)9781509064229

The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far. © 2022 IEEE.

关键词： Data Analytics

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Genetic Testing of Huntington's Disease by Facial Examination of a Mankind using AI programs 3

Genetic Testing of Huntington's Disease by Facial Examinatio...

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3rd International Conference on Innovations in computer science and Software engineering, ICONICS 2022

作者： Atruba Feroze, S. Faiza Nasim, S. NED University of Engineering and Technology Dept.of Computer and Information System Engineering Karachi Pakistan NED University of Engineering and Technology Dept. of Computer Science and I.T NEDUET Karachi Pakistan

ISBN: (纸本)9781665461412

This Genetic disorder is considered as one of the major challenges faced by Medical sciences as it is inherited by humans from one generation to another. With an increasing number of natives of the World at a higher rate, it causes a major impact on information society socially and economically during their lifetime. The citizens of Pakistan and Indian sub continents are facing genomic disease in probably high frequency as compared to other continents. Occurrence of congenital diseases in Pakistan results in higher percentages of unusual genetic diseases especially in children. After analyzing all the current scenarios, some goals and objectives were settled in order to facilitate and give general awareness to people. The objectives of systematic review are evaluation of diagnostic accuracy of AI for the identification of genome contributions to the pathogenesis of Huntington's disease through facial probe AI to accurately identify genetic disorders. The narrative review is performed in Ned University. In this proceeding sequence, 30 cases of both Normal and Genetically affected Patients were documented in which some certain sorts of tests were performed by using facial recognition. For this FR test, at first a combo list of information of all six emotions of a human being i.e. (Happy, Sorrow, Amazed, Fear, Anger, Revulsion) is gathered in the form of mutated images, which was then later be arranged into a pseudo-random order. Now with the help of Vision Technology we have applied some CV Techniques for the extraction of large pattern sets of features. Lastly the purpose of ML and DL techniques is to receive and compare those morphed image data from the Facial Database in order to identify each of six different emotions of HDNormal Person. This whole procedure is conducted in real time. Hence in this way we may recognize the HD genetic disorder among the people in an efficient way. The detailed procedure is explained in the Methodology section of this narrative revi

关键词： Genome

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Prostate Cancer Detection Using Integrated Multi Modal Approaches

Prostate Cancer Detection Using Integrated Multi Modal Appro...

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

作者： Rao, Manjula Gururaj Priyanka, H. Ahamed Shafeeq, B.M. Hemant Kumar Reddy, K. Nmam Institute of Technology Depatment of Computer Science and Engineering Nitte India Pes University Bengaluru India Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Cse Manipal India Vit Ap University Dept. of Cse Inavolu India

ISBN: (纸本)9798350375442

Cancer is one of the terminal diseases that significantly affects an individual's health. Tumours are created when aberrant cells proliferate and divide out of control, posing a threat to neighbouring tissues and organs. Early detection through regular screenings and check-ups can make cancer more treatable. Lots of people throughout are impacted by prostate cancer, the cancer in males that is most frequently diagnosed. The proposed model is a multimodal hybrid approach that utilizes data *** files containing prostate attributes and prostate images. It employs both ML and DL models to classify the data as cancerous or non-cancerous and can further stage the cancerous cells into four stages. The proposed method uses the majority voting system to identify the cancerous or non-cancerous. The model achieves an overall accuracy of 81.48 %, with the ML model at 77.8% and the DL model at 87%. © 2024 IEEE.

关键词： Lung cancer

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Causal Structure Learning from Imperfect Longitudinal Data in Healthcare 11

Causal Structure Learning from Imperfect Longitudinal Data i...

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11th IEEE International Conference on Healthcare Informatics, ICHI 2023

作者： Yang, Haoyu Tourani, Roshan Li, Jia Caraballo, Pedro Steinbach, Michael Kumar, Vipin Simon, Gyorgy University of Minnesota Dept. of Computer Science and Engineering Minneapolis United States University of Minnesota Institute for Health Informatics Minneapolis United States Mayo Clinic Dept. of Internal Medicine Rochester United States

ISBN: (纸本)9798350302639

Causal Structure Discovery (CSD) is the task of learning the set of underlying causal relationships from observational data. Due to their computational scalability and flexibility, a recently developed class of CSD methods, NOTEARS, based on a formulation allowing for continuous optimization, is gaining popularity. However, this formulation can lead to incorrect edge orientations when little/no likelihood advantage is conferred upon any edge orientation, e.g., a → b → c versus c → b → a. In longitudinal data, like electronic health records (EHRs), temporal relationships are observable among many pairs of variables. Such temporal relationship is imperfect but still suggest an orientation since causal effects cannot travel backwards in time. Following this idea, we propose methods to incorporate precedence constraints into continuous optimization-based CSD methods. Experiments on both a synthetic and two real-world datasets validate the effectiveness of the proposed precedence constraints. © 2023 IEEE.

关键词： Machine learning

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Preparing and Annotating a Set of Relevant Contextual Words from the Text Corpus and the Online Newspaper Archives in Bengali

Preparing and Annotating a Set of Relevant Contextual Words ...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Alok Ranjan Pal Prithwish Adhikari Abhishek Dutta Antara Pal Diganta Saha Dept. of Computer Science and Engg. College of Engg. and Mgmt. Kolaghat India Dept. of Computer Science and Engg. Atharva College of Engineering Mumbai India Dept. of Computer Science and Engg. Jadavpur University Kolkata India

Context analysis is one of the obvious tasks in nearly all computational linguistics based works. All of these works pursue this task upto different extents using different techniques. Most of the techniques include the linguistic features, like - N-grams, content words, function words, noun phrase and verb phrase, theme extraction with relevance scoring, facets etc. There are lots of machine learning and deep learning based tools available for this task. The performances of these tools are variable according to different situations. But, the close observations cite that the performance of these systems mainly depends on the availability of the contextual words at that particular experiment. Except for a good collection of contextual words, no system can produce an effective result by using only some complex mathematical calculations. This scenario is the motivation behind this work. In this work, an attempt has been made to construct a structured set of most relevant contextual words according to the classification of text domains, mentioned in the Technology Development for Indian Languages (TDIL) corpus. As it is a group work, therefore different categories of people have been involved in this work for preparing, annotating, verifying and validating the data sets.

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