Impact and Research Challenges of Penetrating Testing and Vulnerability Assessment on Network Threat

2022 International Conference on Business Analytics for Technology and Security (ICBATS), Journal Year: 2023, Volume and Issue: unknown, P. 1 - 8

Published: March 7, 2023

The primary goal of this research article is to identify prevalent network dangers and provide countermeasures these threats. In modern era, everyone has access the internet. SECURITY a serious issue that faces. Every day, experienced hackers breach security exploit weaknesses gain top secret confidential data. To avoid risks, we proposed vulnerability assessment penetration testing (VAPT). This technique addresses CIA's principles confidentiality, integrity, availability. All three goals refer keeping your data safe out hands intruders. We uncover weak places in system during assessment, strategies keep secure from prevent possible attacks via testing. paper presented most in-depth description VAPT, discussing many processes methodologies Vulnerability Assessment Penetration Testing.

Language: Английский

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Development and use of Artificial Intelligence in the Defense Sector

2022 International Conference on Business Analytics for Technology and Security (ICBATS), Journal Year: 2023, Volume and Issue: unknown, P. 1 - 10

Published: March 7, 2023

The revolutionary possibilities made possible by artificial intelligence (AI) extend to every aspect of human life. However, if it is a multiplier progress, also force. It has major concern for the military establishments nations twenty-first century. When civilian and AI are combined, new actors uses can arise. It's crucial war knowledge paves way automated decision- making workflows. far-reaching consequences nuclear strategy because makes that plan irrelevant raises prospect conflict. Furthermore, its applications represent genuine revolution in managing future wars. Because importance deciding superiority, poses serious threat strategic stability. Keywords: AI, Military Application, Defence Strategy, National Approaches, Robot

Language: Английский

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Research Challenges and Future Facet Of Cellular Computing

2022 International Conference on Business Analytics for Technology and Security (ICBATS), Journal Year: 2023, Volume and Issue: unknown, P. 1 - 8

Published: March 7, 2023

cellular computing is promising and exciting domain. As the field continues to evolve, several future facets are expected emerge. One potential facet development of more sophisticated efficient cellular-based systems that can perform complex calculations data processing tasks. This could lead creation new tools technologies in various fields, including medicine, biotechnology, environmental monitoring. Moreover, technology has changed way communication, sharing exchanging data. Mobile grown be very vital as number devices have elevated fairly preceding years. In this paper, we mentioned computing, aspects cell working computing. Also, discussed what challenges most common last merits, demerits, application, its future.

Language: Английский

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Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia

Frontiers in Bioengineering and Biotechnology, Journal Year: 2024, Volume and Issue: 11

Published: Jan. 8, 2024

Dementia is a condition (a collection of related signs and symptoms) that causes continuing deterioration in cognitive function, millions people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining rely primarily on clinical examinations, analyzing medical records, administering neuropsychological testing. However, these methods time-consuming costly terms treatment. Therefore, this study aims present noninvasive method early prediction so preventive steps should be taken avoid dementia.

Language: Английский

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A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

SN Computer Science, Journal Year: 2022, Volume and Issue: 3(6)

Published: Aug. 10, 2022

Language: Английский

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Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning

Sensors, Journal Year: 2022, Volume and Issue: 22(19), P. 7483 - 7483

Published: Oct. 2, 2022

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or genetic renal disease, few patients survive because there no method for early prediction of cancer. Early helps doctors start proper therapy treatment the patients, preventing transplantation. With adaptation artificial intelligence, automated tools empowered with different deep learning machine algorithms can predict cancers. In this study, proposed model used Internet Medical Things (IoMT)-based transfer technique to in its stages, patient's data security, incorporates blockchain technology-based private clouds transfer-learning trained models. To cancer, biopsies kidneys consisting three classes. The achieved highest training accuracy 99.8% 99.20%, respectively, augmentation without augmentation, 93.75% during validation. Transfer provides promising framework combination IoMT technologies technology layers enhance diagnosing capabilities

Language: Английский

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Improving Pneumonia Detection in chest X-rays using Transfer Learning Approach (AlexNet) and Adversarial Training

2022 International Conference on Business Analytics for Technology and Security (ICBATS), Journal Year: 2023, Volume and Issue: unknown, P. 1 - 7

Published: March 7, 2023

The method outlined in this paper employs transfer learning and adversarial training to enhance the precision of pneumonia identification chest X-rays. authors use AlexNet deep architecture, pre-trained on large-scale ImageNet dataset, extract relevant features from X-ray images. They then fine-tune network a smaller dataset annotated X-rays, focusing detection task.To address problem limited labelled data, technique called training, which involves second generate synthetic X-rays that are like real but differ subtle ways. By combination they can improve its robustness generalization performance.The evaluate their approach widely accessible pneumonia, achieving significantly higher accuracy compared previous methods. also demonstrate is effective reducing risk overfitting improving network's ability generalize new Overall, presents promising could have important implications for diagnosis treatment common potentially life-threatening condition.

Language: Английский

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IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning

Computational Intelligence and Neuroscience, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 8

Published: July 21, 2022

A genetic disorder is a serious disease that affects large number of individuals around the world. There are various types illnesses, however, we focus on mitochondrial and multifactorial disorders for prediction. Genetic illness caused by factors, including defective maternal or paternal gene, excessive abortions, lack blood cells, low white cell count. For premature teenage life development, early detection diseases crucial. Although it difficult to forecast ahead time, this prediction very critical since person's progress depends it. Machine learning algorithms used diagnose with high accuracy utilizing datasets collected constructed from patient medical reports. lot studies have been conducted recently employing genome sequencing detection, but fewer presented using history. The existing use patient's history restricted. internet things (IoMT) based proposed model in article uses two separate machine algorithms: support vector (SVM) K-Nearest Neighbor (KNN). Experimental results show SVM has outperformed KNN methods terms accuracy. achieved an 94.99% 86.6% training testing, respectively.

Language: Английский

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Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach

Genes, Journal Year: 2022, Volume and Issue: 14(1), P. 71 - 71

Published: Dec. 26, 2022

Genetic disorders are the result of mutation in deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer’s, cancer, Hemochromatosis, etc. Recently, use artificial intelligence-based methods has shown superb success prediction and prognosis different diseases. The potential utilized predict genetic at an early stage using genome data for timely treatment. This study focuses on multi-label multi-class problem makes two major contributions disorder prediction. A novel feature engineering approach is proposed where class probabilities extra tree (ET) random forest (RF) joined make a set model training. Secondly, utilizes classifier chain multiple classifiers predictions all preceding used by conceding final Because data, macro accuracy, Hamming loss, α-evaluation score evaluate performance. Results suggest that extreme gradient boosting (XGB) produces best scores with 92% 84% accuracy score. performance XGB much better than state-of-the-art approaches, terms both computational complexity.

Language: Английский

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Machine-Learning Model for Genetic Disorder Prediction

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 133 - 144

Published: Jan. 1, 2025

Language: Английский

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