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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Raj Ratan Sudeept Singh Yadav Ritika Malhotra Avneesh Kumar School of Computer Application and Technology Galgotias University Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

In the contemporary financial landscape, banks play a crucial role in facilitating individuals and businesses with their monetary needs. Central to their operations is the assessment of borrower creditworthiness through loan approval processes, ensuring the security of loans extended. However, traditional manual assessment is not only time-consuming but also prone to human error. Therefore, there is an urgent need to develop an automated and reliable credit approval system. In this study, diverse machine learning calculations are compared to decide the ideal show for anticipating whether a bank ought to endrose a advance for a person. Employing a dataset gotten from Kaggle, diverse machine learning methods to a degree logistic reversion, decision trees, K-nearest neighbors (KNN), haphazard woodlands and neural networks are exploited. The animation of these models is evaluated handling measurements to a degree veracity, accuracy, review and F1 score. In particular, the influence of the model is improved by taking into account a number of factors that include the borrower's personal characteristics, such as tax liabilities, purpose of the loan, credit history, loan amount and home ownership, in addition to traditional checking account information. This holistic approach allows for a more accurate estimation of the likelihood of loan default. Furthermore, the logistic regression model proves to be the top performer with an impressive accuracy of 81.89%. Additionally, a trait importance analysis is performed to identify the variables that have the greatest impact on loan approval decisions. The results underline the superiority of ML models over conventional statistical approaches and illustrate their potential to significantly improve the accuracy of loan forecasting.

关键词： Logistic regression Accuracy Machine learning algorithms Reviews Finance Predictive models Nearest neighbor methods Prediction algorithms Security Random forests

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Detection of Cyber Attack in IoT Based Model Using ANN Model with Genetic Algorithm

Detection of Cyber Attack in IoT Based Model Using ANN Model...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Animesh Srivastava Hemendra Shanker Sharma Rishikesh Rawat Navin Garg Department of Computer Science & Engineering Graphic Era Hill University Dehradun India Department of Computer Science & Application COER University Roorkee India Bansal College of Engineering Bhopal India Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun Uttarakhand India

Thanks to the IoT, our everyday lives are now more accessible, connected, and convenient than ever before. Because it facilitates the effortless transfer of massive amounts of data between interconnected devices, it leaves the network vulnerable to various forms of intrusion and attack. As the number of interconnected devices grows, the need for reliable attack detection systems to forestall malicious actions becomes paramount. This paper introduces a strategy for detecting attacks on the Internet of Things (IoT) that integrates Genetic Algorithms (GA) and Artificial Neural Networks (ANN) models. This paper introduces a model for an artificial neural network that uses genetic algorithms to detect attacks on the Internet of Things. While the GA adjusts the hyperparameters of the ANN model, the ANN model is responsible for determining whether network traffic is malicious or not. To evaluate the proposed system, an Internet of Things network traffic dataset was utilized. As demonstrated by the outcomes, the system has a commendable rate of detection. The system can spot DoS attacks. The results show that our method is great at finding attacks on the Internet of Things. we achieved a 99.3 percent accuracy rate with our suggested ANN with GA.

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Breast Cancer Prediction Using Machine Learning

Breast Cancer Prediction Using Machine Learning

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Shiv Ashish Dhondiyal Pooja Luthra Bhupender Sharma Rajni Rajni Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Chandigarh University Mohali Punjab Department of Computer Science and Application Coer University Roorkee Uttarakhand Department Of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer.

关键词： Image segmentation Accuracy Machine learning algorithms Sensitivity Databases Nearest neighbor methods Prediction algorithms Breast cancer Classification algorithms Random forests

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AvoidPwd: A Mnemonic Password Generation Strategy Based on Keyboard Transformation

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China Communications 2022年第10期19卷 92-101页

作者： Shunying Lyu Qian Yao Jianhua Song School of Computer Science and Information Engineering Hubei University430062 WuhanChina Engineering and Technical Research Center of Hubei Province in Educational Informatization 430062 WuhanChina Engineering and Technical Research Center of Hubei Province in Software Engineering 430062 WuhanChina Engineering Research Center of Hubei Province in Intelligent Government Affairs and Application of Artificial Intelligence 430062 WuhanChina

Identity authentication is the first line of defense for network *** have been the most widely used authentication method in recent *** there are security risks in passwords,they will be the primary method in the future due to their simplicity and low *** the security and usability of passwords,we propose AvoidPwd,which is a novel mnemonic password generation strategy that is based on keyboard *** helps users customize a“route”to bypass an“obstacle”and choose the characters on the“route”as the final ***“obstacle”is a certain word using any language and the keys adjacent to the“obstacle”are typed with the“Shift”key.A two-part experiment was conducted to examine the memorability and security of the AvoidPwd strategy with other three password strategies and three leaked password *** results showed that the passwords generated by the AvoidPwd strategy were more secure than the other leaked password ***,AvoidPwd outperformed the KbCg,SpIns,and Alphapwd in balancing security and *** addition,there are more symbols in the character distribution of AvoidPwd than the other *** is hopeful to solve the security problem that people are difficult to remember symbols and they tend to input letters and digits when creating passwords.

关键词： password generation strategy password security mnemonic passwords

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Reliable and Stable Routing in VANET Using Multi-Objective Function Aided K-Means Clustering with Heuristic Algorithm

Reliable and Stable Routing in VANET Using Multi-Objective F...

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Trends in engineering Systems and Technologies (ICTEST), International Conference on

作者： Nabanita Das Maushumi Barooah Anupam Das Department of Computer Application Assam Engineering College Jalukbari Guwahati Assam India Department of computer science and engineering The Assam Royal Global University Betkuchi Guwahati India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307801

The expansion of Vehicular Ad-hoc Networks (VANET) has brought a heightened focus on service delivery within the realm of the smart transit systems. Nevertheless, the mobility attributes inherent to vehicular networks often result in frequent route disruptions, particularly when transmitting data. This circumstance holds true in both advanced and emerging nations, where substantial time is wasted due to congested traffic conditions. These consequences are substantial, encompassing heightened stress levels for drivers, reduced efficiency for diverse personal and commercial vehicles, and escalated emissions of harmful gases, especially those that contribute to air pollution, affecting the well-being of residents in densely populated regions. Clustering stands as a formidable approach for establishing a stable topological arrangement. Therefore, an efficient reliable routing scheme is developed in VANET with the help of a clustering algorithm. The node clustering is initially performed with the help of the K-means Clustering algorithm (KMC) in VANET. An Advanced Pelican Optimization Algorithm (APOA) is utilized for choosing a shorter and more stable route between all available routes to make efficient routing in VANET. The multi-objective functions like delay, hop count, energy, and the ratio of packet delivery are considered for the efficient routing in VANET to make energy-efficient transmission. The effectiveness of the proposed KMC-based optimal routing in VANET is validated with the conventional routing schemes in accordance with some of the metrics to determine efficacy.

关键词： Vehicular ad hoc networks Clustering algorithms Routing Throughput Reliability engineering Stability analysis Delays

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Sparse Attention-based Hierarchical Node Representation for Spatial Domain Identification

Sparse Attention-based Hierarchical Node Representation for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Peng, Wei Ping, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350386226

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM © 2024 IEEE.

关键词： Contrastive Learning

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Analyzing the Performance of Machine Learning Algorithms for Real-Time Data Analysis Under Optimization 3

Analyzing the Performance of Machine Learning Algorithms for...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Vijayalakshmi, V.J. Senthilkumar, K. Mirajkar, Riddhi R. Adlin Jebakumari, S. Goyal, Manish Kumar Sakhare, Nitin N. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Jain Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

In current years, studies has shifted away from conventional statistical methods to include more sophisticated gadget studying algorithms including support vector machines, random forests, and deep getting to know for real-time statistics analysis. But, such algorithms have their very own precise challenges, particularly associated with optimization and performance. This studies paper seeks to discover the challenges posed by applying gadget studying algorithms to actual-time datasets and talk the tactics and strategies to optimize the algorithms for stepped forward performance. Using numerous optimization strategies inclusive of evolutionary algorithms, simulated annealing, and evolutionary techniques are introduced to beautify the performance of the machine mastering algorithms. Additionally, the usage of go-validation, hyper parameter tuning, and ensemble methods to enhance the accuracy and pace of the algorithms is investigated. This paper additionally presents an in depth evaluation of the results received from exceptional optimization strategies with regards to overall performance. In the end, potential areas for in addition studies are discussed in detail. © 2023 IEEE.

关键词： Evolutionary algorithms

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Wireless LAN Security 6

Wireless LAN Security

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6th International Conference on Signal and Image Processing, ICSIP 2021

作者： Al-Shebami, Taha Abdullah Ali Al-Shamiri, Abdulkawi Yahya Radman Department of Computer Application Technology School of Computer Science and Information Engineering Hefei China School of Computer Science and Engineering Hodeidah University Department of Computer Science Al-Hodeidah Yemen

ISBN: (纸本)9780738133737

The proliferation of wireless networks has increased the risks and threats to the security and privacy of its users in assault of privacy, penetration, and piracy of information and data. Due to their consideration as an open communication environment and data is available in space, where radio waves are used to exchange information;that is making them vulnerable to eavesdropping and active attack. Therefore, wireless security is very important to reduce these attacks and threats, and to reduce the risks and attacks caused by security vulnerabilities that threaten wireless networks, it is necessary to find security solutions to prevent attacks and threats on these networks. This paper presents ways to achieve security in wireless networks, to know the types of attacks and threats on these wireless networks, to identify ways, which used to manage and secure wireless networks, and then access to protect these networks against violations and interventions, and how to prevent or minimize these risks with a set of recommendations and proposals. © 2021 IEEE.

关键词： Network security

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SLSNet: Weakly-Supervised Skin Lesion Segmentation Network with Self-attentions 20th

SLSNet: Weakly-Supervised Skin Lesion Segmentation Network w...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Pei, Songwen Huang, Junjie School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China

ISBN: (纸本)9789819970247

computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tackle this problem, a new weakly-supervised skin lesion segmentation network with self-attentions named SLSNet is proposed. SLSNet contains two modules and uses image-level labels as supervision information. One module named Intra-image Self-attention Seed Expansion (ISE) digs intra-image self-attentions with an expansion loss and a confidence loss to expand seed areas. The other module named Inter-image Affinity-based Noise Suppression (IAS) suppresses the noise pixels in attention maps via inter-image correlations. Extensive experiments conducted on ISIC-2017 dataset show that SLSNet achieves relatively high performance while reducing human labeling efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Expansion

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Understanding the Impact AI has on Data Access Performance and Efficiency 3

Understanding the Impact AI has on Data Access Performance a...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Rege, Pallavi R. Ahluwalia, Gunveen Kumar, Ritesh Narasimhayya, B.E. Yadav, Surendra Karthikeyan, Kavitha Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Chennai127 India

ISBN: (纸本)9798350319125

AI has come to be more and more outstanding in recent years in records get entry to overall performance and efficiency. As a result, there were big modifications to how facts are processed and accessed. AI has enabled organizations to extra effectively and efficaciously technique facts, ensuing in accelerated operational efficiency and improved customer satisfaction. This paper presents a comprehensive exploration of the effect of AI on records get entry to performance and performance. It examines the usage of AI to streamline statistics processing and get admission to, and discusses the impact it has on consumer revel in and patron satisfaction. additionally, the paper considers the capacity for future innovation and the way facts get admission to overall performance and performance may be further more desirable via AI. The paper concludes with implications for similarly research and improvement. © 2023 IEEE.

关键词： Capacity Implications Satisfaction Similarly

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