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Integrative Analysis of miRNA-mRNA Expression Data to Identify miRNA-Targets for Oral Cancer 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2021

作者： Mahapatra, Saswati Prasath, Rajendra Swarnkar, Tripti Department of Computer Application Siksha O Anusandhan Deemed to be University Bhubaneswar India Department of Computer Science and Engineering Indian Institute of Information Technology Sri City Chittoor India

ISBN: (数字)9783031215179

ISBN: (纸本)9783031215162

Micro RNAs (miRNAs) are small non coding RNA sequences consisting of 20–23 nucleotides that govern the post transcriptional expression of genes in both normal and disease condition of the cell. Thus, identification of most influencing miRNAs and the associated mRNAs becomes a research quest in diagnostic and prognostic application of cancer. In this study we conducted an integrated analysis of Next Generation Sequencing based miRNA-mRNA expression data to identify dysregulated miRNAs and their target mRNAs for Oral Cancer. A sensible combination of datamining tools such as Random Forest (RF), K-nearest Neighbour (KNN), Support Vector Machine (SVM), log-Fold Change, Adjusted p-values, Matthews coefficient correlation (MCC), Prediction accuracy was considered for this analysis. The prioritized cancer specific target genes obtained in this approach exhibited a MCC value of 0.9 and achieved a consistently higher prediction accuracy of 95% when subjected to classifiers RF, KNN and SVM. These target genes can be presented as predictive variables for early diagnosis of cancer. The selected miRNA-target genes can further be biologically validated to confirm their participation in disease specific pathways and biological processes. © 2022, Springer Nature Switzerland AG.

关键词： RNA

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data Processing Logic 5

Empirical Evaluation of Stock Market Prediction System using...

引用

5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Prabakar, S. Kumar, Ankit Jayakarthik, R. Venkatesh, D. Pratheeba, R.S. Khan, Bhola Isbr Business School Finance and Accounting Bangalore India Faculty of Science Swami Vivekanand Subharti University Department of Computer Application Meerut India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Commerce School of Commerce and Management Mohan Babu University Tirupati India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti601204 India Mjp Rohilkhand University Department of Regional Economics Bareilly India

ISBN: (纸本)9798350385649

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets. © 2024 IEEE.

关键词： Long short-term memory

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A Novel Advanced Transfer Learning Based Approach for Leukemia Detection

A Novel Advanced Transfer Learning Based Approach for Leukem...

引用

Cybernation and Computation (CYBERCOM), International Conference on

作者： Navpreet Kaur Amar Singh School of Computer Science and Engineering Lovely Professional University Phagwara India School of Computer Application Lovely Professional University Phagwara India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

Transfer learning has been adopted more widely to automate detection in recent years. It enables trained models to work well on new data with great transfer learning benefits. In this study, novel advanced transfer learning technique is proposed to identify leukemia. An integration of ResNet50, Principal Component Analysis (PCA) and Big Bang Big Crunch (BBBC) optimization algorithm is proposed. The early, accurate detection of leukemia is no small feat in medical imaging-especially when it comes to huge amounts of complex image data. A transfer learning model called ResNet50 which extracts important features of microscopic images of leukemia cells is used. As there are too many features which made it complex to understand and interpret, PCA is used to reduce some dimensionality and selected only the best few parameters for the further analysis. In addition, the BBBC algorithm inspired by theories concerning the expansion and contraction of the universe is finally utilized for the optimization to take place efficiently and acutely. Experiments on a CNMC_2019 dataset of leukemia demonstrated the superiority and accuracy of the proposed method when compared with other classification methods, especially in terms of reliability. The findings indicate that such an approach may enhance the early diagnosis of leukemia, enabling more reliable diagnosis and individualized treatment planning.

关键词： Accuracy Microscopy Transfer learning Leukemia Reliability theory Feature extraction Planning Optimization Principal component analysis Residual neural networks

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A Novel Hybrid Approach for Threat Detection in Cyber Security using AI algorithm 1

A Novel Hybrid Approach for Threat Detection in Cyber Securi...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Gupta, Abhishek Kumar Dixit, Neetu Kumar, Sujeet Rawat, Priyanka Madhumita Jagannath International Management School Department of Information Technology New Delhi110070 India RBMI Department of Computer Application Greater Noida201306 India GL Bajaj Institute of Management Greater Noida201306 India Lingaya's Vidyapeeth Department of Computer Science & Engineering Faridabad121002 India

ISBN: (纸本)9798350353648

The dynamic cyber-attack landscape might require more traditional ways of detecting and preventing threats. It has also paved the way for new hybrid approaches that merge attributes of two or more different methods, enabling a better and more efficient cyber defence. Such an approach would be to apply artificial intelligence (AI) algorithms to detect threats. In this way, organizations can apply various capabilities to automatically scan network traffic round-the-clock for indicators of suspicious activities, notifying security teams immediately. Despite this, a system that relies entirely on AI algorithms gets many false positives, increasing the time and effort required by security personnel. Our hybrid approach includes human expertise and input to help detect potential risks. This comes as a human-in-the-loop solution, where AI algorithms identify suspicious signals, and humans then corroborate those leads before making an arrest. Combining human and AI intelligence results in a far more accurate and efficient threat detection system. Our strategy incorporates big data analytics and anomaly detection methods to highlight unusual behaviours that suggest a risk is forming. This allows our system to discover and eliminate these cyber attacks in real-time instead of merely reacting after a breach. By leveraging our novel hybrid approach, you can have better coverage and a more robust cyber security plan in the face of threats, giving you a higher level of protection and peace of mind about future attacks. © 2024 IEEE.

关键词： Cyber attacks

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Smart Health Diagnosis:Harnessing Data Mining for Predictive Healthcare

Smart Health Diagnosis:Harnessing Data Mining for Predictive...

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

作者： R. Indrakumari Vishal Pandey Ashish Kumar Sinha Ankesh Kumar School of Computer Science & Engineering Galgotias University India School of Computing Application & Technology Galgotias University India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The use of Smart Health Diagnosis: Data Mining for Predictive Healthcare is to provide a user- friendly and easily accessible platform where individuals can get instant health advice through an intelligent online healthcare system. By the analysis of the reported symptoms, the system can predict potential future diseases and provide relevant recommendations to healthcare providers on courses of action. Intelligent methods accurately determine potential interferences based on patient symptoms. Medical record database was used and symptoms were assigned to underlying diseases using Naive Bayesian calculations. Fast, reliable access to health care is a cornerstone of individual and social health but many people face barriers in receiving timely care. We are trying to solve these problems by introducing Health Prediction System, a revolutionary online healthcare solution that uses data mining for quick support and customized recommendations.

关键词： Databases Smart healthcare Prediction algorithms Bayes methods Data mining Reliability Medical diagnostic imaging Faces Diseases

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Cloud Resource Management: Monitoring 23

Cloud Resource Management: Monitoring

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Anshika Rawat Shubham Singh Pawan Singh Department of Computer Science and Engineering Amity School of EngineeringandTechnology Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India India Computer Engineering and Application Gla University India Department of Computer Science and Engineering Amity School of of Engineering and Technology Amity University Uttar Pradesh India

ISBN: (纸本)9798400709418

The management and use of computer resources within enterprises has changed as a result of the emergence of cloud computing as a paradigm shift. With its diverse and comprehensive portfolio of cloud services, including AWS CloudWatch for monitoring, Amazon Web Services (AWS) is at the vanguard of this revolution. In order to fully utilize the capabilities of these platforms and achieve cost- effectiveness, scalability, and optimal performance, proper cloud resource management is essential. This, study offers a thorough investigation of AWS's cloud resource management, with a focus on Particle Swarm Optimization (PSO) integration as a cutting-edge optimization technique. The dynamic and ever-changing workloads seen in cloud computing systems present a special set of issues for resource allocation and optimization. However, finding the ideal compromise between performance and cost-efficiency is still a challenging task. This study examines the changing environment of cloud resource management with an eye toward the future. In order to enable even more intelligent resource allocation decisions, we take into account the possible integration of machine learning and predictive analytics into our PSO framework. We also look at the broader implications of PSO-driven resource management.

关键词： Amazon Web Services (AWS)

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Measuring the Implications of Email Viruses Through a Unified Model of Cyber Security 6

Measuring the Implications of Email Viruses Through a Unifie...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Adeel, Nazish Kumar, Rajeev Akella, Katyayani N S Manickam, Valli Khan, Mohd Waris Nandury, Satyanarayana V. Integral University Department of Computer Science and Engineering U.P Lucknow India Centre for Innovation and Technology Administrative Staff College of India Telangana Hyderabad India Integral University Department of Computer Application U.P. Lucknow India

ISBN: (纸本)9798350304480

The vast majority of email viruses propagate by ensuring that every address included in the address book of the victim is sent the malicious message or attachment. There are a variety of approaches that can be utilized in order to package and present these viruses. Some of them can be immediately identified as malicious based on the content of the body, the sender, who may or may not be trustworthy, and the subject lines, which are just unintelligible. It may be difficult for receivers to identify individual email messages that include malware since the malicious actor puts a lot of effort into making it appear as though the email message was sent from a respectable sender. Phishing is an attempt to steal sensitive information by impersonating a trustworthy entity, typically a business. Cybersecurity is an extremely important topic in the field of information technology. Information security is one of the most pressing problems facing our generation at the moment. When we think of cyber security, the first thing that comes to mind is cybercrime, which is expanding at an alarming rate. This is because cybercrime is the most common kind of online crime. In order to put an end to these cybercrimes, various governments and businesses are pursuing a variety of preventative measures. In spite of the many safeguards in place, a significant number of people continue to be anxious about the safety of their online activity. The difficulties surrounding the use of email viruses as a threat to cyber security are the primary focus of this article. From the point of view of email viruses, a unified approach for assessing the level of cyber security has been developed. In addition to this, it examines the most recent developments in the methods of cyber security, as well as the ethics and trends that are influencing the industry. © 2023 IEEE.

关键词： cyber security cybercrime Email phishing security measurement

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Bat Algorithm: A Review on Theory, Modifications and applications 3

Bat Algorithm: A Review on Theory, Modifications and Applica...

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3rd International Conference on Intelligent Cybernetics Technology and applications, ICICyTA 2023

作者： Singh, Hakam Witarsyah, Deden Misra, Alok Tiwari, Raj Gaurang Chitkara University School of Engineering and Technology Department of Computer Science and Engineering Himachal Paradesh Baddi India Telkom University School of Industrial and System Engineering Information System Departement Bandung Indonesia Lovely Professional University School of Computer Application Punjab Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350394559

Due to its many applications in a variety of domains, including clustering, cloud computing, and power systems, metaheuristic algorithms have attracted a lot of interest from the scholarly community. By using their self-explorative and adaptive processes, these algorithms provide certain benefits over conventional methods. One well-known metaheuristic approach is effective in resolving optimization problems with constraints and without constraints. This technique was inspired by nature, notably the echolocation ability of bat species. The Bat Algorithm (BA) makes use of a powerful search mechanism to provide the best results when compared to other algorithms of the same class. The theoretical underpinnings of the BA are explored in depth in this research, along with several changes and advancements that have improved its functionality. These enhancements include adjustments to search tactics as well as other developments. The report also thoroughly examines how the BA may be used across a variety of fields, demonstrating its competence and adaptability. The BA has been effectively used in several real-world applications, from cloud computing to clustering, and each one is carefully investigated. The study also offers a thoughtful examination of the BA's possible advancements and future possibilities. It also entails investigating novel modifications and tactics that might improve its effectiveness in handling challenging optimization issues. This study seeks to direct practitioners and academics toward more effective and efficient metaheuristic algorithms, thereby stimulating improvements in optimization approaches. It does this by summarising the present state of the BA and imagining its future development. © 2023 IEEE.

关键词： Cloud computing

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A Method of Constructing and Automatically Labeling Radio Frequency Signal Training Dataset for UAV 48

A Method of Constructing and Automatically Labeling Radio Fr...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Chao Ma, Ruipeng Si, Zheng Chi, Mingmin Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Zhengzhou Zhongke Institute of Integrated Circuit and System Application China Zhengzhou University School of Cyber Science and Engineering China Zhengzhou University Henan Institute of Advanced Technology China

ISBN: (纸本)9781728163277

The problem of signal detection and classification of multiple UAVs can be solved using object detection techniques in computer vision. However, this requires collecting and labeling a large amount of reliable raw data. Since the UAV signal dataset cannot be directly applied to object detection, we propose a method using time-frequency domain filtering and automatic labeling to construct a large-scale time-frequency spectrogram dataset. Experimental results show that the average recognition accuracies of image transmission signals and remote control signals under interference conditions are 97% and 82%, respectively, while the average errors of signal parameters are 0.93% and 5.57%. © 2023 IEEE.

关键词： Object detection

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Web Scraping Techniques and Its applications: A Review 3

Web Scraping Techniques and Its Applications: A Review

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Bhatt, Chandradeep Bisht, Ayush Chauhan, Rahul Vishvakarma, Ashish Kumar, Mukesh Sharma, Sanjay Graphic Era Hill University CSE Department Dehradun India Graphic Era Hill University Computer Science and Engineering Dehradun India Institute of Technology & Management Department of Information Technology Uttarakhand India SGRRITS Computer Application Department Dehradun India

ISBN: (纸本)9798350303360

Web scraping, additionally referred to as web crawling, is an automated data extraction process from websites using specialized software. In the modern-day virtual age, it performs a vital role in fields such as Business Intelligence. By extracting dependent information from HTML textual content, internet scraping allows the retrieval of records no longer ease available in device-readable formats. This paper explores the idea of internet scraping, its operational mechanisms, the ranges involved, and its relevance to domain names like Business Intelligence, artificial intelligence, facts science, huge information, and cybersecurity. It also discusses the ethical issues and legal implications related to web scraping. The paper emphasizes the importance of net scraping inside the information age, highlighting its benefits over guide statistics entry in phrases of thoroughness, accuracy, and consistency. Technologies like spidering and pattern matching are examined as essential additives of hit net scraping implementations. The Python programming language is highlighted as a popular choice for internet scraping projects. Furthermore, the paper gives the ability blessings of web scraping and offers insights into its future trends. © 2023 IEEE.

关键词： Additives

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