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2nd International Conference on Cybersecurity and Evolutionary Data Engineering, ICCEDE 2022

作者： Dutta, Anurag Munjal, DeepKiran Department of Computer Science and Engineering Government College of Engineering and Textile Technology Serampore India Department of Computer Applications G L Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9789819950799

The Dutch National Flag problem—DNF Problem, a Computational Problem proposed by the Dutch computer Scientist—Edsger Wybe Dijkstra—brought a new edge to the domains of Sorting. In this work, we would extend Dijkstra’s Problem to a generalized version, where we would be considering flags with variable numbers of colours and will try to devise an algorithm to arrange them in a monotonic order by its magnitude. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Computational complexity Dnf sort Partitioning Sorting algorithms

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An Analysis of Spam Detection using NLP

An Analysis of Spam Detection using NLP

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Jerosin R.V Kalpana K Department of Computer Applications Hindustan Institute of Technology and Science

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

In the digital era, effective spam detection is essential to ensure secure communication, this paper examines the use of Natural Language Processing (NLP) techniques methods on email and SMS platforms combined with a Recurrent Convolutional Neural Network (RCNN) for spam classification. By integrating RNNs to capture sequential patterns and CNNs to extract local features, RCNN proves to be highly efficient in text classification. The model is trained on email and SMS datasets, leveraging NLP methods like tokenization, stopword removal, and word embeddings to analyze message content. It then classifies messages as either spam or non-spam (ham) with high accuracy and minimal false positives. The experimental results show that the traditional Support Vector Machine (SVM) algorithm achieves an accuracy rate lower than 90%, while the proposed RCNN model demonstrates a notable improvement, reaching an accuracy of 98%. Additionally, the study discusses challenges in spam detection, such as adapting to evolving spam strategies and linguistic variations. The findings demonstrate that the RCNN-based approach enhances spam filtering, ensuring more effective and reliable communication security.

关键词： Support vector machines Adaptation models Accuracy Social networking (online) Unsolicited e-mail Text categorization Feature extraction Message services Tokenization Reliability

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Deep Learning-Based Self-Supervised Transfer Learning for Medical Image Classification 2

Deep Learning-Based Self-Supervised Transfer Learning for Me...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Shaikh, M.Z. Singh, Samender Varshney, Neeraj Saraswat, Birendra Kumar Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India

ISBN: (纸本)9798350371055

Self-supervised switch getting to know is a practical approach for the scientific image type. It includes using unlabeled statistics (from non-clinical images) to teach models to categorize medical photos with low attempts and excessive accuracy. This approach has been utilized in numerous duties, including clinical photo segmentation, computer-aided prognosis, and disease category. To summarize the research studies carried out in this discipline, a complete survey of self-supervised switch learning for scientific image type was performed in 2020. The survey evaluations numerous transfer mastering techniques often used in healthcare, which include area adaptation, multitasking studying, and zero/one-shot mastering. It additionally gives an in-intensity analysis of the modern-day challenges and capability solutions. © 2024 IEEE.

关键词： Medical Outperforms Results Unlabeled Utilizes

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Breast Cancer Prediction using Feature Selection and Classification with XGBoost

Breast Cancer Prediction using Feature Selection and Classif...

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2023 IEEE International Conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Haarika, Raye Babu, Tina Nair, Rekha R. Rajesh, T.M. Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Applications Bengaluru India

ISBN: (纸本)9798350396195

As a result of high population growth, medical research and early sickness identification has become a responsibility. As the inhabitants grows, the likelihood of succumbing to breast cancer rises. A illness detection system that's automated reduces the danger of mortality, provides dependable, efficient, and rapid reaction, and assists doctors in disease diagnosis. to reinforce the predictive efficacy of current models and To estimate the risk of breast cancer, proposed model used Extreme Gradient Boosting (XGBoost) and compared it to Logistic Regression (LR), The Random Forest (RF), and Support Vector Machines (SVM). In this work, target picking the most weighted features using the XGBoost method and training the model with those features was performed. This approach yielded the identical accuracy as training the model with all features, however training the model with all features takes longer. The suggested approach has produced findings that are comparable and may help radiologists provide opinions © 2023 IEEE.

关键词： Population statistics

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A Detailed Survey on Data Mining based Optimization Schemes for Bioinformatics applications 1

A Detailed Survey on Data Mining based Optimization Schemes ...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Thareja, Preeti Chhillar, Rajender Singh Department of Computer Science and Applications Maharshi Dayanand University Rohtak India

ISBN: (纸本)9781607685395

In computational science paradigm, bioinformatics and the data mining approach afford specific and advocating research and development schemes. The technical science of mining, managing and coordinating of resources retrieved from the biological sequences is known as bioinformatics. Some of the current developments, for example, genome-sequencing initiatives, microarrays, proteomics, functional and the essential genomics are ought to be strapped the frontlines of human acquaintance. In recent times, the data analytics and artificial learning methods have stayed progressing in paces. This has emerged through the high-impact applications from promotion to science. The two regions obligate generally been increasing individually;even though scholars have disbursed much exertion on data mining for bioinformatics'. This kind of distinctive problem objects to conduit the available gap concerning bioinformatics system and data mining techniques by unstinting exploration assimilating this two. It is believed that the data mining might deliver required tools for enhanced thoughtful of gene expression, drug strategy and further developing complications in genomics and proteomics. This current survey provides a comprehensive analysis on data mining centered bioinformatics and its applications. © The Electrochemical Society

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Fake News Detection Model for Regional Language 7

Fake News Detection Model for Regional Language

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Nair, Jayashree Akhil, S.S. Harisankar, V. Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9781665421683

Social media has expanded far and wide in the past few years, which has led to the ease of circulating any kind of news and posts all over the network. The work that we are pursuing currently focuses on the prevention of the circulation of large, misinformed news over the network. The different machine and deep learning techniques incorporated with certain Natural Language Processing techniques can result in achieving such good models to filter the fake news and real news apart from themselves. The social media spectrum is active in other low-resource language domains like Malayalam exactly how it prevails in other languages which currently aids in the inflation of rumors and false accusations among the masses. The main objective is to classify the incoming Malayalam contextual news and other rumors to aid humans in fast-tracking the manual verification or detection process of the news. The introduction of the classifier with an additional front-end interface to assist the people in the time of a crisis would help the crowd to a great degree with dynamic regular feeding of real-time data to the language model. © 2022 IEEE.

关键词： Data mining

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Integrated Machine Learning approach for Attack Detection in MQTT-Enabled Smart Home Systems 1

Integrated Machine Learning approach for Attack Detection in...

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1st International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2024

作者： Ray, Abhay Kumar Sharma, Rupak Pandey, Sunil Kumar Department of Computer Applications SRM- Institute of Science & Technology Delhi –NCR Campus Modinagar Ghaziabad India Department of Computer Applications SRM- Institute of Science & Technology Delhi –NCR Campus Modinagar Ghaziabad India Department of IT Institute of Technology & Science Mohan Nagar Ghaziabad India

ISBN: (纸本)9798350391268

With the worldwide rising demand for smart home technologies and home automation, which ensures better privacy and security by using IoT devices in these environments has become more important. This research paper discusses and explores the MQTT protocol, which is an efficient and lightweight messaging protocol commonly used in IoT systems for communication. To identify and mitigate cyber-attacks in smart home systems, this study applies a set of machine learning algorithms to analyze MQTT attack dataset, which aims to detect and classify various cyber-attacks using multiple machine learning approaches with high accuracy and combine their prediction to find out the final result. The study begins with an introduction of smart home, its components, basic introduction, architecture of MQTT protocol, and major attack vectors targeting MQTT-based systems, highlighting vulnerabilities that can be exploited by attackers. Afterward this research paper proposes and implementing a model which uses a set of machine learning algorithms on MQTT attack dataset to train and test models, these become capable to identify anomalies in MQTT traffic, which enables the detection of attacks such as dos attack, flooding packet attack, SlowITe Attack and brute force attack for unauthorized access of servers or systems. Feature selection and data standardization done, all trained and tested models demonstrate high accuracy and effectiveness in threat detection and classification on a comprehensive MQTT dataset. This study illustrates the effectiveness and good application of AI/ML-driven approaches for security leak prevention which is boosting the smart home environments security attack detection and contributing towards the secure IoT ecosystems. © 2024 IEEE.

关键词： Anomaly Detection Attack Identification and classification Cyber Attacks Machine Learning MQTT Protocol Smart Homes

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Dynamic Trust Management in IoT Networks using Reputation-Based Systems

Dynamic Trust Management in IoT Networks using Reputation-Ba...

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

作者： Naidu, D J Samatha Jain, Upma Shruthi, S. Rao, K. B. V. Brahma Iqbal, Syed Mohammad Uzair Natrayan, L. Annamacharya Pg College of Computer Studies Department of Master of Computer Applications India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bms Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India Symbiosis Law School Department of Law Pune India Simats Saveetha School of Engineering Department of Mechanical Engineering India

ISBN: (纸本)9798350375442

The Internet of Things (IoT) enables intelligent data exchange across many kinds of interconnected devices in a diverse and unpredictable physical setting. The Internet of Things (IoT) network used in this study is a cluster-based sensor network that includes a base station and an application server that offers various services in a cloud setting. In this setting, assaults may occur in both device-to-device and user-to-device communication. For pervasive devices with limited resources, the use of conventional encryption techniques to address this problem is not practical. In addition, the majority of current trust-based security solutions either address the user level or the devices level. Therefore, this study aims to tackle environment-specific security concerns at the device and user levels in light of the environment's inherent dynamic and dispersed nature. This research explored the usage of a Trust Based Security Framework (TBSF) and Reputation Based Systems in the Internet of Things (IoT) to accurately describe the actions of devices and users. Fuzzy logic was used to calculate both the device trust and the user trust. A trust-based access control system, secure cluster head selection, and safe routing were all built using these trust values. At first, the security of device-to-device communication was addressed by the design and effective implementation of the Trust Aware Device Access Control System (TADACS). © 2024 IEEE.

关键词： Access control

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IoT-Based Crop Yield Prediction System in Indian Sub-continent Using Machine Learning Techniques

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Remote Sensing in Earth Systems sciences 2023年第3-4期6卷 156-166页

作者： Nithya, V. Josephine, M.S. Jeyabalaraja, V. Department of Computer Applications Dr. M.G.R. Educational and Research Institute Tamil Nadu Chennai India Department of Computer Science Engineering Velammal Engineering College Tamil Nadu Chennai India

The agricultural sector holds significant importance in the socio-economic structure of India. The inability of farmers to make informed decisions regarding the optimal crop selection for their land, based on non-scientific and traditional methods, poses a significant challenge for a nation where farming constitutes the livelihood of nearly 58% of the population. At times, farmers have experienced challenges in selecting appropriate crops that align with the soil conditions, sowing season, and geographical location. By collecting data from sensors in the fields, ranchers can easily ascertain the extent of the crops, which helps preserve scarce resources and lessens the negative effects of weeds on the environment. The majority of Indian farmers choose conventional farming methods when deciding how their crops should be grown. Regardless, ranchers do not jeopardize agricultural yields, which are sensitive to abiotic factors such as soil type. In this proposed method, two types of datasets have been collected from the southern and northern parts of the country through IoT-based sensors. The crop recommendation system has been processed with various machine learning algorithms like logistic regression, KNN, decision tree, and support vector machine through the collected dataset. The accuracy of 97% and 96% has been achieved for processed datasets in the southern and northern parts of India. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Accuracy Crop recommendation Decision tree IoT KNN Logistic regression Machine learning Precision farming SVM Training

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Bibliometric Analysis and Visualization of Scientific Literature on Random Forest Regression

Scientific Visualization

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Scientific Visualization 2022年第5期14卷 54-65页

作者： Babu, Sherin Thomas, Binu A Department of Computer Science Assumption College Autonomous Kottayam Kerala Changanacherry India B Department of Computer Applications Marian College Autonomous Idukki Kerala Kuttikanam India C School of Computer Sciences Mahatma Gandhi University Kerala Kottayam India

Random forest regression (RFR) is a versatile, easy-to-use and efficient tree based machine-learning algorithm that utilizes the power of multiple decision trees for making decisions. So random forest is a subject of a great deal of research, in many of the machine intelligence applications. The objective of this research is to investigate the scientific output of research based on RFR and to explore its hotspots and frontiers through bibliometric analysis for the years 2007 to 2019. The data are collected from the Web of science database. The total number of publications, the citations, and types of publications, publication countries, productive authors, prominent journals, and keyword co-occurrence of RFR research are examined, using VOSviewer software. There are 516 papers, published in 299 journals, of which researchers from the USA published 162 articles. The most prolific author, with 6 publications and 240 citations, is Martin H. Teicher. The most cited article is the research article entitled "Genomic selection in wheat breeding using genotyping by sequencing". Among the journals, the most articles (41 publications) are published by the Remote Sensing journal. © 2022 National Research Nuclear University. All rights reserved.

关键词： Machine learning

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