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Investigation of trees algorithms for improving quality of client's data in data mining tasks

Investigation of trees algorithms for improving quality of c...

2020 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2020

作者： Razmochaeva, Natalya V. Klionskiy, Dmitry M. Saint-Petersburg Electrotechnical University 'Leti' Department of Software Engineering and Computer Applications St. Petersburg Russia Saint-Petersburg Electrotechnical University 'Leti' Department of Computer Science and Engineering St. Petersburg Russia

ISBN: (纸本)9781728181790

Machine learning methods, which are tree-based algorithms, have found their application in the task of improving the quality of client data. Among such methods are distinguished: decision trees and decision forests, random trees, ensembles of trees and forests. Such methods are used both to save large amounts of data from redundant information, to restore missing values and to determine anomalous values. Subsequently, on the data transformed by such algorithms, it is possible to build highly accurate forecasts of changes in the target parameter, whether it is the size of profit or investment. Thus, the effectiveness of applying algorithms on trees in a new field for the introduction of machine learning is proved. © 2020 IEEE.

关键词： Decision trees

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Enhancing Security in the Internet of Things: A Trust-Based Protocol for Resilient Communication

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SN computer Science 2024年第1期5卷 4页

作者： Althaf Ali, A. Hussain, Mohammed Muzaffar Subramaneswara Rao, A. Lavanya, S. Feroz Khan, A.B. Department of Computer Applications Madanapalle Institute of Technology & Science (MITS) Andhra Pradesh Madanapalle India Department of Computer Science and Engineering C. Abdul Hakeem College of Engineering and Technology Tamil Nadu Melvisharam India Department of Software Applications Thiruthangal Nadar College Tamil Nadu Chennai India Department of Computer Science Syed Hameedha Arts and Science College Tamil Nadu Kilakarai India

The quick increasing in the Internet of Things (IoT) devices has raised significant security concerns, particularly in the face of reactive jamming attacks. This paper proposes a trust-based protocol named Trust-Based Protocol for Resilient Communication (TBRC) that incorporates a robust key management technique to improve the security of IoT systems and mitigate the impact of reactive jamming attacks. To assess the effectiveness of our protocol, the work is compared with existing schemes such as CKMT, EDDK, and CEMT. Through extensive simulations under realistic conditions, we analyze key performance metrics, including energy consumption, packet loss, computational time, and throughput. The comparative analysis demonstrates that our proposed protocol outperforms CKMT, EDDK, and CEMT in terms of computational efficiency, throughput, and delay, making it a more effective solution for secure IoT communication. By leveraging a robust key management technique, our protocol provides a secure and resilient communication framework for IoT environments, effectively countering reactive jamming attacks. The integration of this technique strengthens the overall security posture of IoT networks, ensuring reliable and secure communication between devices. This work contributes to advancing IoT security by introducing a trust-based protocol with a robust key management technique that effectively mitigates the impact of reactive jamming attacks. The comparative evaluation against existing schemes validates the superiority of our proposed approach in terms of security, efficiency, and reliability. The results of our evaluation highlight the improved performance of our protocol, positioning it as a promising solution for real-world IoT deployments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DDoS IoT Security Trusted metrics

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A performance analysis method for service-oriented cloud applications (SOCAs)

A performance analysis method for service-oriented cloud app...

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2012 International Conference on computer Communication and Informatics, ICCCI 2012

作者： RajaRaajeswari, S. Selvarani, R. Raj, Pethuru Department of Computer Applications RajaRajeswari College of Engineering Bangalore India Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology Bangalore India Sify Software Ltd. Tidel Park Chennai India

ISBN: (纸本)9781457715822

With the memorable start and massive adoption of cloud concepts, infrastructures and platforms, there is a paradigm shift in the IT landscape. That is, IT solutions and services are being developed, debugged, deployed and delivered from clouds to be conveniently consumed by global users via the open and public web. The point is that the web and the cloud resources are smartly combined and leveraged to ring in a series of delectable improvements and improvisations in the ways and means in which IT is being approached, analyzed, and articulated. With the availability of slim and sleek devices in our hands and pockets, the cloud idea is to enter into a totally exotic and eerie phase. One noteworthy derivative out of all these hot happenings in the cloud world is the eruption of service oriented cloud applications (SOCAs) due to the cool convergence of service orientation and cloud paradigms. In this paper, we have explained the performance bottlenecks of SOCAs by taking into account a number of diverse scenarios. Further on, we have presented a new performance calculation as well as enhancement mechanism for this type of flexible and futuristic cloud-based applications. © 2012 IEEE.

关键词： Information services

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Intrusion Detection Mechanism Using Deep Learning 5

Intrusion Detection Mechanism Using Deep Learning

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ananthi, P. Nirmaladevi, K. Naveen Kumar, S. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India M.Sc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331518097

In today's connected and data-driven world, networks and digital systems need to be protected from malicious attacks. The effectiveness of conventional Intrusion Detection Systems (IDS) in recognizing and impeding novel threats and intricate methodologies has its limits. This study introduces a new method for improving network security by using deep learning techniques in the design and development of an intrusion detection system (IDS). The suggested Intrusion Detection System uses deep learning techniques, specifically Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs), to identify and react to known and new security threats. Data can be taught to be encoded into a lower-dimensional representation and then decoded back into its original form using autoencoders (AEs). Autoencoders are a cutting-edge intrusion detection system that can lower false positives and raise overall detection accuracy because of their capacity to automatically learn from and adjust to the ever-changing threat landscape. They enhance computer networks capacity to spot irregularities and possible security threats. With the deep learning model obtaining a high accuracy of 99% in real-world threat detection, the results show that deep learning is effective in developing intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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Double Encryption Technique for Sharing and Storing the Images in the Cloud Environment 4

Double Encryption Technique for Sharing and Storing the Imag...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Anusha, P. Maruthi, R. Kumar, D. Maghesh Begum, H. Parveen Periyar Maniammai Institute of Science and Technology Department of Software Engineering Tamil Nadu Thanjavur India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350343670

Cryptography is used by all organizations to protect the data files and ensures confidentiality mainly at the time of sharing and storing in the cloud data storage. The cloud service providers use a wide range of tools and techniques for encryption and storage technologies. The intruders are finding number of ways to break or attack the cryptographic systems, so that new ways are constantly being explored to safeguard our files from the attackers This paper explores double encryption cryptographic technique based on Elliptic curve cryptography (ECC) and Advanced Encryption Standard (AES) to perform end-to end encryption for sharing and storing files in the cloud environment. © 2024 IEEE.

关键词： Advanced Encryption Standard Cloud Cryptography Elliptic Curve Cryptography File Sharing

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Location-based collaborative filtering for web service recommendation

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Recent Patents on computer Science 2019年第1期12卷 34-40页

作者： Venkatachalaappaswamy, Mareeswari Ramaraj, Vijayan Ravichandran, Saranya Department of Computer Applications and Creative Media School of Information Technology and Engineering Vellore Institute of Technology-VIT VelloreTamil Nadu India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology-VIT VelloreTamil Nadu India Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology-VIT VelloreTamil Nadu India

Background: In many modern applications, information filtering is now used that exposes users to a collection of data. In such systems, the users are provided with recommended items’ list they might prefer or predict the rate that they might prefer for the items. So that, the users might be select the items that are preferred in that list. Objective: In web service recommendation based on Quality of Service (QoS), predicting QoS value will greatly help people to select the appropriate web service and discover new services. Methods: The effective method or technique for this would be Collaborative Filtering (CF). CF will greatly help in service selection and web service recommendation. It is the more general way of information filtering among the large data sets. In the narrower sense, it is the method of making predictions about a user’s interest by collecting taste information from many users. Results: It is easy to build and also much more effective for recommendations by predicting missing QoS values for the users. It also addresses the scalability problem since the recommendations are based on like-minded users using PCC or in clusters using KNN rather than in large data sources. Conclusion: In this paper, location-aware collaborative filtering is used to recommend the services. The proposed system compares the prediction outcomes and execution time with existing algorithms. © 2019 Bentham Science Publishers.

关键词： Collaborative filtering

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A Comparative Analysis and Prediction of Diabetes Using Machine Learning Approaches 5th

A Comparative Analysis and Prediction of Diabetes Using Mach...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Ansari, Gufran Ahmad Bhat, Salliah Shafi Bhoite, Sachin Department of Computer Science and Engineering and Software Engineering University of Hail Hail Saudi Arabia Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai600048 India Pune411 038 India

ISBN: (纸本)9789819763177

Diabetes is a leading cause of death. People with type 1 diabetes (T1D) can face a number of consequences from their illness. Hyperglycemia is a symptom of T1D disease. Hyperglycemia can be symptoms that can be used for predicting diabetes in patients using the machine learning approach (MLA). Learning algorithms play a role in disease detection and prediction of decision-making. The leading cause of diabetes is really a high blood sugar level or a lack of insulin. As a consequence, it is critical to develop a reliable method to predict diabetes. Once it becomes a serious health concern, it will damage different organs such as the lungs, brain, nerves, blood vessels and heart as a result. Several approaches for detecting diabetes have been used in recent decades. Early diabetic diagnosis is currently very important to help the patients’ health, and this can be done by utilizing a variety of procedures. Tenfold cross-validation technique was used to train dataset for classification. In this paper, authors use different classifications of algorithms such as random forest, decision tree, logistic regression and multilayer perceptron for finding T1D patients. The accuracy of MLP is 98.077% which is the highest among other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

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computers, Materials & Continua 2023年第9期76卷 3933-3948页

作者： S M Hasan Mahmud Md Mamun Ali Mohammad Fahim Shahriar Fahad Ahmed Al-Zahrani Kawsar Ahmed Dip Nandi Francis M.Bui Department of Computer Science American International University-Bangladesh(AIUB)Dhaka1229Bangladesh Centre for Advanced Machine Learning and Applications(CAMLAs) DhakaBangladesh Department of Software Engineering Daffodil International UniversityDhakaBangladesh Department of Computer Engineering Umm Al-Qura UniversityMecca24381Saudi Arabia Department of ECE University of SaskatchewanSaskatoonSK S7N5A9Canada Group of Biophotomatiχ Department of ICTMBSTUSantoshTangail1902Bangladesh

Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory *** common symptoms ofADare losingmemory and early *** addition to these,there are several serious impacts ***,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured ***-stage detection is the most challenging task for controlling and mitigating the impact of *** study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the *** build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and *** stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five *** this research,we applied different preprocessing steps with augmentation techniques to efficiently *** integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and *** three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these *** analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%*** result of the study indicates that the model is highly potential to detect AD in the initial phase.

关键词： Alzheimer’s disease early detection convolutional neural network data augmentation random oversampling machine learning

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Guest Editorial Special Issue on Security and Privacy in Cyber-Physical Systems

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IEEE Internet of Things Journal 2017年第6期4卷 1798-1800页

作者： Yu, Wei Fu, Xinwen Song, Houbing Economides, Anastasios A. Jo, Minho Zhao, Wei Department of Computer and Information Sciences Towson University TowsonMD21252 United States Department of Computer Science University of Central Florida OrlandoFL32816 United States Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Computer Networks and Telematic Applications Laboratory University of Macedonia Thessaloniki546 36 Greece Department of Computer Convergence Software Korea University Seoul Korea Republic of University of Macau China

A typical cyber-physical system (CPS) refers to a system that features a tight integration of computation, networking, and physical elements for interactions between cyber and physical spaces. The Internet of Things (IoT) is considered to be the networking infrastructure of CPS. applications of CPS cover numerous smart-world research areas that our daily life depends upon, including smart transportation, smart electrical power grid, smart cities, smart medical systems, smart manufacturing systems, and others. While major research on improving the efficiency and reliability of CPS by using advanced information and communication technologies has been conducted, the risks of cyberspace security and privacy breaches in CPS need to be seriously investigated before a massive deployment of CPS technologies can or should be realized.

关键词： Special issues and sections Cyber-physical systems Smart cities Smart grids Manufacturing processes

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Animation Qr-Codes for Facial Biometry Tasks and Its applications 10

Animation Qr-Codes for Facial Biometry Tasks and Its Applica...

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10th Mediterranean Conference on Embedded Computing, MECO 2021

作者： Kukharev, Georgy Kazieva, Nazym Shchegoleva, Nadezhda Saint Petersburg Electrotechnical University 'Leti' Department of Software Engineering and Computer Applications Saint Petersburg Russia L.N. Gumilyov Eurasian National University Department of Radio Engineering Electronics and Telecommunications Nur-Sultan Kazakhstan

ISBN: (纸本)9780738133614

The article shows the history of the development of badges as an image accessory designed to represent and/or identify people at a service checkpoint, in a conference or other mass event related to a meeting and contacts of people. This story covers: simple badge cards with printed information about a person, electronic badges with scrolling text, badges with controlled display of QR codes and graphic portraits of people who carry these badges, smart identification and contact control badges. At the same time, it is noted that the latest ideas in the development of badges support the technical side of the creation of 'Electronic Citizen' and, while not entirely perfect, are focused on more local use. One of the alternative solutions is the use of color animated biometric QR codes that store facial biometric information about a particular person - his color graphic portrait, QR codes with documentary and biometric information about him. © 2021 IEEE.

关键词： Biometrics

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