A Proposed Blockchain based System for Secure Data Management of Computer Networks

Taif Khalid; Rabah; Manal M.

doi:https://doi.org/10.54216/JCIM.110204

Journal of Cognitive Human-Computer Interaction | 2023

Earthquake Location Forecasting In Map Using XGBOOST Algorithm

Earthquake is one of the most threatening natural disasters which is caused due to the shaking of the earth’s surface. Common cause of earthquake is due to ground shaking, underground volcanic eruption. Here, XGBoost Algorithm is used to predict the location of the earthquake. In this paper, a earthquake location prediction method is proposed, which is based on the composition of a known system whose behaviour is administered according to the evaluation of more than two decades of seismic events and is designed as a time series using Machine learning. By analyzing the parameters such as Latitude, Magnitude, Depth, Longitude, Depth error, Gap, Time etc.

groups

R. Jeena mail -

Kruthika M. mail -

Princy A. mail -

Tanya A. mail

link https://doi.org/10.54216/JCHCI.050104

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2023

Lip Print Scanner

  This paper presents advancement in lip print perception and a advancement of biometric system by scanning the lips and getting the lip prints of the individual for security purposes to safeguard confidential data and information. The method used here to identify the lip prints is Cheiloscopy and which scans the lips with the help of camera with a micro lens to get lip prints. This security system was developed using machine learning in python and IoT system. This security system is an alternative for fingerprint and footprint. The security system here differentiates lips based on lip pigment, texture, and prints presented on the lips and check the database for a match. The security systems are fully developed with IoT systems and machine learning in python. The R-CNN in machine learning is used for lip analysis and supervised learning in machine learning is used to find a perfect match.  

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Jayakaran P. mail -

Jeffery matthew S. mail -

Litheeswaran S. mail -

Mohamed Arshad mail -

Mahajan S. mail -

R. Priya mail

link https://doi.org/10.54216/JCHCI.050105

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

Data Driven Machine Learning For Fault Detection And Classification In Binary Distillation Column

Mathematical programming can express competency concepts in a well-defined mathematical model for a particular Any system that runs is always be expected to experience faults in  different ways. Any change in the physical state of numerous components, control machinery, as well as environmental factors, might result in these problems. In process industries, where prompt detection is crucial in maintaining high product quality, dependability, and safety under various operating situations, finding these flaws is one of the most difficult tasks. The goal of this project is to implement several machine learning techniques for fault identification and classification in a binary distillation column. A pilot binary distillation unit (UOP3CC) is utilized for this purpose. The set up is run under normal operating conditions and the real time data is collected. Three common faults namely reboiler fault, feed pump fault and sensor fault are introduced one at a time and the faulty data is collected. These data are then introduced in to different machine learning algorithms like Logistic Regression, KNN, Naive Bayes, Decision Tree, Gradient Boosting, X Gradient Boosting, SVC and Light Gradient Boosting for model development. 70% of the data samples used for training and 30% of data samples are used for testing. It is found the Decision tree algorithm gives the best accuracy possible with 99.9%. Using decision tree algorithm, fault classification is performed for different datasets and is found that the algorithm was able to classify accurately even for new untrained datasets.

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Silvester Bennys jakes mail -

M. Mythily mail -

D. Vasanthi mail -

D. Manamalli mail

link https://doi.org/10.54216/JCIM.110105

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Performance Enhancement of Intelligent Healthcare-Based Recommendation System with IFODNN Model

Several diseases have been identified as fatal conditions affecting individuals during their middle and old ages worldwide. In recent years, chronic and pulmonary diseases have exhibited the highest mortality rates among all known conditions in this category. Machine Learning (ML) tools have efficiently studied the causes of these harmful diseases, including analyzing large databases. These databases may contain unreliable and redundant features that affect prediction accuracy and speed. Applying, the feature-based extraction and selection methods to remove inconsistent components is essential. This article implements a deep neural network (DNN) technique for diagnosis to classify different diseases. However, the DNN model faces a challenge, specifically hallucination, in accurately classifying diseases. To overcome this, a hybrid optimization DNN model has been introduced. This model is useful for recommending treatments based on the diagnosed diseases. The hybrid optimization DNN model, referred to as the Intelligent Healthcare Recommendation (IHCR) model, is designed to predict and recommend treatments for chronic and pulmonary disease patients. The research model effectively extracts features at a specific level and selects valuable features to provide accurate recommendations. This recommendation phase is followed by a statistical analysis based on probability, which evaluates patients' risk levels. Reliant on the data from the risk analysis (RA), patients are given recommendations regarding the severity and performance of the related diseases for early treatment. The proposed work has been estimated using dissimilar databases based on muti-diseases, and the outcomes appear encouraging. This research aims to develop the IHCR model for chronic and pulmonary diseases. The performance of the implemented recommendation models is evaluated using parameters like RMSE, specificity (SP), sensitivity (SN), and accuracy (Acc). The results of the recommendation model show an Acc of 96.81–97%.

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Gauri Sood mail -

Neeraj Raheja mail

link https://doi.org/10.54216/JISIoT.160121

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

Machine Learning framework for Information Security Management in Big Data Applications

Big data has become an integral part of modern businesses, but its management and protection present numerous challenges, such as securing sensitive information from unauthorized access, preventing data breaches, and ensuring data integrity. This work investigated applying a machine learning (ML) approach to tackling the challenges of information security and management in big data environments. We present an ML framework that leverages a supervised learning strategy to detect anomalies, classify big data, and predict potential security threats. We also investigate the implementation of this framework and its potential benefits, such as reducing false positives and improving detection rates. Our experimental analysis in public datasets demonstrates the effectiveness of our approach in improving information security and management in big data environments.

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Othman Al Basheer mail -

Murat Ozcek mail

link https://doi.org/10.54216/JCIM.110106

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

Balancing Security and Information Management in the Digital Workplace

As the digital workplace becomes more prevalent, organizations are faced with the challenge of balancing security and information management. On one hand, there is a need to protect sensitive data and prevent cyberattacks, while on the other hand, organizations must enable employees to collaborate and share information effectively. Machine learning (ML) is a promising technology that can help organizations address this challenge. By analyzing data patterns and identifying potential security threats, ML algorithms can enhance security measures and mitigate risks. At the same time, ML can also facilitate information management by automating routine tasks and improving the accuracy of data analysis. In this paper, we explore the role of ML in balancing security and information management in the digital workplace. We propose a hybrid ML model that integrates autoencoder and convolutional subnetworks in unified architecture to help capturing and security threats in the digital workplace, without compromising the information management tasks. We also present a case study of a real-world implementation of ML in a digital workplace setting, highlighting the benefits and limitations of this approach. Our findings suggest that ML can be a valuable tool for achieving a balance between security and information management in the digital workplace, but its successful implementation requires careful consideration of organizational context and stakeholder needs.

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Rabah Scharif mail -

Ossama Embarak mail

link https://doi.org/10.54216/JCIM.110201

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

Data Security in Healthcare Systems: Integration of Information Security and Information Management

 Effective management of patient data is critical for improving the quality of care and patient outcomes in healthcare systems. However, ensuring the confidentiality, integrity, and availability of patient data while complying with regulatory requirements can be challenging. To address this challenge, this work proposes an artificial intelligence (AI)-enabled framework that integrates information security (IS) and information management (IM) capabilities into a unified solution for improving the overall functionality of healthcare systems.  The proposed framework leverages AI algorithms to automate managerial transactions of healthcare systems, while ensuring they are secure against possible threats. By automating these tasks, the framework can reduce the burden on healthcare staff, improve the accuracy and speed of information processing, and reduce the risk of human error. Our framework provides accurate and timely information to healthcare providers, enabling them to make informed decisions and provide better care to patients.

groups

Ahmed Abdelaziz mail -

Alia N. Mahmoud mail

link https://doi.org/10.54216/JCIM.110202

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

Securing Information Management in Collaborative Environments Using Machine Learning

Recently, there has been a significant increase in the use of collaborative environments for managing and sharing information. However, these environments often present significant security risks due to the potential for unauthorized access, data leakage, and other security breaches. To address these risks, machine learning (ML) techniques have been increasingly used to secure information management in collaborative environments. We propose a novel ML approach to be applied to detect and prevent security threats in collaborative environments. Our approach integrates temporal convolution to detect and prevent security threats by analyzing spatial-temporal patterns in data from various sources, such as network traffic, system logs, and user behavior. Furthermore, we present a case study demonstrating the effectiveness of our model in securing collaborative information management. The case study involves the development of our system for detecting insider threats in a collaborative environment. Extensive experimentation on this case study demonstrates the efficiency and effectiveness of the proposed system for securing information management and promoting further developments.

groups

Ahmed Hatip mail -

Karla Zayood mail -

Rabah Scharif mail

link https://doi.org/10.54216/JCIM.110203

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2023

Fuzzy Logic Based Load Balanced Clustering for Network Lifetime Enhancement in WSN

Large number of small sensor nodes exists in WSN’s for sensing and collecting information from the environment. In today’s time, these sensor nodes were applied in under water, military area, health care, earthquake sensing and in dedicated areas with recent technologies. Sensor nodes have limited life time and have supplementary network life. Network lifecycle depends on many factors such as connectivity, residual energy, topology types, single hop, multi hop, distance from base station, distance to cluster heads and much more. Among the various solutions given, clustering is considered to be good solution and optimal cluster head selection leads to efficient energy consumption. This paper proposes fuzzy based multi-attributes clustering that balances load among sensor nodes and also gives energy efficient clustering. Here we have used some attributes such as delay, residual energy, distance to CH, standard deviation to average network lifetime and standard deviation to residual energy. Results and experimental analysis validates that the proposed methods outperforms other compared algorithms.

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Ankita Srivastava mail -

P. K. Mishra mail

link https://doi.org/10.54216/IJWAC.070101

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

A Proposed Blockchain based System for Secure Data Management of Computer Networks

As technology continues to evolve, the importance of information security and management becomes more crucial than ever. Blockchain and machine learning (ML) are two technologies that are gaining increasing attention in this field. Blockchain provides a secure and decentralized platform for storing and sharing information, while ML can help detect patterns and anomalies in data to identify potential security threats. This paper proposes a blockchain-based ML system for securing information management by providing an automated service for detecting anomalies in Ethereum transactions. The system utilizes a blockchain network to securely store and manage data, and ML algorithms to analyze and detect potential security threats. We present a case study using the Ethereum Fraud Detection Dataset to demonstrate the effectiveness of our proposed system in detecting fraudulent transactions. Our results show that our system outperforms traditional ML algorithms in terms of accuracy (99.55%), and F1-score (99.98%), highlighting the potential of blockchain-based ML for improving information security and management in various industries.

groups

Taif Khalid Shakir mail -

Rabah Scharif mail -

Manal M. Nasir mail

link https://doi.org/10.54216/JCIM.110204

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new