Title– CYBER CRIME AND ITS CLASSIFICATION

Author– Osman Goni

Abstract

We live in the age of Modern world. Modern world makes our life essay and comfortable. Modern world creates variety of Crime. Cyber Crime is one of them. Cyber Crime is a common phenomenon in the world. Cyber Crime is that group of activities made by the people by creating disturbance in network, stealing others important and private data, documents, hack bank details and accounts and transferring money to their own. Cyber Crime, especially through the internet, has grown in importance as the computer has become central to commerce, entertainment, and government. Cyber Crime, also called computer crime, the use of a computer as an instrument to further illegal ends, such as committing fraud, Trafficking in child pornography and intellectual property, stealing identities, or violating privacy. The Cyber Crime and they its impacts over the society in the form of economical disrupt, psychological disorder, threat to National defense etc. Restriction of cyber-crime is dependent on proper analysis of their behavior and understanding of their impacts over various levels of society. Now a day’s Cyber Crime is increasing day by day. People have been greatly suffering for it. It is not only creating human suffering but also puts effects on the economy. It is impossible to solve the problems by the government alone. So Cyber Crime is one of the major crimes done by computer expert.  This paper describes the Cyber Crime and variety types of Cyber Crime

Index Term– Cyber Crime, Classification, Cyber Criminal, Hacker, Virus, Attack, Intellectual Property crime.

DOI- 10.30696/IJEEA.X.I.2021.01-17

Reference to this paper should be made as follows:  Osman Goni, (2022), “Cyber Crime and Its Classification” Int. J. of Electronics Engineering and Applications, Vol. 10, No. 1, pp. 01-17, DOI 10.30696/IJEEA.X.I.2021.01-17

Title– An Innovative Artificial Intelligence-Driven Metaheuristic Approach for Boosting the Profitability of Wind Power Generation Systems

Author– Prasun Bhattacharjee1 , Rabin K. Jana2 , Somenath Bhattacharjee3

Abstract

When climate change is prompting cataclysmic repercussions of various human activities universally, wind power generation systems propound a pertinent substitute to traditional hydrocarbon-based fuels for dwindling greenhouse gas discharges. Financial effectiveness is a crucial aspect of carbon-neutralization of power generation industries as propositioned in the Paris accord of 2015. The present study intends to boost the yearly profit of wind power generation systems by implementing an innovative transformation of the Genetic Algorithm approach. A novel dynamic method for apportioning the crossover and mutation probabilities has been applied to optimize the deemed objective. Authentic air-flow form of an onshore wind power generation site in India has been engaged for computing the yearly profit. The study outcomes substantiate the advantageous capability of the proposed innovative approach for increasing the financial viability of wind power generation systems with two randomly chosen topography situations.

Index Term– Crossover, Genetic Algorithm, Mutation, Profitability Maximization, Wind Farm.

DOI- 10.30696/IJEEA.X.I.2018.18.27

Reference to this paper should be made as follows:  D. Wilson, S. Rodrigues, C. Segura, I. Loshchilov, F. Hutter, G. L. Buenfil, A. Kheiri, E. Keedwell, M. Ocampo Pineda, E. Özcan, S. I. V. Peña, B. Goldman, S. B. Rionda, A. Hernández-Aguirre, K. Veeramachaneni and S. Cussat-Blanc, “Evolutionary computation for wind farm layout optimization,” Renewable Energy, Vol. 10, No. 1, pp. 18-27., 2018.

Title– DOG BREED CLASSIFICATION USING DEEP LEARNING

Author– Reethik Prasad, Prateek Pardeshi and Mr. T.K Kumar

Abstract

Deep learning algorithms model can be skilled by wide research and study which also provide the vitality to train it . The complications of Information arrangement and prophecy of information can be also sorted through deep learning. For picture revelation and allocation ,Convolutional Neural Networks (CNNs) gives single method. For detecting dogs in challenging photographs we use CNN based accession and as a result we reflect on the identification and description of one of several dog breeds.The standard metrics were verified by experimental conclusion analysis and it is confirmed by the graphical representation that the algorithm (CNN) provides good search efficiency for all datasets evaluated.

Index Term– Deep learning, Classification, Preprocessing, Colab Machine learning, Overfitting.

DOI- 

Reference to this paper should be made as follows:  

Title– Security & Privacy Concerns, Medjacking and Attacks in IoT Healthcare System

Author– Mukesh Choubisa , Prof.SO Khanna

Abstract

The Internet of Technology (IoT) is an emerging technology in computer science society. IoT Internet based information architecture facilitate the exchange of information from one place/system to another place/system. The Internet of technology has the principle of providing an IT-infrastructure to exchanges of „„things‟‟ in a reliable and secure manner in network. The Internet of Things (IoT) refers to a basic concept of linked/connected devices of all types over the Internet wireless or wired. The popularity of IoT has improved rapidly, as these technologies are use by many organizations for various purposes, including medical devices, network communication, education, business development and transportation.

Index Term– IoT Security, IoT Privacy, IoT in Healthcare, Medjacking in IoT, IoT Healthcare Security and privacy challenges, IoT healthcare systems, security of IoT, privacy, information security.

DOI- 

Reference to this paper should be made as follows:  

Title– Weighted Clustering Ensemble with Base Clustering Frequency and Diversity

Author– Arko Banerjee, Suvendu Chandan, Chhabi Rani Panigrahi

Abstract

Clustering Ensemble, also referred as Consensus Clustering, is a tool for enhancing the reliability and stability of data clustering by aggregating the base clusterings obtained by different clustering algorithms in the input ensemble. This study introduces a novel ensemble selection strategy for establishing consensus clustering. Our strategy avoids looking at the entire population of base clusterings in the ensemble in order to establish a quality consensus by carefully selecting a few base clusterings. The experimental results reveal that the suggested method’s consensus clustering surpasses some other well known clustering ensemble methods in terms of clustering accuracy for diverse data sets.

Index Term– Ensemble Clustering; Consensus; Frequency; Diversity.

DOI- 10.30696/IJEEA.X.I.2022.47.57

Reference to this paper should be made as follows:  A. Ben-Hur, A. Elisseeff and A. Guyon, “A stability based method for discovering structure in clustered data”, Pacific Symposium on Biocomputing, 2002, pp. 47-57.