The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

Organizational Change: Sociological Perspectives, David Collins, Routledge, London, 1999, 214 pp, ISBN 0‐415‐17156‐3 (paperback) £16.99

This study examines the link between corporate social responsibility and consumer purchase intention by focusing on the mediating role of corporate reputation and the moderating role of peer pressure. The results of distinctive data from 378 consumers of Pakistan reveal that corporate reputation positively relates to consumer purchase intention, and that corporate reputation fully mediates the positive influence of corporate social responsibility on consumer purchase intention. We also found that peer pressure moderates the relationship between the corporate reputation and consumer purchase intention. Furthermore, the mediating effect between corporate reputation and consumer purchase intention is moderated by peer pressure, that is to say moderated mediation. We discuss implications of these findings for research and practice.

https://ojs.excelingtech.co.uk/index.php/IJSCM/article/download/1777/935

The Impact of CSR on Consumers Purchase Intention: The Mediating Role of Corporate Reputation and Moderating Peers Pressure

In order to study the relationship between employees perceived of CSR activities which hold by organizations influence employee job performance, this paper integrates Corporate Social Responsibility Theory (CSR Theory) and Social Identity Theory (SIT) in a new model. The proposed model employs organizational commitment as the mediator to study the relationship between employees perceived of CSR and employee job performance. The research employs quantitative research and distributes questionnaire to 281 respondents who are the staffs working in household products manufacturers in southern China. The data has been separated in five parts to analysis, they are demographic data of respondents, variable analysis of means, variable analysis of factor analysis, variable correlation and the result of hypothesis. The results of this study showed that employees perceived of CSR have positive influence on employee job performance.

Effect of corporate social responsibility on employee job performance: A case study of household products manufacturers in southern China

The study examine the correlation between employees job performance and corporate social responsibility, through the use of  qualitative and quantitative research design paradigm, to do that effective there research objectives and two hypotheses were design; (1)To identify the contribution of employees job performance  to corporate social responsibility (ii)To determine ways how corporate social responsibility can be improve in organization (iii)To analyzes the organizational  excellence and the corporate social responsibility under the period under  review. H1: there is a positive relationship between employee’s job performance and the corporation social responsibility H0: there is no positive relationship between employee’s job performance and the corporation social responsibility, in order to find the correlation between the two variables. To test the hypotheses, correlation was conducted between the independent and dependent variable which the degree of correlation to 0.625 showing high level of correlation hence the null hypothesis is rejected and the alternative hypothesis is accepted. And the results from the content analysis of qualitative also accept the alternative hypothesis and reject the null. Cronbach alpha statistic was also conducted for test of reliability which shown 0.951 which shows that the instrument used is highly reliable. Hence the study shows that there is a positive and significant relationship between the two important variable of the study employees job performance and corporate social responsibility.

/pdf/employees-job-performance-and-pursuit-of-corporate-social-beg4uv3pmq.pdf

Employees Job Performance and Pursuit of Corporate Social Responsibility in the Banking Industry a Case Study of Zenith Bank Plc & First Bank Plc Jalingo

In Sentiment Analysis, there are three (3) approaches namely, machine learning, lexicon-based and ruled based approaches. This study investigates on machine learning approaches which involves text vectorization or word embedding- an essential step in natural language processing tasks since most machine learning algorithms work with numerical input. Text vectorization involves the representation or mapping of words or documents of a corpus to numerical vectors of numbers or real numbers. There are several approaches in the literatures on document/text representation, however this study will focus on three (3) commonly used ones viz; Bag of words, TF-IDF, word2vec and doc2vec, and try to identify the reason behind that for review and recommendation to the researchers in hurry. Review of this study shows that TF-IDF feature vector representations generally outperforms other two (2) vectorization methods word2vec and doc2vec, specifically in book review sentiment classification. And therefore recommended for future studies in book review data set

https://slujst.com.ng/index.php/jst/article/download/266/115/1397

Sentiment Classification: Review of Text Vectorization Methods: Bag of Words, Tf-Idf, Word2vec and Doc2vec

Artificial Intelligence (AI) tools are promising multifaceted techniques for addressing the most mundane tasks for greater efficiency and high productivity. Cyber security space is one of the areas that AI is promising to revolutionize. This study will develop a conceptual and theoretical framework to support a research design that can simulate research in understanding how AI can be applied to Cyber Kill Chain phases. This study has reviewed 21 journal and conference articles mostly from IEEE Xplore database. An overview of the application of artificial intelligence (AI) in cybersecurity, particularly within the framework of the Cyber Kill Chain was provided in this study. It also emphasizes the limitations of traditional security approaches and the necessity for innovative and intelligent defense methodologies. The results of reviewing the relevant literatures discovered that the key components of cybersecurity, includes identity, asset management, automated configuration management, security control validation, governance, risk assessment, and vulnerability identification. A theoretical framework was developed which introduces the Cyber Kill Chain model with a Unified Kill Chain model to address its shortcomings. Application of AI in cybersecurity offers an optimistic solutions to address the evolving threat landscape. AI techniques, such as machine learning, anomaly detection, and behavioural analysis, have shown great potential in enhancing various aspects of cybersecurity. However, challenges related to data quality, adversarial attacks, and privacy concerns need to be addressed for successful implementation. Further research and development are crucial to fully harness the power of AI in cybersecurity and stay ahead of evolving cyber threats. 

Cyber Kill Chain Analysis Using Artificial Intelligence

Nowadays, people find it easier to express opinions via social media-formally known as Web 2.0. Sentiment analysis is an essential field under natural language processing in Computer Science that deals with analyzing people's opinions on the subject matter and discovering the polarity they contain. These opinions could be processed in collective form (as a document) or segments or units as sentences or phrases. Sentiment analysis can be applied in education, research optimization, politics, business, education, health, science and so on, thus forming massive data that requires efficient tools and techniques for analysis. Furthermore, the standard tools currently used for data collection, such as online surveys, interviews, and student evaluation of teachers, limit respondents in expressing opinions to the researcher's surveys and could not generate huge data as Web 2.0 becomes bigger. Sentiment analysis techniques are classified into three (3): Machine learning algorithms, lexicon and hybrid. This study explores sentiment analysis of Web 2.0 for novice researchers to promote collaboration and suggest the best tools for sentiment data analysis and result efficiency. Studies show that machine learning approaches result in large data sets on document-level sentiment classification. In some studies, hybrid techniques that combine machine learning and lexicon-based performance are better than lexicon. Python and R programming are commonly used tools for sentiment analysis implementation, but SentimentAnalyzer and SentiWordnet are recommended for the novice. 
Keywords:   Sentiment Analysis; Web 2.0; Applications; Tools; Novice

Sentiment Analysis in the Era of Web 2.0: Applications, Implementation Tools and Approaches for the Novice Researcher

Flood disaster, is a natural endemic, that has engulf Taraba State, rendering lives and the food security incapacitated, the State is in a more vulnerable stage, an attempt for search of solutions by the government and other agency add more salt to the injury, simply because the attitude of the Government is uncoordinated, insincere and unprepared but mere propaganda of interest, unknowingly to many, Taraba state produced 35-40% of the food that feed the Nation, the flood disaster in Taraba State has a dual effects, one against the food security of the Nation and against lives and properties of the people. The paper examine the relationship that exist between the two important variables and the roles play as a means of nailing to board the ugly situation which have multiple effects, one on human lives, economic and the food security of the Nation. The paper make recommendations on how flood disaster can be management using more viable strategy to mitigate the effects of the damage caused by the flood and to avoid subsequent occurrences.

Examination of Flood Disaster and Government Attitudes Taraba State Experience

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