International Journal of Communication Networks and Information Security (IJCNIS)
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1021 research outputs found
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Cloud Security Posture Management: Tools and Techniques for Compliance and Risk Management
No full textCloud Security Posture Management (CSPM) is crucial for organizations aiming to ensure the security and compliance of their cloud environments. This paper explores advanced tools and techniques for managing cloud security posture, focusing on compliance and risk management. CSPM tools provide continuous monitoring, automated compliance checks, and vulnerability management to address the dynamic nature of cloud environments. Techniques discussed include the implementation of automated security policies, real-time threat detection, and risk assessment frameworks. By leveraging these tools and techniques, organizations can enhance their ability to proactively manage security risks, ensure regulatory compliance, and protect critical assets in the cloud. This paper also highlights best practices for integrating CSPM into broader security strategies and provides case studies illustrating successful deployments
A Hybrid Multi-Criteria Approach for Evaluation and Selection of Suppliers
No full textThis paper proposes a Hybrid Multi-Criteria Approach for Evaluation and Selection of Suppliersimplemented in two steps. At the first stage, qualitative performance evaluation is performed by using FAHP (Fuzzy Analytical Hierarchy Process) in order to find standard weights for criterion, then suppliers are ranked using TOPSIS at the second step. Supplier evaluation is a multi-criteria decision problem involving many quantitative and qualitative factors, as multiple criteria are often considered when evaluating supply sources. All sources of supply focus on their performance, such as price, quality, delivery, and service, which are the main factors that all companies use to evaluate it. Over the previous decade, many companies and researchers have been studying the problem of supplier evaluation in order to develop decision models that can effectively solve it. In this study, a new approach is introduced and proposed to improve the quality of supplier selection and evaluation. The new method considers both qualitative and quantitative variables when evaluating the performance of supplier selection
Influence of Artificial Intelligence on Customer Relationship Management (CRM)
No full textBackground: Customer Relationship Management has revolutionized business administration using Artificial Intelligence. However, it is critical to note that the degree of AI's engagement with CRM in various economic arenas and how it affects customer-related procedures remains unexplained.
Objective: The purpose of this study was to assess the effect of AI on Texas-based e-commerce enterprises' CRM performance, focusing on the positive aspects of AI, main challenges, and customer satisfaction levels as a secondary factor.
Method: Data was derived through a sample of 112 enterprises in Texas from January 2022 to December 2023. Moreover, the current research used a mixed-method approach, combining quantitative surveys and qualitative semi-structured interviews to receive a more complete description. At this stage, statistical methods were applied to process the data collected and the impact of particular AI applications on CRM metrics.
ResultsT he findings revealed that 87 out of 112 (77.7%) of the surveyed enterprises reported significant improvements in customer satisfaction scores post-AI integration. Additionally, 73 out of 112 (65.2%) businesses experienced enhanced efficiency in customer service operations due to AI-powered automation. Personalized marketing efforts saw a 56 out of 112 (50%) increase in engagement rates, attributed to AI's data analysis capabilities. However, 45 out of 112 (40.2%) of the respondents highlighted concerns regarding data privacy and the high costs associated with AI implementation.
ConclusionThe study shows that CRM performance in e-commerce companies has increased as a result of AI's engagement. The advantages of AI integration such as enhanced customer satisfaction, improved efficiency, and PR activities. The data points to concerns such as privacy and fees associated with AI implementation, but the pros outweigh the cons and result in a competitive edge for firms
Enhancing Stock Price Prediction: Improvising in KNN
No full textStock price prediction is very crucial for informed investment decisions, involving forecasting futurestock values which are based on various factors. K-nearest neighbors (KNN) is a machine learningalgorithm that can assist in predicting stock prices by identifying patterns and similarities betweenthe target stock and its neighboring data points in a multidimensional feature space. However,traditional KNN algorithms encounter challenges like sensitivity to irrelevant features and outliers,potentially compromising predictive accuracy. To address this, integrating Density-Based SpatialClustering of Applications with Noise (DBSCAN) before KNN proves effective. DBSCAN identifiesand filters out noisy data points and outliers, refining the dataset for subsequent KNN analysis. Thisintegration not only mitigates traditional KNN issues but also uncovers underlying data structures,improving overall predictive power in stock market analysis
Unveiling the Impact of Pro-Environmental Behavior on Corporate Environmental Performance
No full textThis study investigates the relationship between pro-environmental behavior and corporate environmental performance, focusing on data collected from 387 employees across ten highly polluting manufacturing industries in Jammu and Kashmir. The analysis was conducted using SPSS 23.0 and AMOS 20.0, with hypothesis tested through structural equation modeling (SEM). The results strongly support the hypothesis, revealing a significant and positive impact of pro-environmental behavior on corporate environmental performance. These findings underscore the critical role of employee-driven environmental actions in enhancing organizational environmental outcomes. The study highlights the need for organizations to foster and promote pro-environmental behavior within their workforce as a strategy to improve environmental performance and address stakeholder pressures effectively. Additionally, the study provides directions for future research, suggesting avenues for further exploration to validate and extend these findings in different contexts
Strategies for Scaling EdTech Startups in Emerging Markets
No full textThis research paper examines the strategies for scaling educational technology (EdTech) startups in emerging markets. The study analyzes the current landscape of EdTech in these regions, exploring fundamental scaling strategies, technology infrastructure considerations, localization approaches, partnership opportunities, funding mechanisms, talent management, regulatory challenges, marketing strategies, operational scalability, and impact measurement. The paper aims to provide a comprehensive framework for EdTech entrepreneurs and stakeholders to navigate the complex ecosystem of emerging markets and achieve sustainable growth
Isolation and Characterization of a High Thermal Resistance Bacteriophage vB-KPP01 Infecting Antibiotics Resistant Clinical Klebsiella pneumoniae (PP464225) Isolated from Egypt
No full textBackground: Antibiotics-resistant Klebsiella pneumoniae haspersistently developed greater resistance to groups of antibioticsas ?-lactums, Sulfonamides, carbapenem, Aminoglycosides andFluoroquinolones. Nonetheless, bacteriophages are beinginvestigated as a potential substitute for antibiotics in thetreatment of bacterial infections due to host specificity, no seriesside effects, without destroying normal flora of patient. In thisstudy, Bacteriophage vB-KPP01, isolated from local sewage ofTalkha, Egypt, was tested in-vitro to evaluate its lytic activityagainst antibiotics resistant K. pneumoniae isolated from bloodof patient with pneumonia at the Sandoub Health InsuranceHospital (SHIH). Methods: Thebacteriophage vB-KPP01 was assessed for its morphologicalcharacterization, phage adsorption, growth kinetics, in-vitro hostrange, temperature, dilution end point and pH sensitivity. InvitroLytic activity of phage vB-KPP01 was determined against K.pneumoniae. Results: bacteriophage vB-KPP01 produced aclear plaque with a halo (0.6 to 1.1cm) and had an icosahedralhead (127 nm) with short non-contractile tail (30.1 nm) wasclassified as podoviridae. The phage was tested against variousclinical strains and results proved it to be host specific and had aburst size of 490 PFU/cell. It was stable over a wide pH range of4–11.4 with maximum activity at pH 8.1 and had relatively strongheat stability up to 90°C. Phage vB-KPP01 demonstratedsignificant in-vitro lytic activity against K. pneumoniae, resultingin a maximum decrease in K. pneumoniae counts with 78.3%after 9.5 h of incubation. Conclusion: These attributes suggestthat phage vB-KPP01 could hold therapeutic promise for thetreatment of K. pneumoniae infections
IoT Map: A Comprehensive Framework for IoT Data Management and Analysis
No full textThe Internet of Things (IoT) revolutionizes numerous industries by enabling real-time data collection and analysis from a multitude of connected devices. However, the vast amount of data generated by IoT devices presents significant challenges in data management, security, and real-time processing, particularly regarding data storage, real-time processing, security, and predictive modeling. This paper introduces IoTMap, a comprehensive framework designed to streamline IoT data workflows by integrating database management, live sniffing, exploitation testing, and modeling capabilities into a single platform. IoTMap addresses the inefficiencies and fragmentation seen in existing solutions by offering an all-in-one approach that enhances data handling, improves security, and provides real-time insights and predictive analytics. Through extensive testing with a variety of IoT devices, the framework demonstrated high efficiency in data management, effective real-time traffic analysis, robust security assessments, and accurate modeling of IoT environments. IoTMap’s unified and scalable approach positions it as a valuable tool for researchers, developers, and practitioners, facilitating more efficient and effective IoT data management and analysis while paving the way for advanced IoT applications and research
AI-based Organic Farming as an Instrument for Environmental Protection
No full textThe combination of AI and traditional farming methods shows potential for tackling environmental issues in agriculture. This research paper examines how AI-driven organic agriculture can serve as a powerful tool for safeguarding the environment. Through analyzing recent literature, case studies, and upcoming technologies, we explore the potential of AI in enhancing organic farming methods to lessen environmental effects, enhance resource utilization, and support biodiversity. The research investigates different AI uses in organic farming, such as precision agriculture, pest control, soil health tracking, and predicting crop yields. Our results indicate that incorporating AI into organic agriculture can make a substantial impact on promoting sustainable farming practices and preserving the environment. Yet, issues like data privacy, technological accessibility, and the necessity for farmer training need to be resolved in order to fully exploit the advantages of this method
PREDICTION OF SENTIMENT OF PEOPLE FOR CASTING THEIR VOTE USING MACHINE LEARNING
No full textPolitical discussion - in its substance - conveys a vigorous profound charge, and online entertainment has turned into an immense field for electors to disperse and examine the thoughts proposed by up-and-comers. The Brazilian official appointment of 2018 were set apart by an elevated degree of polarization, making the conversation of the competitors' thoughts a philosophical war zone, brimming with allegations and verbal hostility, making a brilliant hotspot for opinion investigation. In this paper, we break down the feelings of the tweets posted about the official applicants of Brazil on Twitter, with the goal that it was feasible to recognize the close to home profile of the disciples of every one of the main up-and-comers, and hence to observe which feelings had the most grounded impacts upon the political race results. Likewise, we made a model utilizing opinion examination and AI, which anticipated with a relationship of 0.90 the end-product of the political race