INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY

Harnessing the Power of AI in Data Warehousing Security: A Cloud Computing Approach

2024-04-02T06:44:32+02:00

This research explores the integration of Artificial Intelligence (AI) into the realm of data warehousing security, leveraging a comprehensive Cloud Computing approach. The study aims to enhance the robustness and efficiency of data protection mechanisms by employing AI algorithms within cloud-based infrastructures. By analyzing patterns, anomalies, and potential threats in real-time, this approach seeks to proactively safeguard sensitive information stored in data warehouses. The synergistic combination of AI and Cloud Computing not only fortifies security measures but also optimizes resource utilization, ensuring a dynamic and adaptive defense against evolving cyber threats.

Next-Gen Electric Vehicles: Advancements in Battery Materials and Antenna Technologies

2024-04-02T06:45:42+02:00

This research explores the cutting-edge advancements in next-generation electric vehicles (EVs), focusing on two key aspects: battery materials and antenna technologies. As the automotive industry undergoes a transformative shift towards electrification, understanding the innovations in these areas is crucial for achieving enhanced performance, range, and connectivity in EVs. The study investigates the latest developments in battery materials, including novel chemistries and materials engineering, as well as advancements in antenna technologies to support communication, connectivity, and intelligent vehicular systems. By synthesizing information from diverse sources, this research provides insights into the evolving landscape of EV technologies, offering a glimpse into the future of sustainable and connected transportation.

Implementing Artificial Intelligence in Real-time Cyber Threat Hunting and Response Mechanisms

2024-05-27T03:42:53+02:00

In today's ever-evolving cybersecurity landscape, the proactive identification and mitigation of cyber threats have become imperative to safeguarding sensitive data and critical infrastructure. This paper explores the integration of Artificial Intelligence (AI) techniques into real-time cyber threat hunting and response mechanisms to enhance the capabilities of security operations teams. By leveraging AI algorithms, such as machine learning, natural language processing, and anomaly detection, organizations can augment their threat detection capabilities, automate incident response workflows, and adapt to rapidly changing threat landscapes.

The implementation of AI in real-time cyber threat hunting enables security analysts to sift through vast volumes of data generated by network logs, endpoint devices, and security sensors to identify indicators of compromise (IoCs) and potential security breaches. Through the application of machine learning models, anomalies in network behavior and suspicious patterns indicative of cyber threats can be detected in near real-time, enabling swift and targeted responses to mitigate risks and minimize impact.

Furthermore, the integration of natural language processing (NLP) techniques facilitates the analysis of unstructured data sources, such as security advisories, threat intelligence reports, and social media feeds, to extract actionable insights and contextual information relevant to emerging threats. By automatically processing and correlating disparate sources of threat intelligence, security operations teams can prioritize alerts, identify emerging attack vectors, and orchestrate timely responses to mitigate potential risks.

In addition to threat detection, AI-driven automation plays a crucial role in streamlining incident response workflows and reducing response times. By employing automated playbooks and decision-making algorithms, security teams can orchestrate responses to detected threats, such as quarantining compromised assets, blocking malicious IP addresses, and applying security patches, without manual intervention. This enables organizations to respond to cyber threats with greater agility and efficiency, thereby minimizing the dwell time of attackers and mitigating the potential impact of security incidents. Overall, the integration of Artificial Intelligence in real-time cyber threat hunting and response mechanisms holds immense promise for enhancing the effectiveness and efficiency of cybersecurity operations. By harnessing the power of AI algorithms to augment human capabilities, organizations can proactively identify and respond to cyber threats in a dynamic and evolving threat landscape, thereby strengthening their resilience against cyber-attacks and safeguarding their digital assets.

Data Warehousing Solutions for E-commerce: Comparing Traditional and Cloud-based Options

2024-05-26T16:22:55+02:00

Data warehousing solutions play a crucial role in empowering e-commerce businesses to manage and analyze vast volumes of data for informed decision-making. With the advent of cloud computing, organizations now have the option to deploy their data warehousing infrastructure either on-premises or in the cloud. This paper compares traditional on-premises data warehousing solutions with cloud-based alternatives in the context of e-commerce, examining factors such as scalability, cost-effectiveness, flexibility, and security. Through a comprehensive analysis of the strengths and limitations of each approach, this paper aims to provide insights into the selection and implementation of data warehousing solutions tailored to the unique requirements of e-commerce businesses.

Leveraging Generative Artificial Intelligence for Financial Market Trading Data Management and Prediction

2024-06-23T07:57:19+02:00

Abstract: The paper explores using generative artificial intelligence (AI) in financial market data management and forecasting. By integrating multiple data sources and feature extraction techniques, such as fundamental analysis, technical indicators, global economic data, and sentiment analysis, generative AI constructs a comprehensive deep learning framework that significantly enhances financial data management efficiency and market forecasts' accuracy. Specifically, technologies like generative adversarial networks (Gans) and variational autoencoders (VAE) demonstrate substantial data augmentation and model optimisation potential. The application value of the model in real-time market prediction and trading strategy optimization is further amplified through reinforcement learning methods.

2024-08-02T10:06:40+02:00

The smart city term defined as smart environment in which the things are smart like smart security, smart healthcare, smart governance etc. Smart things are somehow smart using the IOT technology smart city facing the issues of decision making and real time prediction. In this paper the smart cities component is discuss that smart cities components are less intelligent but not autonomous in decision making. There still lack in autonomous decision making of a component for real time environment. then the autonomous system of components said to be smart in term of smart leads towards the smart city using artificial intelligence.

Mitigating Cyber Security Risks in IoT: A Focus on Reliability and Efficiency

2024-08-06T16:29:00+02:00

Cybersecurity is the practice of defending computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks. Various problems such as authentication, authorization, and privacy have been highlighted in the context of cybersecurity. Previous research has addressed and mitigated these threats to a considerable extent, but the aspect of reliability in cybersecurity remains underexplored. This paper proposes a qualitative method to emphasize data security reliability and productivity on various aspects and parameters that are crucial in cybersecurity. Although these reliability measures are still in development, they aim to reduce the number of threats affecting cybersecurity performance.

Mathematical Approaches for Cost Optimization in Cybersecurity: A Strategic Framework

2025-02-04T18:14:31+01:00

In an era dominated by digital innovation, cybersecurity remains a pivotal challenge for organizations striving to safeguard sensitive data and maintain operational continuity. Cost optimization in cybersecurity is crucial, as resource constraints often hinder the deployment of comprehensive protective measures. This study presents a strategic framework leveraging mathematical approaches for cost optimization in cybersecurity. The framework integrates game theory, linear programming, and machine learning algorithms to balance resource allocation with risk mitigation. By employing game theory, the interaction between cyber attackers and defenders is modeled to predict potential attack vectors and design robust countermeasures. Linear programming is utilized to optimize budget allocation across various cybersecurity components, ensuring maximum risk reduction within financial constraints. Additionally, machine learning algorithms are incorporated to enhance threat detection and adapt security measures dynamically based on evolving threats. This holistic framework addresses the complexities of cybersecurity investment, providing actionable insights for decision-makers. It underscores the importance of proactive strategies that align with organizational goals and evolving threat landscapes. The findings demonstrate the potential of mathematical models to improve the efficacy of cybersecurity strategies while minimizing costs. Future research directions include exploring real-time optimization models and integrating artificial intelligence for predictive risk management. This study contributes to the growing field of cybersecurity economics, offering a practical roadmap for organizations to fortify their defenses efficiently.

Mathematical Innovations Driving Artificial Intelligence and Machine Learning

2025-02-04T18:37:33+01:00

Mathematical innovations are at the core of advancements in Artificial Intelligence (AI) and Machine Learning (ML). These technologies have witnessed exponential growth due to breakthroughs in various mathematical fields, which enhance their capabilities to process data, recognize patterns, and make predictions. At the heart of these innovations are optimization techniques, linear algebra, probability theory, statistics, and graph theory, all of which contribute to improving AI models. Optimization methods, such as gradient descent and stochastic optimization, are fundamental for training machine learning algorithms, ensuring they converge to optimal solutions. Linear algebra plays a vital role in managing and transforming large datasets, particularly in deep learning networks where matrix operations are crucial. Furthermore, probability theory and statistics provide the necessary frameworks for reasoning under uncertainty, a central component in ML for model evaluation, decision making, and risk assessment. Graph theory facilitates the representation of data relationships, which is particularly beneficial in network analysis, recommendation systems, and knowledge graphs. The fusion of these mathematical concepts allows for the creation of robust, scalable, and adaptable AI systems. As these mathematical foundations continue to evolve, so too will the complexity and efficiency of AI and ML algorithms. This paper explores how mathematical innovations have directly impacted AI and ML and provides an outlook on emerging mathematical techniques that promise to further propel these fields into new frontiers.

"The Role of Mathematics in Advancing Modern Technology: Applications and Innovations"

2025-02-04T18:23:44+01:00

Mathematics serves as the foundation of modern technological advancements, playing a crucial role in shaping artificial intelligence, machine learning, cryptography, quantum computing, and data science. Mathematical models and algorithms enable the optimization of complex systems, enhancing computational efficiency and problem-solving capabilities. In artificial intelligence and machine learning, linear algebra, probability theory, and calculus underpin neural networks and predictive models, driving automation and decision-making in diverse sectors, including healthcare, finance, and engineering. Cryptographic techniques, based on number theory and modular arithmetic, ensure secure communication in digital transactions and cybersecurity frameworks. Furthermore, quantum computing leverages advanced mathematical principles such as tensor analysis and quantum probability to revolutionize computational speed and encryption methods. In the domain of data science and big data analytics, statistics and optimization algorithms facilitate data-driven decision-making, transforming industries by improving accuracy and efficiency. Mathematical theories also contribute to advancements in robotics, aerodynamics, and engineering design, ensuring precision and innovation in modern technology. As mathematical research continues to evolve, its integration with emerging technologies is expected to drive groundbreaking innovations, reinforcing its role as the backbone of scientific and technological progress. This paper explores the applications of mathematical principles in contemporary technological advancements, emphasizing their impact on shaping the digital era.

Computational Approaches to Market Risk Prediction: A Comparison of Hidden Markov and Markov Models in the Pakistan Stock Exchange

2025-02-04T18:07:04+01:00

Market risk prediction is a critical aspect of financial decision-making, particularly in volatile stock markets such as the Pakistan Stock Exchange (PSX). This study examines the effectiveness of computational approaches in market risk prediction by comparing Hidden Markov Models (HMMs) and traditional Markov Models (MMs). While Markov Models rely on observable states and assume a memoryless stochastic process, HMMs incorporate latent variables, allowing for a more nuanced representation of market dynamics. The study employs historical PSX data, integrating price trends, trading volumes, and volatility indicators to assess the predictive accuracy of both models. Our findings suggest that HMMs outperform traditional Markov Models in capturing complex market behaviors, particularly during periods of economic uncertainty and sudden market shifts. The study highlights the superior ability of HMMs to model latent structures in financial time series, enabling investors and analysts to anticipate market risks more effectively. Furthermore, our research explores the implications of computational approaches in financial risk management, emphasizing the role of machine learning in refining stock market predictions. The comparative analysis contributes to the growing body of literature on quantitative finance and market risk assessment, offering insights into the potential adoption of HMMs in algorithmic trading and portfolio optimization. The findings of this study are relevant for policymakers, investors, and financial analysts seeking robust predictive models for market risk assessment in emerging economies. Future research directions include the integration of deep learning techniques with HMMs to further enhance predictive accuracy.