Abstract
Artificial Intelligence (AI) and Cloud Computing are two transformative technologies that reshape the digital landscape of modern organizations. Cloud computing provides scalable computing resources, storage capabilities, and on-demand services, while AI offers intelligent decision-making, automation, predictive analytics, and machine learning capabilities. The integration of AI into cloud environments has significantly improved operational efficiency, resource optimization, cybersecurity, service delivery, and business intelligence.
This study explores the role of Artificial Intelligence in enhancing cloud computing infrastructures and services. The research investigates the benefits of AI-enabled cloud systems, including automated resource management, predictive maintenance, intelligent workload balancing, cost optimization, and enhanced customer experiences. Furthermore, the study examines the challenges associated with integrating AI into cloud environments, such as data privacy concerns, computational complexity, interoperability issues, algorithmic bias, and regulatory compliance.
Security remains a critical concern in cloud ecosystems. Therefore, this paper analyzes major security issues including data breaches, insider threats, adversarial AI attacks, identity management vulnerabilities, and compliance risks. The study also evaluates AI-driven security mechanisms such as anomaly detection, threat intelligence, behavioral analytics, and automated incident response systems.
Through an extensive literature review and qualitative analysis of existing research, the paper demonstrates that AI significantly enhances cloud computing capabilities while introducing new security and ethical challenges. The findings suggest that organizations adopting AI-powered cloud solutions must implement robust governance frameworks, cybersecurity measures, and regulatory compliance strategies to maximize benefits and minimize risks.
Keywords: Artificial Intelligence, Cloud Computing, Machine Learning, Cybersecurity, Resource Optimization, Cloud Security, Big Data Analytics, Intelligent Automation, Predictive Analytics, AI-driven Cloud Services.
Kurban Kurban, A. Farooqi· International Scientific Jou...· 0 citations
Abstract
The Internet of Things (IoT) has emerged as one of the most significant technological advancements in recent years, enabling seamless communication and interaction among physical devices through the Internet. IoT technology has transformed traditional monitoring and control systems by providing real-time data collection, remote accessibility, intelligent decision-making, and automated control mechanisms. The increasing demand for smart environments in sectors such as smart homes, healthcare, agriculture, industrial automation, and smart cities has accelerated the adoption of IoT-based solutions. This dissertation presents the design and implementation of an IoT-based smart system for real-time monitoring and automation using low-cost hardware components, cloud computing platforms, and mobile applications.
The primary objective of this research is to develop an efficient, reliable, and scalable IoT-based monitoring system capable of collecting environmental data, transmitting information to cloud servers, and enabling remote monitoring and automated control. The proposed system integrates NodeMCU ESP8266 as the central processing unit with sensors such as the DHT11 temperature and humidity sensor and the PIR motion sensor. These sensors continuously monitor environmental conditions and transmit the collected data to cloud platforms through wireless communication. ThingSpeak is used as the cloud platform for data storage, visualization, and analysis, while the Blynk mobile application provides users with real-time monitoring and remote control capabilities.
The development process involved hardware configuration, software programming, cloud integration, and system testing. The NodeMCU microcontroller was programmed using Arduino IDE to acquire sensor readings, establish wireless connectivity, and communicate with cloud services. The collected data was uploaded to the cloud platform and displayed through graphical dashboards for real-time observation. Furthermore, automation functionalities were incorporated through relay modules that enable automatic control of connected devices based on predefined threshold values and environmental conditions.
Experimental investigations were conducted to evaluate the performance and effectiveness of the proposed system. The results demonstrated successful real-time monitoring of temperature, humidity, and motion detection parameters. Sensor data was accurately transmitted to the cloud platform and displayed on the mobile application with minimal delay. The automation features successfully triggered control actions whenever predefined conditions were satisfied. The system also demonstrated reliable communication between sensors, cloud servers, and end users, thereby validating the feasibility of the proposed approach.
A comparative analysis between traditional monitoring systems and the proposed IoT-based solution revealed significant improvements in terms of automation, accessibility, data management, operational efficiency, and remote monitoring capabilities. The implementation confirmed that IoT technology can substantially reduce manual intervention, improve response time, and enhance system effectiveness. Despite challenges such as network dependency, security concerns, and sensor limitations, the developed system proved to be a cost-effective and practical solution for intelligent monitoring and automation applications.
Zarreen Fatima, A. Farooqi· International Scientific Jou...· 0 citations
This research paper presents a comprehensive study of an AI-based fake news detection system leveraging Natural Language Processing techniques and multiple machine learning algorithms to automatically classify news articles as real or fake.
Shahid Khan, A. Farooqi· International Scientific Jou...· 0 citations
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