Cloud computing has gained significant traction due to its ability to provide scalable, on-demand resources, facilitating efficient data processing and management. A crucial component of cloud performance is task scheduling, which determines how tasks are allocated to remote servers for execution. This paper focuses on analyzing and implementing three widely used task scheduling algorithms: First-Come, First-Served (FCFS), Shortest Job First (SJF), and Round Robin (RR). The study aims to evaluate how these algorithms influence cloud system performance, particularly in terms of task execution efficiency, resource utilization, and system throughput. By simulating various cloud environments and workload scenarios, the paper assesses each algorithm's strengths and limitations. The analysis highlights how the selection of scheduling algorithms directly impacts cloud performance, emphasizing the need for optimized task allocation strategies to ensure better system resource management. The findings demonstrate that while each algorithm has its advantages in specific contexts, effective scheduling is crucial for maintaining overall system stability and maximizing resource utilization. The paper concludes with recommendations for selecting the most appropriate algorithm based on workload characteristics and desired performance outcomes, offering valuable insights into improving cloud computing efficiency.
In today's era of increasing reliance on mobile devices, chatbots play a vital role due to their simplicity and accessibility. The COVID-19 pandemic has further highlighted the insufficiency of healthcare resources, emphasizing the need for scalable digital solutions. This paper presents an application that leverages deep learning to assist with online disease diagnosis via a chatbot interface. The study focuses on predicting an individual’s susceptibility to heart attacks based on specific health indicators. Using a robust dataset, a deep learning model was developed to analyse key features and accurately assess the risk of cardiac events. The model was then integrated into a chatbot, allowing users to access personalized health insights in real-time. By combining advanced machine learning techniques with an intuitive conversational interface, the proposed system aims to enhance early detection and preventive care. The application is designed to reduce the burden on healthcare systems while empowering individuals with critical health information in a user-friendly format. This approach demonstrates the potential of integrating artificial intelligence with conversational platforms to address pressing health challenges effectively.
In today's era of increasing reliance on mobile devices, chatbots play a vital role due to their simplicity and accessibility. The COVID-19 pandemic has further highlighted the insufficiency of healthcare resources, emphasizing the need for scalable digital solutions. This paper presents an application that leverages deep learning to assist with online disease diagnosis via a chatbot interface. The study focuses on predicting an individual’s susceptibility to heart attacks based on specific health indicators. Using a robust dataset, a deep learning model was developed to analyse key features and accurately assess the risk of cardiac events. The model was then integrated into a chatbot, allowing users to access personalized health insights in real-time. By combining advanced machine learning techniques with an intuitive conversational interface, the proposed system aims to enhance early detection and preventive care. The application is designed to reduce the burden on healthcare systems while empowering individuals with critical health information in a user-friendly format. This approach demonstrates the potential of integrating artificial intelligence with conversational platforms to address pressing health challenges effectively.
Software effort estimation plays a critical role in project management, especially in the face of failure patterns and time constraints that complicate predictive accuracy. This study evaluates the application of machine intelligence techniques, specifically Hierarchical Temporal Memory (HTM) and Auditory Machine Intelligence (AMI), to improve estimation accuracy. Employing a quantitative methodology, the research investigates five core aspects: comparative effectiveness of HTM and AMI, the impact of failure patterns on estimation accuracy, the influence of time constraints on technique performance, complexity trade-offs, and optimization strategies for real-time applications. Findings reveal HTM's superior accuracy compared to AMI, though its complexity poses challenges for real-time usability. Failure patterns significantly influence estimation accuracy, emphasizing the need for adaptive models. Time constraints were found to affect the performance of both techniques, with HTM displaying greater sensitivity. Optimization techniques demonstrated the potential to mitigate complexity and enhance real-time applicability. This research contributes to the evolving field of software effort estimation by providing a nuanced understanding of the interplay between machine intelligence, failure patterns, and time dynamics. While limitations include reliance on historical datasets and restricted real-time validation, future research can address these gaps by exploring diverse datasets and advanced optimization techniques, offering practical implications for more accurate and efficient software project planning.
Enhancing the Quality of Service (QoS) and security of wireless communication systems necessitates a robust Radio Frequency Interference (RFI) detection mechanism, enabling effective mitigation strategies. This study presents a novel multi-class Multi-Layer Perceptron (MLP) neural network designed for real-time classification of jamming signals in digital video broadcasting based on DVB-S2 standards. The communication signal is assumed to coexist with one of three major interference types: Continuous Wave Interference (CWI), Multiple CWI (MCWI), or Chirp Interference (CI). The proposed algorithm effectively distinguishes interference types and identifies the Signal of Interest (SoI). Principal Component Analysis (PCA) is employed to optimize feature selection, improving classifier performance. Additionally, various learning methods—online, full-batch, and mini-batch—are analysed to determine the most effective approach for real-time applications. A key contribution of this study is the creation of a novel real-time jamming signal dataset, distinct from those used in similar research, allowing for a more comprehensive evaluation of classifier robustness. Performance assessments, conducted at varying Signal-to-Noise Ratios (SNR), demonstrate the classifier's effectiveness in recognizing both known and unknown interference signals. Comparisons with the Support Vector Machine (SVM) technique reveal superior classification and detection capabilities of the proposed MLP-based design. These findings highlight the potential of the MLP approach for real-time RFI detection and mitigation, providing a significant advancement in ensuring the reliability and security of modern wireless communication systems.