International Journal on Recent and Innovation Trends in Computing and Communication
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Cardiovascular Disorder Detection in Diabetes Mellitus Patients: An Integrated VGG and Bi-LSTM Model Optimized Using the ABC Algorithm
Full text linkThere is a major public health concern at the intersection of Diabetes Mellitus (DM) and Cardiovascular Diseases (CVDs). Patients with a diabetes diagnosis are more likely to experience a variety of cardiovascular problems. Better patient outcomes and lower healthcare costs can result from early diagnosis of these problems. This study presents a fresh computational model to tackle this problem. This research presents an integrated method that optimizes the VGG and Bidirectional Long Short Tem Memory (Bi LSTM) models together with the help of the Artificial Bee Colony (ABC) algorithm, which is based on the swarm intelligence of artificial bees. Cardiac images are processed using the VGG network, which has been shown to be highly effective in image classification, while the Bi LSTM is optimized for processing time series data from medical sensors, such as heart rates and blood sugar levels. The selected characteristics are then used in the proposed VGG 16 model before being sent to Bi-LSTM for further processing and abnormality detection. The VGG consists of 16 layers, all of which are blocks of 2D Convolution and Max Pooling layers. The ABC method was created as a result of research into intelligent behavior and is now widely used in areas such as problem solving, categorization, and optimization. The ABC algorithm is used to the unified model, which results in improved adaptability, speed of convergence, and robustness. To better forecast cardiovascular diseases, this research presents an Integrated VGG16 model with Bi-LSTM model with ABC optimization (VGG-Bi-LSTM-ABC) to predict the cardiovascular disorders. When compared to the standard model, the proposed model's ability to detect disorder is much better. Preliminary results from a carefully selected dataset of DM patients show that the integrated model outperforms state-of-the-art approaches in key measures, further demonstrating the promise of Artificial Intelligence (AI)-driven advances in medical diagnosis
Secure Data Transmission to Improve the Performance of Communication in Hybrid Systems
Full text linkLight Fidelity (Li-Fi) is a way of communication using LED’s with a high data rate and secures data transmission. But it has some drawbacks like data loss during shadowing and flickering of light, interference, etc. To deal with data loss we can use Hybrid Li-Fi and Wi-Fi Networks (HLWNets) by combining Li-Fi and Wireless Fidelity (Wi-Fi). Emerging HLWNets design and implementation face secure data transmission issues introduced due to Wi-Fi networks. We propose a novel comprehensive solution called Efficient Handover Protocol with Secure Data Transmission (EHPSDT).To assure the total security of data, we proposed security architecture based on Attributed-based Elliptic Curve Encryption (AECC) that ensures confidentiality and integrity. It also allows for fine-grained access control in HLWNets. Compared to other current methodologies, the proposed method minimizes overall processing overhead. The result of simulation revealed the performance of the proposed EHPSDT compared to underlying methods in terms of packet delivery ratio (PDR), average throughput, and communication overhead
Artificial Neural Networks and Optimization Technique: A theoretical study
Full text linkArtificial Neural Networks (ANNs) have become a pivotal tool in modern artificial intelligence (AI), significantly impacting various fields such as image processing, natural language processing, and autonomous systems. The training process of ANNs requires find-ing optimal parameters (weights and biases) that minimize a loss function, which can be computationally intensive and challenging. To achieve better performance, it is crucial to employ efficient optimization techniques that guide the network toward optimal solutions effectively. This paper provides an overview of ANNs, including their structure, types, applications, advantages, challenges, and future directions. This review also provides optimization techniques that are used to enhance their performance during training
AI Powered Document Automation in Mortgage Processing Scaling Classification and Extraction
Full text linkThe article reviews the application of machine learning based on the automation of the enterprise document systems to be applied to scale up mortgages services by classification and retrieval of mortgage information from documents with a processing volume of more than a million pages in a day. The system can implement a cloud-native and microservice architecture to deliver 98 percent accuracy in classification and more than 85 percent in field extraction with document integration that can support over 700 types of documents. Rather, it will cut the amount of time devotable to the analysis of manuals by 60 percent and the level of compliance preparation by 40 percent. Multimodal models are high performance learned pipelines that can easily be distributed (using Redis and Kafka), scalable, and economical. Companied with the findings, it can be proposed that the pace, precision and scale are greatly improved with the automation of the mortgage document proceedings using AIs
Maturity in IT Monitoring: Enhancing Enterprise Preparedness for Critical Incidents
Full text linkIn today's complex enterprise IT environments, the true measure of an organization's preparedness for critical incidents lies in the maturity of its IT monitoring capabilities. This maturity directly dictates how effectively IT teams can detect, navigate, and resolve incidents, ultimately minimizing downtime and business impact. High Mean Time To Detect (MTTD) and Mean Time To Resolve (MTTR) IT problems are directly linked to significant business losses, with IT downtime costing businesses over 1 million per hour, sometimes lasting for days [5, 6, 7].
This white paper delves into the dual pillars of IT monitoring maturity: proactive monitoring with actionable alerting and comprehensive visibility for deep investigation and root cause analysis. We will explore how the proliferation of alert noise can severely impede incident triage, leading to significant delays and extended MTTD. A mature monitoring practice emphasizes the generation of critical, high-fidelity alerts that truly matter. Beyond alerts, effective incident response hinges on holistic visibility across all IT layers—network, application, infrastructure, end-user, and logs—ensuring real-time data capture and historical storage for context to drastically reduce MTTR.
Through a detailed use case of high CPU utilization on a server, we will illustrate the rigorous process of problem qualification and the multi-faceted investigation required to uncover root causes. This involves correlating data from diverse dependencies, from network traffic and application transactions to server health metrics and logs. The paper argues that true problem resolution aims for long-term fixes, moving beyond superficial adjustments to address underlying issues and build enduring IT resilience. Achieving IT monitoring maturity is not just about tools, but about establishing processes and data-driven insights that empower IT teams to fix problems faster and more effectively than ever before
Towards a Unified Framework for Serverless Microservices in Cloud-Native Environments
Full text linkWith cloud-native computing gaining popularity, people are using microservices and serverless more, since both help create applications that can grow, be divided into parts, and remain steady. Nevertheless, these approaches also bring some limitations—microservices make managing systems hard while serverless functions have to cope with delays, no store of state, and invisible errors. Therefore, this paper suggests a common architecture that combines serverless and microservices under Kubernetes, Knative, and Istio in a cloud setting. Because of this approach, any workload can be smoothly designed, adjusted for any demand, and regularly monitored on any type of cloud environment. This study describes a system made up of Serverless-Microservices Tier, an Abstraction Layer for control, and a Cloud-Native Platform Layer for handling management and execution. It is confirmed in real-world settings, mainly in e-commerce, healthcare, and IoT, that it is flexible and can run smoothly. If hybrid deployment is compared to using only one of the models, the unified use of control planes offers more advantages. It also describes future developments, such as using AI for orchestration, including edge computing, and operating at the declarative level, so the proposed approach stands out as an up-to-date method for the next-gen of cloud-native systems
Nanostructured Alloys for High-Temperature Applications: A Study on Performance and Longevity
Full text linkNanostructured alloys have become a revolutionary material for high-temperature applications due to their exceptional mechanical properties, thermal stability, and resistance to environmental degradation. The present work focuses on the performance and durability of these materials with respect to extreme conditions in advanced engineering systems. Nano-oxides, for instance, Y2Ti2O7 pyrochlore have been found to play an essential role in optimizing creep resistance and tensile strength and also showing improved irradiation tolerance, thus finding applications in the fusion and fission reactors. Though significant amounts of progress in understanding the routes of composition processing to optimize properties have been accomplished, fabrication remains a difficult step in achieving reproducible performance because of defect introduction. This study also assesses the economic and practical feasibility of using nanostructured alloys in critical high-temperature environments, providing insight into their long-term reliability and potential for transformative industrial applications
Federated Learning a Collaborative Machine Learning Across Countries with Data Privacy
Full text linkWith growing importance of data in shaping policies, economic strategies, and healthcare systems, securing citizens data has become a critical issue for national governments. At the same time, the potential benefits of large-scale collaborative machine learning (ML) across countries are undeniable. Federated learning (FL) offers a unique solution to this dilemma by enabling the training of AI models across decentralized data sets without requiring data to be shared. This paper explores how different countries can use federated learning to contribute to collaborative machine learning while ensuring national data security. We examine the privacy-preserving mechanisms in FL, the technical challenges, and propose a framework for cross-country collaboration on a global scale.
Reinforcement Learning for Warehouse Management and Labor Optimization
No full textThe rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized warehouse management and labor optimization. Among the various AI methodologies, Reinforcement Learning (RL) has emerged as a powerful tool to address complex logistical challenges by enabling intelligent systems to learn and adapt dynamically. This paper explores the role of RL in warehouse management, emphasizing dynamic order picking, robotic sortation, labor management, and overall optimization. The research incorporates case studies from leading industry players, analyzing real-world applications of RL in improving operational efficiency, reducing costs, and enhancing labor productivity. Furthermore, this paper examines the challenges and future implications of RL adoption in warehouse settings, providing insights into how this technology can shape the future of logistics and supply chain management
Multi-Cloud Observability: Tools and Techniques for Monitoring and Troubleshooting Complex Hybrid Cloud Environments
Full text linkThis article focuses on detection tools and methods for hybrid cloud that are used to deal with complexity levels within multi-cloud infrastructures. It breaks down some of the best open-source and commercial observability solutions, like Prometheus, Grafana, Jaeger, Datadog, New Relic, Dynatrace, and Splunk, describing the offered functions, their advantages, and disadvantages. Some of the problems highlighted in the research include multi-cloud visibility and data consistency, integration difficulties, and inherent scalability. The real-life examples of TD Bank and Blinkit show how organizations can use and realize the values of observability solutions for better service dependability, quick reaction to incidents, and customer satisfaction. The paper then analyses some of the new trends, like the use of artificial intelligence in monitoring and enabling automated repairs and improvements to the network while at the same time trying to improve operational efficiency and looking at operation costs. Core problem-solving approaches for multi-cloud cases are articulated, which include diagnostics of the root cause, proper handling of the incident handling process, and the use of intelligent automation for problem-solving. Thus, the results highlight the need to implement extensive observability strategies for the effective management of distributed cloud systems. Future advancements are expected with cloud technologies; hence, organizations need to keep abreast of the latest concerning observability tools and approaches to ensure their multi-cloud environments remain high on performance and reliability