Smart Grid (SG) can be portrayed as a high level electric power network structure for improved adequacy, resolute quality and security, with smooth joining of sustainable and non-inexhaustible sources through automated control and flow correspondences progresses.
Machine-to-machine (M2M) communication is a vital empowering innovation for the future. Industrial Internet of Things (IoT) applications. It assumes a significant part in the availability and combination of mechanized machines, like sensors, actuators, regulators, and robots.
In this work, an original scaled down round labyrinth molded implantable recieving wire is presented for clinical field to be worked in clinical band. The biocompatible polyamide substrate (r = 4.3 and tan = 0.004) with 0.05 mm thickness has been utilized as both substrate and superstrate.
Remote sensor organization (WSN) has been a subject of expansive assessment attempts in the new year’s, and has been particularly seen as a widespread and general strategy for a couple emerging applications, for instance, a continuous traffic noticing, natural framework and battle zone observation.
In picture legal sciences, recognition of picture imitations including non-direct controls have gotten a lot of interest in late past. Middle separating (MF) is one such non-straight control procedure which is regularly utilized in number of uses, for example, to conceal drive commotions.
This examination investigates the joining of protection safeguarding homomorphic encryption plans in cloud-based AI to address blossoming concerns regarding information security and protection. This proposed method is based on recent contributions and focuses on tailoring homomorphic encryption algorithms like Paillier and Fully Homomorphic Encryption (FHE) to specific machine learning tasks. To strike a balance between data utility and privacy, seamless compatibility with preprocessing pipelines is prioritized.
Detecting anomalies is turning to be one of the focal areas in cyber defence system in the presence of numerous types of cyber threats. The research looks at multi-layer application of machine learning regimes in cyber security applications and particularly focuses on the anomaly detection which enables the computer to develop and respond to new threats, provide predictive and monitoring services.
Hyperparameter tuning is critical to machine learning model development, significantly impacting model performance. However, the process can be time-consuming and resource-intensive, especially when dealing with complex deep-learning models and large datasets. This paper explores proactive scaling strategies for efficient and cost-effective hyperparameter configuration in machine learning models using cloud infrastructure.
Kubernetes has emerged as a powerful platform for orchestrating containerized applications, but with its growing adoption, security concerns have become increasingly paramount. This paper explores advanced security mechanisms within Kubernetes, focusing on isolation and access control strategies designed to enhance the security posture of Kubernetes environments. Isolation techniques such as namespaces, network policies, and node isolation are critical in preventing unauthorized access and minimizing the attack surface.
Maximizing investment returns and the entire project benefits through strategic project prioritization is paramount in programs aimed at enhancing the sustainability of building infrastructures. This necessity is particularly evident when implementing a revolving-fund approach, leveraging savings from initial projects for subsequent improvements. The success of such an approach depends on the meticulous prioritization of projects.
This paper seeks to understand how to use Security Orchestration, Automation, and Response (SOAR) solutions in achieving and sustaining PCI Compliance, emphasizing the incident response for regulatory security. Based on the principles of the SOAR framework, improvements are made regarding the speed and accuracy of the incident response procedures, which are essential for compliance with the PCI DSS.
The rapid advancement of artificial intelligence systems has brought about many possibilities and issues in multiple fields. Indeed, recent advances in AI algorithms have already provided capabilities on data analysis and making decision with incomparable efficiency; therefore, reliability and credibility of available data remain as priority concern. Current architecture of a centralized data warehousing framework makes it open to fraud, has vulnerability of single point failure, and the process is nontransparent in nature.
The rapid advancement of wireless communication technologies necessitates innovative solutions to meet the growing demand for high data rates, reliability, and efficiency. This paper presents a novel AI-enhanced Multiple Input Multiple Output (MIMO) communication system that leverages advanced machine learning techniques to optimize beam forming, channel estimation, and resource allocation.
Geothermal HVAC systems provide the best solution to conventional HVAC systems since they are economical, environmentally friendly, and long-term cost-efficient. The following paper presents an analysis of the cost and benefits of geothermal HVAC systems in residential areas with respect to costs and performance. Typically, it looks at the purchase prices, the running costs, costs of repairs and maintenance and any environmental effects.
This research paper explores strategies and solutions for optimizing scalability and performance in cloud services. It examines various aspects of cloud architecture, scalability techniques, performance optimization strategies, and advanced technologies. The study delves into vertical and horizontal scaling, auto-scaling techniques, load balancing, caching mechanisms, and database optimization. Additionally, it investigates the role of containerization, serverless computing, and edge computing in enhancing cloud performance.
There has been a tremendous shift in the fields of Artificial Intelligence (AI) and Machine Learning (ML) due to the fast development of big data analytics. Today, and particularly in recent years, we are witnessing the increase in volumes of data originating from social networks, IoT devices, and enterprise systems which have offered the chance to develop more complex and precise AI and ML models. This paper aims to discuss the development of AI and the use of ML, which occurred due to the availability of immense datasets. It also looks at how data availability has helped these novelties gain more accuracy, efficiency, and versatility.
The implementation of AI methods into DevOps pipelines within bioinformatics has several effects: Initially, it liberates researchers’ time as it provides for automation of simple and repetitive procedures like data pre-processing and feature extraction. Secondly deployment of AI models can be done at scale as the data can be processed in parallel and distributed fashion. This helps to enhance the rate of processing large amounts of information and the efficiency of functioning. In addition, the integration of AI into the models has been proven to increase the high predictive and classifying effectiveness of bioinformatics results gained from specimens.
The payment industry has evolved a lot in the tech aspect. Free-For-All features a CI/CD culture because of cloud-computing integration intended to improve the CI/CD pipeline for payment gateways. Two cloud platforms, Azure and AWS, provide rich CI/CD services that include numerous automation tools, which enable payment gateways to provide high availability, security, and scalability. This paper considers the Azure and AWS CI/CD solutions for the automated deployment of payment gateways. One of the issues that contribute to the decision is the integration of tools, security, deployment options, and cost.
Fuzzy Based Routing Protocol For Smart Grid NetworkR. Karpaga Priya, Venkatanarayanan.S
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Smart Grid (SG) can be portrayed as a high level electric power network structure for improved adequacy, resolute quality and security, with smooth joining of sustainable and non-inexhaustible sources through automated control and flow correspondences progresses.
A Data-Oriented M2m Messaging Mechanism for IndustrialS. Morrey Christain, Robert Johanson .V.D
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Machine-to-machine (M2M) communication is a vital empowering innovation for the future. Industrial Internet of Things (IoT) applications. It assumes a significant part in the availability and combination of mechanized machines, like sensors, actuators, regulators, and robots.
Design of implantable Flower Maze Structured AntennaGayathri.C, Venkatanarayanan.S
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In this work, an original scaled down round labyrinth molded implantable recieving wire is presented for clinical field to be worked in clinical band. The biocompatible polyamide substrate (r = 4.3 and tan = 0.004) with 0.05 mm thickness has been utilized as both substrate and superstrate.
Secured Low Power Wireless Sensor Network By Using Lion Optimization AlgorithmMuhammadu Ansari, Sadamiro A. O
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Remote sensor organization (WSN) has been a subject of expansive assessment attempts in the new year’s, and has been particularly seen as a widespread and general strategy for a couple emerging applications, for instance, a continuous traffic noticing, natural framework and battle zone observation.
Error Detection Technique for a Median FilterG. Barkavi, G. B. Shanawaz
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In picture legal sciences, recognition of picture imitations including non-direct controls have gotten a lot of interest in late past. Middle separating (MF) is one such non-straight control procedure which is regularly utilized in number of uses, for example, to conceal drive commotions.
Privacy-Preserving Homomorphic Encryption Schemes for Machine Learning in the CloudRanadeep Palle, Dr. A. Punitha
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This examination investigates the joining of protection safeguarding homomorphic encryption plans in cloud-based AI to address blossoming concerns regarding information security and protection. This proposed method is based on recent contributions and focuses on tailoring homomorphic encryption algorithms like Paillier and Fully Homomorphic Encryption (FHE) to specific machine learning tasks. To strike a balance between data utility and privacy, seamless compatibility with preprocessing pipelines is prioritized.
Anomaly Detection in Cybersecurity: Leveraging Machine Learning AlgorithmsAshok Choppadandi, Jagbir Kaur, Pradeep Kumar
Chenchala, Akshay Agarwal, Varun Nakra, Pandi Kirupa Gopalakrishna Pandian
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Detecting anomalies is turning to be one of the focal areas in cyber defence system in the presence of numerous types of cyber threats. The research looks at multi-layer application of machine learning regimes in cyber security applications and particularly focuses on the anomaly detection which enables the computer to develop and respond to new threats, provide predictive and monitoring services.
Proactive Scaling Strategies for Cost-Efficient Hyperparameter Optimization in Cloud-Based Machine Learning Models: A
Comprehensive ReviewMadan Mohan Tito Ayyalasomayajula, Sailaja Ayyalasomayajula
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Hyperparameter tuning is critical to machine learning model development, significantly impacting model performance. However, the process can be time-consuming and resource-intensive, especially when dealing with complex deep-learning models and large datasets. This paper explores proactive scaling strategies for efficient and cost-effective hyperparameter configuration in machine learning models using cloud infrastructure.
Advanced Security Mechanisms in Kubernetes: Isolation and Access Control StrategiesAnirudh Mustyala, Sumanth Tatineni
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Kubernetes has emerged as a powerful platform for orchestrating containerized applications, but with its growing adoption, security concerns have become increasingly paramount. This paper explores advanced security mechanisms within Kubernetes, focusing on isolation and access control strategies designed to enhance the security posture of Kubernetes environments. Isolation techniques such as namespaces, network policies, and node isolation are critical in preventing unauthorized access and minimizing the attack surface.
Evaluating Planning Strategies for Prioritizing the most viable Projects to Maximize Investment Returns Amit Mangal
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Maximizing investment returns and the entire project benefits through strategic project prioritization is paramount in programs aimed at enhancing the sustainability of building infrastructures. This necessity is particularly evident when implementing a revolving-fund approach, leveraging savings from initial projects for subsequent improvements. The success of such an approach depends on the meticulous prioritization of projects.
SOAR Solutions in PCI Compliance: Orchestrating Incident Response for Regulatory Security Naresh Kilaru, Sai Krishna
Manohar Cheemakurthi, Vinodh Gunnam
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This paper seeks to understand how to use Security Orchestration, Automation, and Response (SOAR) solutions in achieving and sustaining PCI Compliance, emphasizing the incident response for regulatory security. Based on the principles of the SOAR framework, improvements are made regarding the speed and accuracy of the incident response procedures, which are essential for compliance with the PCI DSS.
Block Chain-enabled Data Analytics for Ensuring Data Integrity and Trust in AI Systems Lohith Paripati, Nitin Prasad, Jigar Shah,
Narendra Narukulla, Venudhar Rao Hajari
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The rapid advancement of artificial intelligence systems has brought about many possibilities and issues in multiple fields. Indeed, recent advances in AI algorithms have already provided capabilities on data analysis and making decision with incomparable efficiency; therefore, reliability and credibility of available data remain as priority concern. Current architecture of a centralized data warehousing framework makes it open to fraud, has vulnerability of single point failure, and the process is nontransparent in nature.
Advanced AI Techniques in Wireless MIMO Communication: Improving Throughput, Latency, and Robustness
Vikram Nattamai Sankaran
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The rapid advancement of wireless communication technologies necessitates innovative solutions to meet the growing demand for high data rates, reliability, and efficiency. This paper presents a novel AI-enhanced Multiple Input Multiple Output (MIMO) communication system that leverages advanced machine learning techniques to optimize beam forming, channel estimation, and resource allocation.
Detailed Cost-Benefit Analysis of Geothermal HVAC Systems for Residential Applications: Assessing Economic and
Performance FactorsAnkitkumar Tejani, Jyoti Yadav, Vinay Toshniwal, Rashi Kandelwal
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Geothermal HVAC systems provide the best solution to conventional HVAC systems since they are economical, environmentally friendly, and long-term cost-efficient. The following paper presents an analysis of the cost and benefits of geothermal HVAC systems in residential areas with respect to costs and performance. Typically, it looks at the purchase prices, the running costs, costs of repairs and maintenance and any environmental effects.
Optimizing Scalability and Performance in Cloud Services: Strategies and SolutionsDr. Saloni Sharma, Ritesh Chaturvedi
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This research paper explores strategies and solutions for optimizing scalability and performance in cloud services. It examines various aspects of cloud architecture, scalability techniques, performance optimization strategies, and advanced technologies. The study delves into vertical and horizontal scaling, auto-scaling techniques, load balancing, caching mechanisms, and database optimization. Additionally, it investigates the role of containerization, serverless computing, and edge computing in enhancing cloud performance.
Harnessing The Power Of Big Data: The Evolution Of AI And Machine Learning In Modern TimesVenkata Nagesh Boddapati,
Eswar Prasad Galla, Janardhana Rao Sunkara, Sanjay Ramdas Bauskar, Gagan Kumar Patra, Chandrababu Kuraku, Chandrakanth Rao Madhavaram
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There has been a tremendous shift in the fields of Artificial Intelligence (AI) and Machine Learning (ML) due to the fast development of big data analytics. Today, and particularly in recent years, we are witnessing the increase in volumes of data originating from social networks, IoT devices, and enterprise systems which have offered the chance to develop more complex and precise AI and ML models. This paper aims to discuss the development of AI and the use of ML, which occurred due to the availability of immense datasets. It also looks at how data availability has helped these novelties gain more accuracy, efficiency, and versatility.
AI-Optimized Bioinformatics Pipelines in DevOpsYogesh Ramaswamy
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The implementation of AI methods into DevOps pipelines within bioinformatics has several effects: Initially, it liberates researchers’ time as it provides for automation of simple and repetitive procedures like data pre-processing and feature extraction. Secondly deployment of AI models can be done at scale as the data can be processed in parallel and distributed fashion. This helps to enhance the rate of processing large amounts of information and the efficiency of functioning. In addition, the integration of AI into the models has been proven to increase the high predictive and classifying effectiveness of bioinformatics results gained from specimens.
CI/CD Automation for Payment Gateways: Azure vs. AWSPavan Kumar Joshi
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The payment industry has evolved a lot in the tech aspect. Free-For-All features a CI/CD culture because of cloud-computing integration intended to improve the CI/CD pipeline for payment gateways. Two cloud platforms, Azure and AWS, provide rich CI/CD services that include numerous automation tools, which enable payment gateways to provide high availability, security, and scalability. This paper considers the Azure and AWS CI/CD solutions for the automated deployment of payment gateways. One of the issues that contribute to the decision is the integration of tools, security, deployment options, and cost.