This study focuses on providing simulation results and analysis of a fault-tolerant voter circuit, with a specific emphasis on the Triple Modular Redundant (TMR) system. TMR systems enhance the robustness of the voter circuit by introducing redundancy at the logic level. The proposed technique aims to minimize the area cost while ensuring multiple fault detection capability.
Wastewater especially from the chemistry laboratory contains several toxic chemicals that are harmful to the environment although the quantity of wastewater produced by the laboratory is relatively small. This study is aimed at analyzing the physicochemical parameters of wastewater collected from the chemical laboratories in Kohima Science College Jotsoma, and Model Christian College, Kohima using standard analytical procedures.
Several vehicles in tropical Africa, such as Nigeria, operate with open window, and this is expected to affect aerodynamics of the vehicles. Based on established theory, Aerodynamic drag is a major component of energy losses which limit the energy available to propel a vehicle. The goal of this study is to determine the effect of open window on the aerodynamics of a typical minibus.
Scaling of technology and increased integration density can cause parameter and noise changes, which can increase error rates at different stages of computing. Soft failures and single-event upsets are a persistent issue in memory applications. This work is primarily concerned with the design of an effective Multi Detector/Decoder (MLDD) for fault detection and fault correction in memory applications.
This work suggests a delay-efficient architecture for shift registers by replacing flip flops with pulsed latches. Using latches instead of flip flops is an excellent way to save space and power. Adding the required delays in pulses for latches helps mitigate the timing issue they display. Included in this is a delay-generating pulse counter. The delays can be obtained simply adding one to the counter. The proposed kogge stone architecture generates several deviations from the standard adder design while simultaneously minimizing delay to the greatest possible extent.
Legacy systems, the cornerstones of many organizations for decades, are increasingly becoming liabilities in the face of evolving technologies and security threats. Modernization, the process of updating these systems, offers a path towards enhanced functionality, security, and efficiency. However, this transformation is not without its costs. This article delves into the complexities of cost-benefit analysis (CBA) for legacy system modernization.
Over the last few years, personal health monitoring wearable devices have emerged as innovative applications of Artificial Intelligence (AI) in the healthcare industry as they help in real time analysis and prediction of health standardized check-ups and health management. To navigate through the current trends, new technologies and developments, the prospects are as follows: The article also gives a logical look at the state of the art of such devices, enumerating the advantages and drawbacks, as well as outlining the main ethical issues.
This study presents a comprehensive review of ethical considerations and emerging trends in AI-based recruitment practices. With the proliferation of artificial intelligence (AI) technologies in the recruitment landscape, organizations face both opportunities and challenges in leveraging these tools to enhance their talent acquisition processes. The study critically analyzes the potential benefits and drawbacks of AI in recruitment, drawing on a systematic literature review encompassing academic journals, industry reports, and case studies.
In recent years, the proliferation of wireless networks and the increasing demand for efficient data transmission have underscored the need for advanced optimization techniques. Machine learning (ML) has emerged as a transformative tool in enhancing the performance and reliability of wireless networks. This paper explores the application of machine learning algorithms to optimize data flow and connectivity in wireless networks.
This paper helps in the development of the concept of hybrid model project management that embraces traditional, agile, and lean approaches in the construction industry. It is to be integrated with traditional, agile, and lean methodologies to carry out effective research on the complexities, dynamic nature, and the challenges faced in modern projects in the construction industry. This approach will adopt agile practices, for instance, iterative development, client collaboration, as well as traditional frameworks, like Gantt charts and CPM, towards bettering the outcome of the project and minimizing delays and cost overrun.
An increasing amount of data leaves organizations to choose efficient, automated data pipelines and warehousing systems to tackle the surging volume and complexity of data, as sanctioned by the prominence of big data and cloud-based solutions. These systems rely on Machine Learning (ML) techniques for their performance and reliability improvement. This paper studies the interfacing of ML within automated data pipelines and warehousing frameworks by investigating how ML models can optimize data ingestion, data transformation, and data quality assurance processes. ML automates anomaly detection, data cleansing, and transformation processes, freeing humans to produce more accurate, reliable data flow from source to storage.
This study explores the development of cloud-agnostic architectures for large-scale online signature verification systems. By decoupling applications from specific cloud providers, organizations can achieve flexibility, scalability, and cost-efficiency. Cloud-agnostic solutions enable seamless operations across diverse cloud environments, addressing vendor lock-in and ensuring consistent performance. Leveraging modern design patterns, advanced cloud-native technologies, and distributed systems principles, the proposed framework enhances the resilience and adaptability of signature verification systems. Performance evaluations demonstrate the effectiveness of cloud-agnostic designs in supporting real-time, large-scale applications while maintaining fault tolerance and high availability.
The study highlights the importance of ServiceNow in improving health data analysis in Rural Health Authorities (RHAs) dealing with unique issues like health disparities, staff shortages, and limited access to healthcare services. ServiceNow provides tools to automate processes, simplify data administration, and deliver real- time analytics. The study examines successful case studies and literature to investigate current practices and obstacles in rural health data analysis. Future developments in ServiceNow, including AI and machine learning, are explored.
Integration and functional testing of systems is quite important for ensuring reliability and correctness. However end-to-end testing is susceptible to randomness due to interactions with external services, which may behave non-deterministically. This presents a need to create hermetic test environments, so that systems can be tested in a vacuum, i.e., without interacting with external dependencies. This normally requires setting up fakes and mocks to stub responses from interactions with external dependencies.
The hiring process determines the company's production and culture by finding qualified and compatible candidates. Industrial-organizational psychology and human resources experts have posted job postings, given exams to assess aptitude, and interviewed to determine compatibility for over a century. Big data and ML have replaced human recruiters with AI in several companies. AI systems' widespread use in recruiting has raised concerns that they may be biased and have an outsized impact if employed systematically. In the previous decade, AI fairness research has increased because of chatbot and candidate assessment algorithm bias.
In recent years, Model-Based Systems Engineering (MBSE) has gained significant traction as a methodology for addressing the increasing complexity of engineering systems. MBSE emphasizes the use of modeling languages, such as SysML and UML, to support the design, simulation, testing, and integration of complex systems. This research paper explores the application of MBSE for the design and integration of complex robotics systems, focusing on the tools, techniques, and benefits of MBSE in the robotics domain. We investigate the challenges associated with subsystem integration, standardization, scalability, and real-time simulation.
It was established that Smart Data Warehousing (SDW) augments data warehousing with AI to advance business intelligence. This paper examines how new data warehousing solutions powered by AI technologies have transformed these raw data. The main focus areas are AI-enhanced SDW’s advantages, new automation trends, self-ranking analytics, and real-time decision-making.
Effective Error Detection in VLSI Circuits Using ComparatorsM.Vinoka, Dr.S.Durairaj
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This study focuses on providing simulation results and analysis of a fault-tolerant voter circuit, with a specific emphasis on the Triple Modular Redundant (TMR) system. TMR systems enhance the robustness of the voter circuit by introducing redundancy at the logic level. The proposed technique aims to minimize the area cost while ensuring multiple fault detection capability.
Physico-Chemical Analysis of Wastewater Samples Collected From the Chemistry Laboratory at Kohima Science College,
Jotsoma and Model Christian College Kohima Vineinu Rhetso, A Chubarenla, Nikili K Zhimo, Neilanuo Huozha, Henwau Hentokhu, Daniel Kibami
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Wastewater especially from the chemistry laboratory contains several toxic chemicals that are harmful to the environment although the quantity of wastewater produced by the laboratory is relatively small. This study is aimed at analyzing the physicochemical parameters of wastewater collected from the chemical laboratories in Kohima Science College Jotsoma, and Model Christian College, Kohima using standard analytical procedures.
Aerodynamic Study of Minibus in Open and Closed Window ScenariosOlusola Oloruntoba, Oluwasanmi Alonge,
Ojotu Joseph, Oluranti Abiola
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Several vehicles in tropical Africa, such as Nigeria, operate with open window, and this is expected to affect aerodynamics of the vehicles. Based on established theory, Aerodynamic drag is a major component of energy losses which limit the energy available to propel a vehicle. The goal of this study is to determine the effect of open window on the aerodynamics of a typical minibus.
LDPC Decoders for the Design and Implementation of High Performance and Low Cost TechniquesSyamsuddin Millang, Siti Nuraeni
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Scaling of technology and increased integration density can cause parameter and noise changes, which can increase error rates at different stages of computing. Soft failures and single-event upsets are a persistent issue in memory applications. This work is primarily concerned with the design of an effective Multi Detector/Decoder (MLDD) for fault detection and fault correction in memory applications.
Using a Kogge Stone Adder to Create Low-Area-Delay Pulsed Latches for a Shift RegisterIng. Liviu Gise
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This work suggests a delay-efficient architecture for shift registers by replacing flip flops with pulsed latches. Using latches instead of flip flops is an excellent way to save space and power. Adding the required delays in pulses for latches helps mitigate the timing issue they display. Included in this is a delay-generating pulse counter. The delays can be obtained simply adding one to the counter. The proposed kogge stone architecture generates several deviations from the standard adder design while simultaneously minimizing delay to the greatest possible extent.
Cost-Benefit Analysis of Legacy System Modernization: A Critical Evaluation for Informed Decision-MakingVijayasekhar Duvvur
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Legacy systems, the cornerstones of many organizations for decades, are increasingly becoming liabilities in the face of evolving technologies and security threats. Modernization, the process of updating these systems, offers a path towards enhanced functionality, security, and efficiency. However, this transformation is not without its costs. This article delves into the complexities of cost-benefit analysis (CBA) for legacy system modernization.
AI-Driven Personal Health Monitoring Devices: Trends and Future DirectionsNaga Ramesh Palakurti
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Over the last few years, personal health monitoring wearable devices have emerged as innovative applications of Artificial Intelligence (AI) in the healthcare industry as they help in real time analysis and prediction of health standardized check-ups and health management. To navigate through the current trends, new technologies and developments, the prospects are as follows: The article also gives a logical look at the state of the art of such devices, enumerating the advantages and drawbacks, as well as outlining the main ethical issues.
An Analytical Review of Contemporary AI-Driven Hiring Strategies in Professional ServicesAmit Mangal
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This study presents a comprehensive review of ethical considerations and emerging trends in AI-based recruitment practices. With the proliferation of artificial intelligence (AI) technologies in the recruitment landscape, organizations face both opportunities and challenges in leveraging these tools to enhance their talent acquisition processes. The study critically analyzes the potential benefits and drawbacks of AI in recruitment, drawing on a systematic literature review encompassing academic journals, industry reports, and case studies.
Machine Learning in Wireless Networks: Optimizing Data Flow and ConnectivityVikram Nattamai Sankaran, Dr. M. Sivasankari,
Rakesh Thoppaen Suresh Babu
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In recent years, the proliferation of wireless networks and the increasing demand for efficient data transmission have underscored the need for advanced optimization techniques. Machine learning (ML) has emerged as a transformative tool in enhancing the performance and reliability of wireless networks. This paper explores the application of machine learning algorithms to optimize data flow and connectivity in wireless networks.
Developing a Hybrid Approach: Combining Traditional and Agile Project Management Methodologies in Construction Using
Modern Software ToolsRinkesh Gajera
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This paper helps in the development of the concept of hybrid model project management that embraces traditional, agile, and lean approaches in the construction industry. It is to be integrated with traditional, agile, and lean methodologies to carry out effective research on the complexities, dynamic nature, and the challenges faced in modern projects in the construction industry. This approach will adopt agile practices, for instance, iterative development, client collaboration, as well as traditional frameworks, like Gantt charts and CPM, towards bettering the outcome of the project and minimizing delays and cost overrun.
The Role of Machine Learning in Automated Data Pipelines and Warehousing: Enhancing Data Integration, Transformation,
and AnalyticsAbhishek Vajpayee
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An increasing amount of data leaves organizations to choose efficient, automated data pipelines and warehousing systems to tackle the surging volume and complexity of data, as sanctioned by the prominence of big data and cloud-based solutions. These systems rely on Machine Learning (ML) techniques for their performance and reliability improvement. This paper studies the interfacing of ML within automated data pipelines and warehousing frameworks by investigating how ML models can optimize data ingestion, data transformation, and data quality assurance processes. ML automates anomaly detection, data cleansing, and transformation processes, freeing humans to produce more accurate, reliable data flow from source to storage.
Implementing Cloud-Agnostic Solutions for Large-Scale Signature Verification SystemsManoj Chavan
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This study explores the development of cloud-agnostic architectures for large-scale online signature verification systems. By decoupling applications from specific cloud providers, organizations can achieve flexibility, scalability, and cost-efficiency. Cloud-agnostic solutions enable seamless operations across diverse cloud environments, addressing vendor lock-in and ensuring consistent performance. Leveraging modern design patterns, advanced cloud-native technologies, and distributed systems principles, the proposed framework enhances the resilience and adaptability of signature verification systems. Performance evaluations demonstrate the effectiveness of cloud-agnostic designs in supporting real-time, large-scale applications while maintaining fault tolerance and high availability.
Using Service Now to Analyze Health Data in Rural Health AuthoritySravanthi Mallireddy
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The study highlights the importance of ServiceNow in improving health data analysis in Rural Health Authorities (RHAs) dealing with unique issues like health disparities, staff shortages, and limited access to healthcare services. ServiceNow provides tools to automate processes, simplify data administration, and deliver real- time analytics. The study examines successful case studies and literature to investigate current practices and obstacles in rural health data analysis. Future developments in ServiceNow, including AI and machine learning, are explored.
RPC Replay for Robust Testing of Golang ApplicationsNilesh Jagnik
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Integration and functional testing of systems is quite important for ensuring reliability and correctness. However end-to-end testing is susceptible to randomness due to interactions with external services, which may behave non-deterministically. This presents a need to create hermetic test environments, so that systems can be tested in a vacuum, i.e., without interacting with external dependencies. This normally requires setting up fakes and mocks to stub responses from interactions with external dependencies.
Mitigating AI Bias in Recruitment: Policy Approaches for Transparent Candidate Selection and Broader Implications for Trust in Algorithmic DecisionsTasriqul Islam, Sadia Afrin
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The hiring process determines the company's production and culture by finding qualified and compatible candidates. Industrial-organizational psychology and human resources experts have posted job postings, given exams to assess aptitude, and interviewed to determine compatibility for over a century. Big data and ML have replaced human recruiters with AI in several companies. AI systems' widespread use in recruiting has raised concerns that they may be biased and have an outsized impact if employed systematically. In the previous decade, AI fairness research has increased because of chatbot and candidate assessment algorithm bias.
Model-Based Systems Engineering (MBSE) for the Design and Integration of Complex Robotics SystemsShashank Pasupuleti
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In recent years, Model-Based Systems Engineering (MBSE) has gained significant traction as a methodology for addressing the increasing complexity of engineering systems. MBSE emphasizes the use of modeling languages, such as SysML and UML, to support the design, simulation, testing, and integration of complex systems. This research paper explores the application of MBSE for the design and integration of complex robotics systems, focusing on the tools, techniques, and benefits of MBSE in the robotics domain. We investigate the challenges associated with subsystem integration, standardization, scalability, and real-time simulation.
The Evolution of Smart Data Warehousing: How AI Is Taking Business Intelligence to Unimaginable HeightsPrem Tamanam
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It was established that Smart Data Warehousing (SDW) augments data warehousing with AI to advance business intelligence. This paper examines how new data warehousing solutions powered by AI technologies have transformed these raw data. The main focus areas are AI-enhanced SDW’s advantages, new automation trends, self-ranking analytics, and real-time decision-making.