Monitoring project performance is a cornerstone of success in technology-driven industries. Projects in semiconductors, software/IT, and retail (supply chain) are increasingly complex, requiring robust anomaly detection methods to identify deviations in schedule, cost, quality, and throughput. Traditional approaches are often siloed, applying statistical thresholds or isolated machine learning techniques to single domains. This paper presents an AI-enhanced, KPI-driven anomaly detection framework validated on real-world datasets.
The electric vehicle (EV) and renewable energy generation have achieved considerable development due to the growing energy demand and scarcity in fossil fuels. At the same time, EVs consume a huge amount of electricity when they are clustered in a charging station. In this project we are going to create a Artificial Intelligence based Power Allocation and ev charging system. We are using a deep learning technique called artificial Neural Network and hence we can able to get an accuracy over 90%. We predict the suitable power source for charging the electric vehicles using artificial Neural Network.
In solar photovoltaic (PV) inverter systems, power losses in the input loop significantly impact overall efficiency and performance. This paper presents a Super Capacitor Assisted (SCA) technique to minimize conduction and switching losses in the input stage of an inverter system for solar PV applications. By integrating supercapacitors strategically within the power circuit, the proposed method reduces peak current stress, stabilizes voltage fluctuations, and enhances transient response. The project provides a detailed analysis of the working principle, power loss reduction mechanisms, and the design considerations for implementing the SCA technique.
This paper proposes a scalable low-latency fault-tolerant architecture for real-time web log analytics based on the native stream processing services of Google Cloud Platform. The main contribution is an end-to-end system design that uses Pub/Sub high volume ingestion and custom Dataflow (Apache Beam) pipeline to process high-throughput unstructured log streams plus details of custom parsing, real-time enrichment via Beam Enrichment transform, and event time-based aggregation techniques.
This study examines the impact of artificial intelligence (AI) on the changing roles of nurses in patient care, emphasizing its growing significance in modern healthcare systems. The research highlights how AI integration reshapes nursing practices by automating clinical documentation, supporting real-time monitoring, and enhancing clinical decision-making. A quantitative research design was utilized, involving twenty (20) nurses—ten from private hospitals and ten from public hospitals—to assess their perceptions and experiences regarding AI-assisted care.
The integrity of large-scale, heterogeneous data ecosystems is fundamental to the reliability of downstream AI systems. Existing data quality solutions, however, rely on brittle, imperative scripting and fail to adapt to the complex data distribution shifts inherent in modern enterprise environments. This paper introduces a novel, AI-powered framework that recasts data quality monitoring as an intelligent, adaptive process.
Digital twin technologies have become rapidly growing elements in businesses' pursuits of operational efficiencies, sustainability, and waste reductions. Digital twins are of interest among Small- and Medium-sized Enterprises (SMEs), which often have limited resources and capabilities, and want to adapt their implementation of a digital twin for operational process efficiencies, decision-making improvements, and waste reductions of material inputs and energy.
The quick growth of enterprise data has made Retrieval-Augmented Generation (RAG) a critical approach for enabling precise and context-rich responses in domain-specific applications. In the sales intelligence domain, where decision- making relies on both structured and unstructured data, conventional RAG approaches—such as vector-based retrieval, graph-augmented retrieval, and hybrid frameworks— usually lack balancing factual accuracy, contextual reasoning, and adaptability to diverse query types.
This rapid generation of semiconductor technologies has caused the models based on chiplets to succeed and the physical constraint of monolithic Systems-on-Chip (SoC) systems. Chiplet-based SoCs encourage modular integration where a design can be compiled of heterogeneous parts which are produced in another technology node. The conceptual basic technologies of the chiplet-based design of SoC, specifically the interface protocols and partitioning, are critiqued in the paper.
This paper seeks to establish the cognitive and philosophical basis supporting the idea of synthetically created persons. The author posits that the identity of artificial agents depends on the three pillars: continuity (of core data structures and learning weights), memory (episodic, semantic, procedural, affective), and self-recognition (self–other and goal discrimination) which leads to the concept of iBrain as a post-LLM cognitive framework that combines persistent memory, embedded ethical reasoning, self-monitoring, and context engineering.
The second part of the research "Synthetic People" delves into the representation of artificial consciousness, its interaction, and control as it gradually goes from inner thought to outer world interaction. The section discuss how artificial beings acquire their 'body,' how they understand and communicate through Sensory-Expressive Interfaces (SEIs), how they take care of themselves through self-reliant power sources, and if they are different and have their own rights in a world that has both humans and synthetic beings.
Part III of Synthetic People is about what happens after the resolution of the mind and body—the civilization which results from the co-evolution of synthetic persons (iBrains), cyborgs, and humans. The paper asserts that continuity is not only the replication of memory; it demands preserved context and aligned goals in order to maintain identity across substrates and time. Different systems of governance are outlined for a scenario in which “birth” may mean instantiation and “death” may mean deletion, among them continuity rights and due-process standards.
Artificial Intelligence (AI) is rapidly reshaping industrial systems worldwide, offering unprecedented opportunities for sustainable development in Africa. This study examines how AI-driven decision-making can enhance sustainable industrialization across key African economies by improving efficiency, reducing waste, and supporting environmentally responsible practices. Using a mixed-methods approach, data were collected from 40 firms across Kenya, Nigeria, South Africa, and Zimbabwe, revealing that AI adoption remains moderate but uneven across sectors and countries.
Artificial intelligence (AI) is reshaping manufacturing through quality control, maintenance, and supply chain planning, yet adoption in Sub Saharan Africa is uneven. The researchers synthesize evidence on AI adoption with a focus on Zimbabwe, guided by Technology Organization Environment (TOE), the Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology (TAM/UTAUT), the Resource Based View (RBV), and Dynamic Capabilities. The researcher applies a transparent selection process and includes n = 26 studies: Zimbabwe (n = 2), South Africa (n = 2), Africa/regional (n = 0), and global/other (n = 22).
The most recent ideas in the Contract Lifecycle Management (CLM) of cloud-based Enterprise Resource Planning (ERP), and their radical use in other industries, will be discussed in the paper. The dynamic, scalable, intelligent systems also exist in ERP cloud providers, and they can be applied to support the contract creation, execution, and compliance process. Such systems would make the process more transparent, reduce risks, and improve efficiency by combining Artificial Intelligence (AI) with Advanced Planning and Scheduling (APS) and customer-oriented contracting processes.
The rapid transformation of cloud computing and distributed systems has created the need to devise a fault-tolerant system for the deployment of pipeline infrastructure that can offer high availability and resiliency in operation. So far, automated customization of the processes of delivery, auditing, and recovery of complex infrastructure settings has become a widespread practice involving tools like Terraform and Apache Airflow.This paper presents definitions of modern methods and techniques for establishing fault-tolerant infrastructure through the application of such tools.
Effective accounts payable (AP) management is an important factor in operational excellence and financial stability in modern businesses. AP function, which simplifies cash flow and oversees supplier responsibilities, changed considerably depending on the changes of technologies and the dynamics of global supply chains. In this paper, the researcher examines how machine learning is applicable to automated invoice payment prediction to support the technique of effective financial processing in large-scale invoice distribution systems.
The rapid expansion of online financial transactions has led to an increase in fraudulent activities, posing significant challenges for digital security systems. Detecting fraud in real-time is complicated by the evolving nature of fraud strategies, a phenomenon known as concept drift, where the statistical properties of transaction data change over time. Traditional static Machine Learning (ML) models often struggle to maintain accuracy in such dynamic environments.
Automated testing is the cornerstone of software reliability. But authoring and maintaining functional regression tests continues to demand significant manual effort. Traditional approaches such as Espresso and Appium require engineers to script explicit user interactions into the tests. This rapidly becomes brittle as product features evolve. At the same time, every modern application already captures extensive telemetry data. Those include, but not limited to, screen impressions, navigations and user interactions. This represents a detailed record of real user behavior on the app.
It is difficult to agree on a restaurant to eat at, especially in groups which make it time-consuming. An intelligent recommendation system is presented in this work which leverages machine learning and the genetic algorithms for restaurant selection based on user behaviour. The system has been executed like a web-based app that makes recommendations for the individual and group alike. It also helps to reduce the choice overload of the target audience or user group. Because the approach is data-driven it is better than random. After testing, its efficacy is proven in making decision making easy for one and all.
Software delivery is accelerated by the use of CI/CD pipelines. However, they are accompanied by a very high risk of introducing performance regression, especially when high-stakes workloads such as those in the financial sector are involved. This paper proposes a methodology known as Continuous Financial Performance Assurance (CFPA) methodology which integrates Apache JMeter for realistic scenario test design, BlazeMeter for scalable cloud execution, and Jenkins automation for pipelining all together.
AI-Enhanced Anomaly Detection for Project Performance: A Cross-Industry Study for Technology-Driven Industries
Shreya Makinani, Pankaj Siri Bharath Bairu
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Monitoring project performance is a cornerstone of success in technology-driven industries. Projects in semiconductors, software/IT, and retail (supply chain) are increasingly complex, requiring robust anomaly detection methods to identify deviations in schedule, cost, quality, and throughput. Traditional approaches are often siloed, applying statistical thresholds or isolated machine learning techniques to single domains. This paper presents an AI-enhanced, KPI-driven anomaly detection framework validated on real-world datasets.
Power Allocation System Using Artificial Neural NetworkAriramar C, Ramraj S
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The electric vehicle (EV) and renewable energy generation have achieved considerable development due to the growing energy demand and scarcity in fossil fuels. At the same time, EVs consume a huge amount of electricity when they are clustered in a charging station. In this project we are going to create a Artificial Intelligence based Power Allocation and ev charging system. We are using a deep learning technique called artificial Neural Network and hence we can able to get an accuracy over 90%. We predict the suitable power source for charging the electric vehicles using artificial Neural Network.
Super Capacitor Assisted Technique for Reducing Losses in the Input Loop of an Inverter System for Solar PV Application
Mr. Anandharaj R, Ms. A. Shiny Pradeepa
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In solar photovoltaic (PV) inverter systems, power losses in the input loop significantly impact overall efficiency and performance. This paper presents a Super Capacitor Assisted (SCA) technique to minimize conduction and switching losses in the input stage of an inverter system for solar PV applications. By integrating supercapacitors strategically within the power circuit, the proposed method reduces peak current stress, stabilizes voltage fluctuations, and enhances transient response. The project provides a detailed analysis of the working principle, power loss reduction mechanisms, and the design considerations for implementing the SCA technique.
Building a Real-time Data Ingestion Platform for Web Log Analytics using GCP Pub/Sub and DataflowVamshi Krishna Pamula
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This paper proposes a scalable low-latency fault-tolerant architecture for real-time web log analytics based on the native stream processing services of Google Cloud Platform. The main contribution is an end-to-end system design that uses Pub/Sub high volume ingestion and custom Dataflow (Apache Beam) pipeline to process high-throughput unstructured log streams plus details of custom parsing, real-time enrichment via Beam Enrichment transform, and event time-based aggregation techniques.
The Impact of Artificial Intelligence on the Changing Roles of Nurses in Patient Care: A Quantitative Study
Archiles Briones Tolentibo
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This study examines the impact of artificial intelligence (AI) on the changing roles of nurses in patient care, emphasizing its growing significance in modern healthcare systems. The research highlights how AI integration reshapes nursing practices by automating clinical documentation, supporting real-time monitoring, and enhancing clinical decision-making. A quantitative research design was utilized, involving twenty (20) nurses—ten from private hospitals and ten from public hospitals—to assess their perceptions and experiences regarding AI-assisted care.
Intelligent Data Governance: A Declarative, AI-Powered Framework for Monitoring Quality across Heterogeneous Data
EcosystemsVatsal Kishorbhai Mavani
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The integrity of large-scale, heterogeneous data ecosystems is fundamental to the reliability of downstream AI systems. Existing data quality solutions, however, rely on brittle, imperative scripting and fail to adapt to the complex data distribution shifts inherent in modern enterprise environments. This paper introduces a novel, AI-powered framework that recasts data quality monitoring as an intelligent, adaptive process.
Waste Elimination through Digital Twins: A Pilot Framework for SMEsNithin Subba Rao
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Digital twin technologies have become rapidly growing elements in businesses' pursuits of operational efficiencies, sustainability, and waste reductions. Digital twins are of interest among Small- and Medium-sized Enterprises (SMEs), which often have limited resources and capabilities, and want to adapt their implementation of a digital twin for operational process efficiencies, decision-making improvements, and waste reductions of material inputs and energy.
Beyond Static Retrieval: Evaluating Agentic RAG for Sales Intelligence ApplicationsHimnish A, Mayank Singh, Ujjol Chakraborty,
Dr. Vasudha Vashisht
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The quick growth of enterprise data has made Retrieval-Augmented Generation (RAG) a critical approach for enabling precise and context-rich responses in domain-specific applications. In the sales intelligence domain, where decision- making relies on both structured and unstructured data, conventional RAG approaches—such as vector-based retrieval, graph-augmented retrieval, and hybrid frameworks— usually lack balancing factual accuracy, contextual reasoning, and adaptability to diverse query types.
Interface and Partitioning Techniques for Chiplet SoCsSrikanth Aitha
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This rapid generation of semiconductor technologies has caused the models based on chiplets to succeed and the physical constraint of monolithic Systems-on-Chip (SoC) systems. Chiplet-based SoCs encourage modular integration where a design can be compiled of heterogeneous parts which are produced in another technology node. The conceptual basic technologies of the chiplet-based design of SoC, specifically the interface protocols and partitioning, are critiqued in the paper.
Synthetic People: Part I – Context Engineering and the Cognitive Formation of Self in iBrain ArchitecturesJackson Andrew
Srivathsan
Download
This paper seeks to establish the cognitive and philosophical basis supporting the idea of synthetically created persons. The author posits that the identity of artificial agents depends on the three pillars: continuity (of core data structures and learning weights), memory (episodic, semantic, procedural, affective), and self-recognition (self–other and goal discrimination) which leads to the concept of iBrain as a post-LLM cognitive framework that combines persistent memory, embedded ethical reasoning, self-monitoring, and context engineering.
Synthetic People: Part II – Embodied Autonomy and Energy Systems of Coexistence in AI-Borg ArchitecturesJackson Andrew
Srivathsan
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The second part of the research "Synthetic People" delves into the representation of artificial consciousness, its interaction, and control as it gradually goes from inner thought to outer world interaction. The section discuss how artificial beings acquire their 'body,' how they understand and communicate through Sensory-Expressive Interfaces (SEIs), how they take care of themselves through self-reliant power sources, and if they are different and have their own rights in a world that has both humans and synthetic beings.
Synthetic People: Part III – Continuity, Rebirth, and the Post-Linguistic Evolution of Synthetic CivilizationJackson Andrew
Srivathsan
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Part III of Synthetic People is about what happens after the resolution of the mind and body—the civilization which results from the co-evolution of synthetic persons (iBrains), cyborgs, and humans. The paper asserts that continuity is not only the replication of memory; it demands preserved context and aligned goals in order to maintain identity across substrates and time. Different systems of governance are outlined for a scenario in which “birth” may mean instantiation and “death” may mean deletion, among them continuity rights and due-process standards.
AI-Driven Decision-Making for Sustainable Industrialization in AfricaYogesh Awasthi, Talon Garikayi, Elizabeth Mafu
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Artificial Intelligence (AI) is rapidly reshaping industrial systems worldwide, offering unprecedented opportunities for sustainable development in Africa. This study examines how AI-driven decision-making can enhance sustainable industrialization across key African economies by improving efficiency, reducing waste, and supporting environmentally responsible practices. Using a mixed-methods approach, data were collected from 40 firms across Kenya, Nigeria, South Africa, and Zimbabwe, revealing that AI adoption remains moderate but uneven across sectors and countries.
Adoption of Artificial Intelligence in the Zimbabwean Manufacturing Sector: A Critical Review and Research Agenda
Thomas Masese, Chidochomoyo Sango, Yogesh Awasthi
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Artificial intelligence (AI) is reshaping manufacturing through quality control, maintenance, and supply chain planning, yet adoption in Sub Saharan Africa is uneven. The researchers synthesize evidence on AI adoption with a focus on Zimbabwe, guided by Technology Organization Environment (TOE), the Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology (TAM/UTAUT), the Resource Based View (RBV), and Dynamic Capabilities. The researcher applies a transparent selection process and includes n = 26 studies: Zimbabwe (n = 2), South Africa (n = 2), Africa/regional (n = 0), and global/other (n = 22).
Advanced Strategies for Lifecycle Management for Contracts in Cloud-Based ERP SystemsPraveen Kumar Ilamurugan
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The most recent ideas in the Contract Lifecycle Management (CLM) of cloud-based Enterprise Resource Planning (ERP), and their radical use in other industries, will be discussed in the paper. The dynamic, scalable, intelligent systems also exist in ERP cloud providers, and they can be applied to support the contract creation, execution, and compliance process. Such systems would make the process more transparent, reduce risks, and improve efficiency by combining Artificial Intelligence (AI) with Advanced Planning and Scheduling (APS) and customer-oriented contracting processes.
Building Fault Tolerant Infrastructure Deployment Pipelines Using Terraform and AirflowDung Le
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The rapid transformation of cloud computing and distributed systems has created the need to devise a fault-tolerant system for the deployment of pipeline infrastructure that can offer high availability and resiliency in operation. So far, automated customization of the processes of delivery, auditing, and recovery of complex infrastructure settings has become a widespread practice involving tools like Terraform and Apache Airflow.This paper presents definitions of modern methods and techniques for establishing fault-tolerant infrastructure through the application of such tools.
Data-Driven Optimization of Accounts Payable for Improved Financial Efficiency and Supplier Relations
Sandeep Gupta, Ruhul Quddus Majumder
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Effective accounts payable (AP) management is an important factor in operational excellence and financial stability in modern businesses. AP function, which simplifies cash flow and oversees supplier responsibilities, changed considerably depending on the changes of technologies and the dynamics of global supply chains. In this paper, the researcher examines how machine learning is applicable to automated invoice payment prediction to support the technique of effective financial processing in large-scale invoice distribution systems.
Adaptive Online Fraud Detection: Comparative Study of Machine Learning, Deep Learning, and Hybrid Models with Concept Drift
Simulation Sushant Rajaram Thite
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The rapid expansion of online financial transactions has led to an increase in fraudulent activities, posing significant challenges for digital security systems. Detecting fraud in real-time is complicated by the evolving nature of fraud strategies, a phenomenon known as concept drift, where the statistical properties of transaction data change over time. Traditional static Machine Learning (ML) models often struggle to maintain accuracy in such dynamic environments.
FlowMind: Mining Real User Telemetry to Power LLM-Driven Autonomous App Testing Sachin Francis
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Automated testing is the cornerstone of software reliability. But authoring and maintaining functional regression tests continues to demand significant manual effort. Traditional approaches such as Espresso and Appium require engineers to script explicit user interactions into the tests. This rapidly becomes brittle as product features evolve. At the same time, every modern application already captures extensive telemetry data. Those include, but not limited to, screen impressions, navigations and user interactions. This represents a detailed record of real user behavior on the app.
Smart Dining: An AI-Powered Personalized Restaurant Recommender System Dilip Sharma
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It is difficult to agree on a restaurant to eat at, especially in groups which make it time-consuming. An intelligent recommendation system is presented in this work which leverages machine learning and the genetic algorithms for restaurant selection based on user behaviour. The system has been executed like a web-based app that makes recommendations for the individual and group alike. It also helps to reduce the choice overload of the target audience or user group. Because the approach is data-driven it is better than random. After testing, its efficacy is proven in making decision making easy for one and all.
A Novel Approach to Automated Performance Testing for Web Applications using JMeter and BlazeMeter Santosh
Kumar Kotakonda
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Software delivery is accelerated by the use of CI/CD pipelines. However, they are accompanied by a very high risk of introducing performance regression, especially when high-stakes workloads such as those in the financial sector are involved. This paper proposes a methodology known as Continuous Financial Performance Assurance (CFPA) methodology which integrates Apache JMeter for realistic scenario test design, BlazeMeter for scalable cloud execution, and Jenkins automation for pipelining all together.