Urban traffic congestion is a growing concern worldwide, particularly in developing countries like India. Traffic signal control strategies play a vital role in regulating vehicular flow at intersections. This paper presents a comprehensive review of fixed-time and actuated traffic signal control systems, comparing their efficiency, implementation, and adaptability. It evaluates signal timing techniques, performance indicators, and real-world applications. Challenges and opportunities associated with each system are discussed, supported by Indian and global case studies. The paper concludes with recommendations for future research and policy integration in smart traffic systems.

Social media applications are being widely popular in this era such as Instagram, Twitter, Facebook and etc.The users of social media have been vigorously increasing as that of its data. Hence many researchers started to study and analyse it for different purposes, like based on locations detecting the event photos, clustering its contents, advertising strategies etc.Our Target here is to develop an application for social sites which will arrange images by their captions using TF IDF. Method to rank the keywords of top twenty users based on 10 newly image captions are used.TF-IDF is the method used successfully in this paper to reveal a set of keywords with its ranking. The highest ranking of keywords shows the current topic of user. TF-IDF is an useful to find and rank the keywords of social media users image caption. Thisapplication is suitable for Facebook, Instagram etc. for arranging these types of images by caption. We are using Java, JSP as front end and MySQL as backend also will use bootstrap, CSS, JQuery for better GUI. Here we are uploading images and caption then performing TF IDF on that after calculation we are using show result button which will display top 10 images based on our trained caption. This will check all images uploaded by our friends.”

The aim of this paper in to device and implement an educational robot with end effecter (Gripper), the arm include the electrical, wiring controller and devices to make the robot work correctly. The mechanical parts were manufactured by nontraditional cutting (Abrasive water jet), bending and drilling operation. The robot parts are assembled together the electrical element controllers in their right positions. An appropriate design selection for gripper is selected so it is can to with stand for light loads. To evaluate the static and dynamic behavior of the robot a selected test have been made for the robot to do some simple actions and the actions were acceptable from all mechanical actions were some motions are selected and whole system behavior was acceptable (motion – control –target.

This research used laboratory test techniques to evaluate (investigate) the effect of municipal solid waste leachate (MSWL) particularly, on the compressive strength of concrete. The laboratory test was conducted on 72 concrete cubes of 150mm x 150mm x 150mm in size. The water/cement ratio of 0.55 was used with varying water/Leachate ratios of 100:00, 75:25, 50:50 and 00:100 respectively. The cubes were divided in to two and cured in both water and leachate medium for 7 days, 14 days and 28 days accordingly. The findings revealed that the municipal solid waste leachate (MSWL) reduced the compressive strengths of the cubes after 28 days of age by 2.79 % for zero leachate cured in leachate medium.

As globalization, digital transformation, and regulatory complexity reshape the human capital landscape, large enterprises face increasing pressure to manage workforce mobility, retain high-value talent, and maintain compliance across jurisdictions. Traditional HR approaches often fall short in addressing these evolving demands due to their reactive nature and fragmented data sources. This paper examines how predictive people analytics can be harnessed as a strategic lever to optimize workforce decisions and align them with enterprise-wide performance, mobility goals, and legal obligations. By integrating advanced data modeling techniques including machine learning, sentiment analysis, and attrition forecasting organizations can identify early signals of flight risk, engagement decline, and compliance vulnerabilities at both local and global levels. Particular attention is paid to modeling cross-border mobility patterns, capturing cultural and operational factors that influence relocation success, and ensuring compliance with region-specific labor laws and privacy regulations. The study also highlights the role of data governance, ethical AI deployment, and cross-functional collaboration in unlocking the full potential of predictive HR systems. Through real-world enterprise use cases and a proposed implementation framework, the paper offers actionable insights for HR leaders, compliance officers, and workforce strategists aiming to build more resilient, responsive, and compliant global talent ecosystems. Ultimately, predictive people analytics is positioned not merely as a reporting tool, but as a critical enabler of strategic foresight in human capital planning and global workforce optimization.

Fraud detection and regulatory compliance remain persistent challenges across financial, healthcare, and digital ecosystems, where fraudulent activities evolve rapidly and exploit systemic vulnerabilities. Traditional rule-based compliance systems are rigid and often struggle to detect subtle, emerging risks in complex environments. Recent advances in artificial intelligence, particularly time series analysis, offer new opportunities to design adaptive compliance frameworks that can anticipate anomalies, detect deviations in real time, and dynamically recalibrate regulatory responses. At a broader level, the integration of time series fraud detection models addresses the escalating sophistication of fraudulent behaviors across industries, aligning compliance efforts with proactive rather than reactive risk management. These models leverage statistical learning, recurrent neural networks, and hybrid deep learning architectures to capture temporal dependencies and detect rare yet impactful anomalies. When applied to regulatory contexts, adaptive frameworks informed by time series modeling can streamline oversight by embedding continuous monitoring, anomaly scoring, and early warning systems into institutional workflows. This not only improves fraud resilience but also enhances trust, transparency, and cost-efficiency in compliance operations. In dynamic environments such as financial markets or cross-border payment systems, adaptive compliance becomes essential to manage shifting regulatory landscapes and diverse jurisdictional requirements. Narrowing this focus, the proposed framework highlights the value of time series fraud detection in tailoring compliance strategies for highly volatile domains, enabling regulators and organizations to predict risk trajectories and deploy timely interventions. Ultimately, designing adaptive compliance frameworks through time series models bridges technological innovation with governance needs, ensuring resilience in an era of accelerating financial and regulatory complexity.

In 2017, organizations face intensifying pressure to accelerate digital delivery while maintaining reliability, security, and customer satisfaction. Traditional process frameworks provide valuable structure but often fail to capture the dynamic, cross-functional realities of Lean-Agile and DevOps transformations. To address this gap, this paper proposes a structured Lean-Agile and DevOps Maturity Model that guides enterprises on their journey toward continuous improvement. The model synthesizes industry frameworks such as SAFe and Scrum with DevOps practices including automated testing, continuous integration, and telemetry. It defines clear levels of maturity across dimensions such as deployment automation, release management, observability, security integration, and organizational culture. By providing a staged roadmap, the model enables teams to benchmark their current capabilities, identify bottlenecks, and prioritize investments. Through case studies in financial services, telecommunications, and e-commerce, we demonstrate how organizations applying this model reduced release lead times by more than 70%, improved deployment success rates by 40%, and institutionalized continuous improvement practices at scale. We conclude that structured maturity models remain essential in 2017 for aligning leadership, technology, and culture, offering both a diagnostic lens and a continuous improvement engine for modern enterprises.