Unlocking Customer Attributes: RFM Segmentation and CLTV Insights. The application aims to identify valuable patterns in behavior and preferences through this strategic combination of RFM (recency, frequency, monetary) segmentation and CLTV (lifetime value) analysis. This progressive approach gives complete insights into current patron purchase habits, transaction frequency, and the economic performance of the business. Through RFM segmentation, this system seeks to become aware of distinct consumer businesses, allowing corporations to design their marketing strategies more effectively. Additionally, the use of CLTV evaluation affords a comprehensive view of each consumer's long-term price, helping organizations prioritize and expand valuable relationships. Ultimately, this system promises to offer corporations actionable insights to be able to enhance their advertising efforts, improve consumer pride, and increase ordinary profitability.

The Mediation Act, 2023 is India's first dedicated and comprehensive cross-sectoral legislation on mediation. It is both significant and yet legally complex in its ramifications for matrimonial and family disputes because the “family” adjudication in India is already legally complex due to the personal-law reconciliation functions that are integrated into the Hindu Marriage Act, 1955 and the Special Marriage Act, 1954, the Family Courts Act, 1984, and protective measures like the Protection of Women from Domestic Violence Act, 2005. This paper contends that the Act’s implications should not be considered a blanket “pro mediation” reform, but rather, a highly contested framing of the possible and the limited scope of embedded mediation, along with the mediation ‘exclusionary’ non-categories, confidentiality, and the integrated court ecosystem. The paper advances three main arguments. To begin with, the Act’s “overriding effect” clause is expressly subjacent to a number of enactments, including the Family Courts Act, 1984, which points to a possible intentionality to retain the family court conciliation frameworks and the High Court mediation rules. Second, although the Act creates a first in domestic violence/child abuse for confidentiality exceptions, this protective measure is a reactive one and needs to be accompanied by structural screening, representation, trauma informed, and judicial review, in order to adequately protect vulnerable parties. Third, the Court Annexed Mediation Centers continue to play an important role, especially given that transitional provisions maintain courtannexed rules until new standards are entered, and given that national efforts (e.g. “Mediation ‘For the Nation’” campaign in 2025) reflect the State’s growing dependence on institutional mediation for disputes involving domestic and family violence) holds three. Using doctrinal analysis, feminist and access-to-justice theory, parliamentary materials, and Supreme Court case law, this paper addresses two questions: whether specific disputes should be completely excluded, and how to safeguard the interests of vulnerable parties without compromising the therapeutic and administratively efficient aspects of the settlement.

Modern thermal power plants are increasingly integrating Operational Technology (OT) networks with higher-level analytics platforms such as OSI PI and cloud-based systems to support digital transformation initiatives. However, such integrations significantly increase cybersecurity exposure, particularly when legacy and unsupported network infrastructure is retained. This paper presents a lifecycle-driven and cyber-aware modernization of an OT network backbone involving the replacement of legacy Cisco Catalyst 2960 Series switches with Cisco Catalyst 1300 Series and Catalyst 9200L switches. The upgrade was necessitated by End-of-Sale (EOS) and End-of-Support (EoS) declarations, coupled with the introduction of OSI PI data flow from DCS through Kepware servers to cloud platforms. In addition to infrastructure upgrade, a SCADA-based network health monitoring system was developed to visualize the availability of redundant networks (NET-A and NET-B), enabling early detection of network degradation or failure. All switch configuration, testing, and commissioning were performed in-house through self-learning, without reliance on external vendors. Enhanced network segmentation, and security policies were implemented to mitigate cybersecurity risks while ensuring performance and scalability. The results demonstrate improved reliability, security posture, and internal capability development, providing a repeatable framework for OT network modernization

Smart Greenhouse Gardening: An Intelligent Smart Greenhouse Monitoring and Automated Plant Protection System, designed to improve crop growth through IoT-based sensing and AI-driven automation. The system continuously monitors temperature, humidity, soil moisture, and light using smart sensors, while AI models detect plant diseases and pest infection at an early stage. Based on real-time data, automated actions such as irrigation, ventilation, and lighting control are performed to maintain optimal growing conditions. The solution reduces manual effort, improves resources efficiency, and supports healthier plant growth in greenhouse environments.

The expansion of Internet of Things (IoT) ecosystems and cyber–physical systems has shifted artificial intelligence processing from centralized cloud infrastructures to resource-constrained edge devices. Although edge computing enables lower latency, reduced network congestion, and enhanced data privacy, it also presents significant obstacles for deep learning training due to limited computation power, energy constraints, and hardware heterogeneity. To address these challenges, this paper introduces a Hybrid Parallel Distributed Deep Learning Framework tailored for heterogeneous edge environments. The proposed approach integrates data parallelism and model parallelism to efficiently utilize diverse edge resources. Workload distribution is adaptively managed based on device capabilities, network variability, and energy availability. Experimental evaluations using image classification tasks show that the proposed framework achieves superior training efficiency, improved energy utilization, and enhanced model performance when compared with centralized cloud training and single-parallel edge learning methods.

This review surveys recent methods for vehicle detection, tracking, and speed estimation using monocular video, stereo vision, aerial imagery, LiDAR, and hardware-assisted optical sensing.Detection backbones such as YOLO, SSD, and Faster RCNN areexaminedalongsidetrackingalgorithmsincludingSORT,DeepSORT,andByteTrack.Monocularhomography,stereodepth estimation,LiDAR-basedtracking,andopticalmodulationapproachesareanalyzedintermsofaccuracy,robustness,anddeploy-ment feasibility. Thepapersummarizesdatasets,evaluationmetrics,andresearchtrends,whileidentifyingkeychallengessuchas weather robustness, lackofstandardizedspeedgroundtruth,andreal-timesensorfusion. Recommendationsforfutureintelligent transportation systems are also discussed.

Dyslexia is a neurodevelopmental learning disorder that affects reading fluency, spelling accuracy, and written expression, often resulting in academic difficulties and reduced self-confidence. Conventional assistive tools provide limited support by addressing isolated learning challenges. This paper presents WordWhiz, an AI-powered assistive system designed to enhance reading comprehension and writing accuracy for individuals with dyslexia. The proposed system integrates text-to-speech with word highlighting, speech-to- text with grammar correction, phonetic spelling assistance, and transformer-based sentence simplification within a unified framework. The system is implemented as a device-based application to ensure low latency, data privacy, and offline usability. Experimental evaluation demonstrates improved text readability, reduced grammatical errors, and enhanced user engagement, validating the effectiveness of AI-driven assistive technologies in inclusive education

Space Vector Pulse Width Modulation (SVPWM) is a high-performance modulation strategy for voltage source inverters, offering improved DC link voltage utilization and lower harmonic content compared to traditional PWM techniques. This work presents the design and FPGA-based realization of an SVPWM algorithm implemented on a Basys-3 development board using a Xilinx Artix-7 FPGA and Verilog hardware description language. The proposed architecture exploits the parallel processing capability and deterministic execution of FPGA hardware to perform essential SVPWM operations, including Clarke transformation, sector determination, dwell time computation, and real-time pulse generation. Three-phase switching signals are generated using sector-dependent switching sequences covering all six space vector regions. The correctness of the generated PWM-A, PWM-B, and PWM-C signals is verified through simulation and validated experimentally using on-board LEDs for visual confirmation of switching transitions. Hardware results demonstrate accurate six-sector space vector synthesis, low latency, and reliable real-time operation. The proposed FPGA-based SVPWM implementation is therefore well suited for highperformance inverter applications such as motor drives and renewable energy systems

As robotics continues to advance across industries, autonomous systems are increasingly being adopted for safety, surveillance, and human interaction. Urban environments, which are often large, complex, and difficult to monitor, still rely heavily on static cameras, manual patrols, and limited human oversight for security. These traditional methods become inadequate during peak periods such as night shifts and holidays, and they lack real-time updates, mobility, and multi-threat adaptability. This project proposes an autonomous patrolling bike designed to function as a mobile guard and digital sentry, capable of independently navigating predefined paths, avoiding obstacles, and providing real-time alerts and threat information. The robot integrates multimodal interaction through cloud connectivity, AI vision, and sensor fusion, supported by IoT infrastructure to access and update information dynamically. By combining mobility with intelligent communication, the system aims to enhance site safety, reduce risk for human guards, and demonstrate the effective role of robotics in creating smarter and more secure urban environments.

Chemical contaminants in sea water, especially heavy metals and organic pollutants, pose significant threats to marine ecosystems and human health. This study investigates the concentrations of heavy metals (e.g., mercury, lead, cadmium) and organic pollutants (e.g., polycyclic aromatic hydrocarbons, pesticides) in sea water samples from coastal areas. The study analyzes the source, distribution, and environmental impact of these pollutants in three different regions: industrial, agricultural, and pristine coastal areas. Using advanced analytical techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Gas Chromatography-Mass Spectrometry (GC-MS), the concentrations of heavy metals and organic pollutants were measured. Results indicate that anthropogenic activities, particularly industrial discharge and agricultural runoff, significantly elevate pollutant levels. These pollutants not only affect marine biodiversity but also pose risks to human health through the food chain. The paper concludes with recommendations for mitigation strategies to reduce pollution and preserve marine ecosystems