Though effective, remote sensing-groundwater potential mapping has limitations in the form of feature selection complexity, atmospheric errors, and the need for high-resolution imagery. Land cover variations, differences in soil makeup, and annual cycles could potentially affect accuracy, and deep models require significant computation for extensive application. Through the use of KOMPSAT-2 data, this paper demonstrates how Tri-Plateau Exponential Stereoscopic Scalable Quantum Cascaded Visual Attention Network (TP-ES-SQCVANet) beats groundwater potential mapping when it comes to enhancing the accuracy of classification and surmounting traditional hurdles. The Exponential Distribution Optimization (EDO) technique is applied for feature selection following preprocessing using the Adaptive Tri-Plateau Limit Tri-Histogram Algorithm (ATP-LTH). Reliable groundwater potential mapping using remote sensing is guaranteed by using the Planet Optimization Algorithm (POA) for optimization following the Stereoscopic Scalable Quantum Cascaded Visual Attention Network (SSQCVANet) for classification. The Python test script assesses groundwater potential mapping with the KOMPSAT-2 dataset. Receiver Operating Characteristic (ROC) technique was utilized to assess the performance of the model. Groundwater potential maps were also produced and compared through an ensemble method known as FR-BCT (Feature Reduction-Based Brightness Contrast Transformation). Test outcomes indicate that TP-ES-SQCVANet performs superior to existing methods, with 99.8% accuracy with FR-BCT and 99.9% accuracy with BCT (Brightness Contrast Transformation). These findings suggest that automatic systems could be more effective than manual systems. By identifying areas of high groundwater potential, the findings of this study can assist in sustainable groundwater resource management.

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This literature review presents a comprehensive analysis of the advancements in Abrasive Jet Machining (AJM) for machining applications over the past decade (2015–2025). AJM has evolved from a coarse material removal method to a precision manufacturing process, driven by innovations in abrasive materials, nozzle designs, jet control strategies, and hybrid processing techniques. This review categorizes and synthesizes findings from peer-reviewed studies published in high-impact journals, focusing on developments in nano-structured and composite abrasives, functionally graded nozzle materials, and advanced carrier media. Processing advancements such as optimized nozzle geometries, pulsed and submerged jet techniques, and AI-driven control frameworks are critically examined. AJM's application spectrum is analyzed across aerospace, biomedical, microfabrication, and electronic domains, emphasizing its unique capabilities for machining hard, brittle, and thermally sensitive materials. Comparative assessments highlight AJM's advantages in material versatility, surface integrity, and sustainability over conventional and thermal-based machining techniques. The review also identifies existing challenges—such as nozzle wear, process standardization, and abrasive waste—and outlines future directions including smart AJM systems, green abrasives, and integration with additive manufacturing post-processing. Collectively, the findings position AJM as a versatile and strategic technology for precision, clean, and adaptive manufacturing.

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New Shillong Township appears to be an ideal location for urban development and expansion due to its undulating landscape, slope, and high land cost. GIS-based multi-criteria evaluation of slope, road proximity, land usage, property valuations, and other features are used to identify potential areas for urban expansion and development in New Shillong Township. ArcGIS 10.8 software assessed six thematic information layers and merged and pan-sharpened spatial data (Cartosat-1 and Kompsat) to choose suitable locations in New Shillong Township. It focuses on the Weighted Linear Combination and Overlay Weighted Average (OWA) Sum, both based on the GIS (WLC). Six criteria—drainage, aspect, cost, slope, road proximity, and land use—were assessed and given weights in this study. Using visual interpretation of satellite data, various thematic information layers were created for each characteristic, showing site suitability on an ordinal scale. The pair-wise comparison matrix method was used to normalize the maps based on various criteria. We have determined the weights of each criterion by comparing their importance to one another. OWA and WLC were used to combine criterion weights and maps. The joint comparison matrix displays the weights for the following: slope (=0.34), road proximity (=0.16), land use (=0.23), land cost (=0.12), drainage (=0.09), and aspect (=0.07). Consistency Ratio (CR =0.0117) of 0.10 suggested that the joint comparisons were reasonably consistent. The final suitability map, which covers an area of 40.8 square kilometers, was created using both weighted sum overlay and spatial analysis tools. Following suitability research, it was discovered that 0.03 sq km of the available area is not suitable. 1.18 square kilometres were classified as low suitable, 3.68 square kilometres as moderately suitable, 12.77 square kilometres as highly suitable, and 25.26 square kilometres as extremely highly suitable. The results revealed that agricultural or forest areas are appropriate for urban growth.

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By 2025, social media has permeated every aspect of daily living, with a complex relationship between online existence and mental health. This article explores the new developments in this interaction, particularly the ways in which online identity formation, algorithms for tailored content, and the growing consciousness of mental health on social media are affecting our overall wellbeing. This study investigates the advantages and disadvantages of social media use for mental health using a combination of surveys, interviews, and content analysis. The findings highlight new trends in online campaigns for psychological well-being and emphasize the importance of striking a healthy balance when using social media. Lastly, the article offers fresh perspectives on how platforms might be created in the future to further assist users' mental health.

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This research investigates the potential of using waste generated from discarded or broken concrete cubes, typically produced during construction quality control tests, as an alternative material in the manufacturing of pavement pavers. The study evaluates mechanical properties, durability, cost-effectiveness, and environmental impacts of using this recycled material compared to conventional aggregates. Results suggest that concrete cube waste can be successfully incorporated into paver production without compromising performance, offering a sustainable solution that supports a circular economy within the construction industry.

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This paper introduces a comprehensive multimodal system designed for accessible computer control, incorporating eye movement tracking, gesture recognition based on sign language, and voice commands. The system aims to provide a hands-free, user-friendly interface that significantly enhances digital accessibility for users with disabilities, such as motor impairments or vision-related limitations, as well as users seeking efficient multitasking solutions. Implemented using Python and associated libraries (OpenCV, TensorFlow, SpeechRecognition), the platform integrates real-time computer vision, machine learning, and speech recognition technologies. The result is a unified interaction model that empowers users to perform cursor movements, clicks, and various computing tasks with precision and convenience. Extensive testing demonstrated accuracy levels above 90% across different modalities. The system promotes digital inclusion and provides a foundation for scalable assistive technology.

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Optisystem 15.0 software is used to analyze all the results of proposed model. Fiber Bragg Grating (FBG) is taken as the main dispersion compensation parameter for the design of our system. The simulation results of Walsh code appears to be more efficient than all. Thus the use of Walsh code leads to a large amount of dispersion compensation by providing high value of Q-factor and it proves to be the optimum input sequence for a high dispersion compensated optical communication system. Optical fibers are free from radio-frequency interference, electromagnetic interference or switching transients that gives electromagnetic pulses because of the reason that they form a dielectric waveguide. Therefore the optical communication system operation remains affected by transmission which is taking place through environment that is electrically noisy and also no shielding form EMI is required for the fiber cable.

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The escalating global demand for sustainable construction materials has spurred interest in incorporating recycled plastic aggregates into concrete to reduce environmental impact and address plastic waste. This study investigates the effects of substituting fine aggregates with recycled polyethylene terephthalate (PET) aggregates at 0%, 10%, and 20% replacement levels on concrete's compressive strength and environmental footprint. An experimental approach, following ASTM C39 standards, evaluates compressive strength at 7 and 28 days under varying curing temperatures (20°C, 30°C), with PET aggregates treated with silica fume to enhance bonding. A simplified life cycle assessment (LCA) quantifies CO2 emissions, focusing on material production and plastic recycling processes. Expected results indicate a 5-20% reduction in compressive strength but a 10-15% decrease in CO2 emissions at 20% replacement, supporting sustainability without compromising non-structural applications. The findings highlight the potential of recycled PET aggregates to balance mechanical performance and environmental benefits, contributing to greener construction practices. Limitations, such as long-term durability, are noted, with recommendations for future research into optimized processing and structural applicability.

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Emotion recognition has garnered significant attention due to its potential applications across various domains such as healthcare, education, and human-computer interaction. This paper presents a multimodal emotion recognition system that combines facial expression analysis and text sentiment analysis to improve the accuracy and effectiveness of emotion detection. Utilizing deep learning techniques, such as Convolutional Neural Networks (CNNs) for facial expression recognition and Natural Language Processing (NLP) for text sentiment analysis, this system can classify emotions in real-time. The paper provides a comprehensive overview of the system's design, the models used, and the challenges faced in the development process, including data integration and real-time processing. Through this work, we demonstrate how multimodal approaches can significantly enhance emotion detection accuracy in a variety of settings.

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The leather industry generates a significant amount of solid waste during the processing of animal hides and skins, including proteinous wastes like chrome shavings and fleshing residues, as well as non-proteinous wastes such as buffing dust. Improper disposal of these wastes poses serious environmental challenges due to their chemical composition, particularly the presence of chromium. This study investigates the feasibility of utilizing these leather industry wastes in the construction of Stone Matrix Asphalt (SMA) pavement layers. In this research, lime obtained from limed fleshing was used as a filler, and chrome shavings were employed as a stabilizing additive in SMA mixtures. The mix designs were prepared in accordance with IRC: SP-79-2008 specifications and tested using the Marshall Method to determine the optimum binder content. Performance evaluation was carried out through various laboratory tests, including Marshall Stability, Indirect Tensile Strength (ITS), Moisture Susceptibility, and Wheel Tracking (Rutting) tests. The results indicated that the modified SMA mixes exhibited enhanced mechanical properties, including improved stability, moisture resistance, and rutting performance, while also meeting all specified standards. Furthermore, the reuse of leather waste materials contributes to sustainable construction practices and offers an environmentally responsible solution for waste management in the leather industry. This study concludes that incorporating leather waste in bituminous mixes is both technically viable and environmentally beneficial, paving the way for eco-friendly and cost-effective road construction solutions.

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The implementation of machine learning techniques to optimize crop selection based on essential soil and climate parameters. The soil attributes analyzed include pH values, nitrogen (S), phosphorus (P), and potassium (K) concentrations, while climate variables include temperature (°C), moisture (%) and precipitation (mm). By using the dataset for harvest recommendations from Kaggle, the study identifies key determinants of plant suitability and evaluates the effectiveness of several machine learning algorithms, such as accidental forests, gradient increase, vector machine (SVM) support, and KNearest Neighbor (KNN). The aim is to improve agricultural productivity through data-controlled recommendations tailored to specific environmental conditions. This study uses advanced feature selection methods to identify the primary variables that have an impact on harvest recommendations and provide valuable insight into the key factors needed for accurate predictions. Various models of the evaluation of machine learning are done using power metrics such as accuracy, recall, and F1 scores to determine the most effective approach. The findings indicate that all models compared to other algorithms exhibit strong prediction skills, with gradients and random forests consistently increasing higher accuracy and reliability. This result provides a valuable perspective for the continued development of precision agriculture and its practical implementation, which promotes a data-controlled approach to optimizing crop selection.

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A Customer Relationship Management (CRM) system is a software application designed to help businesses manage and improve their interactions with both current and potential customers. It serves as a centralized platform where customer information such as contacts details, purchase history, preferences, and communication records are stored and accessed. The CRM system supports various business functions including sales, marketing, and customer service. In sales, it helps track leads, manage opportunities, and monitor the progress of deals through the pipeline. In marketing, it allows for campaign management, customer segmentation, and automation of tasks like sending promotional emails. For customer support, the system enables logging of service tickets, tracking of issues, and ensuring timely follow-ups. By integrating with external tools such as email clients, social media, and enterprise resource planning (ERP) systems, the CRM system offers a holistic view of customer engagement. It also provides analytical tools to generate insights into customer behavior, sales trends, and campaign effectiveness. Overall, a CRM system enhances customer satisfaction, improves team productivity, and supports data-driven decision-making for business growth.

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Macular degeneration, glaucoma, cataracts, and diabetic retinopathy are among the vision-threatening conditions that represent a serious global health concern. Costly and specialized techniques can impede early and correct diagnosis, which is essential for successful therapy, particularly in underprivileged regions. We have created an AI-powered online application that analyzes retinal images using the ResNet50 model in order to close this gap. This platform offers scalable and reasonably priced diagnostic assistance for the early detection of eye diseases, providing thorough reports that empower patients and medical professionals. Its ability to learn continuously guarantees that accuracy will increase over time, providing a potent weapon to fight avoidable blindness and vision impairment globally.

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This research paper is based on the revival and regeneration of the fading identity of tribes their culture, artistry, and tradition through the medium of functionally planned architectural spaces inspired by their traditions and customs. This paper questions whether the presence of well-planned and aesthetically relatable architectural spaces and buildings helps in reviving the art and craftsmanship of Indigenous tribes as it will give a great blow to their financial income growth and will give them a boosting factor to revive their identity leaving an impression of their region and its regional touch of authenticity to the world. According to the paper, the fusion of indigenous customs and beliefs into creating a specific space devoted to its users and spatial requirements creates a creative approach for coming up with design concepts and planning narratives that can be used for projects catering to a community of specific people. Artistry blending into architecture and its structures not only supports and encourages artisans' livelihoods but also revitalizes the region's cultural heritage and beliefs, making it a valuable addition to design practices. The findings are incorporated into a design for a Tribal Training and Resource Centre that would offer intergenerational learning, craft revitalization, and cultural exchange. The proposed design illustrates that even when using traditional materials and a spatial logic developed over generations, it is possible to address contemporary needs and create a welcoming, sustainable and culturally situated built environment.

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This paper presents an intelligent and interpretable recommendation system designed to effectively match students with potential sponsors, fostering a mutually beneficial relationship between emerging talent and organizations seeking innovative contributors. The platform utilizes a hybrid approach combining natural language processing (NLP) techniques and vector space models to analyze and compare student resumes with job or sponsorship descriptions. Core components of the system include text preprocessing using SpaCy—where stopwords, punctuation, and irrelevant content are removed and words are lemmatized to their base form—followed by the transformation of text into TF-IDF vectors to capture the relative importance of terms. Cosine similarity is then used to compute the degree of alignment between resumes and sponsor-defined criteria. Additionally, the system features a rule-based skill extraction module that identifies technical proficiencies from resumes using a curated skill list, and a recommendation engine that matches these extracted skills against predefined job roles through intersection logic. This combined methodology ensures accurate and explainable results, helping organizations identify suitable candidates while empowering students to access meaningful opportunities based on merit. The platform serves as a step forward in democratizing access to sponsorships and enhancing visibility for skilled students across diverse domains.

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In the competitive landscape of modern job markets, interview readiness plays a critical role in determining the success of students transitioning into professional roles. However, many candidates face significant challenges in preparing effectively due to a lack of personalized guidance and relevant practice. Generic preparation resources often fail to address individual strengths, weaknesses, and job- role alignment, leading to uncertainty, low confidence, and underperformance. To address this gap, we propose an AI-powered self-assessment platform designed to assist students in preparing for job interviews through intelligent analysis of their resumes in comparison to job descriptions. Leveraging the capabilities of the LLaMA 8B large language model integrated with GROQ AI, our system rapidly computes a relevance score and generates a set of personalized interview questions tailored to each user's resume and the specified job role. Additional functionalities include chat history retention for tracking previous sessions, enabling continuous learning, and the implementation of natural language processing techniques for deeper analysis. The system also stores user interaction data securely in a MySQL database while offering a seamless experience through a Flask-based backend. This innovative approach not only helps users understand their job fit and likely interview content but also prepares them mentally and strategically by focusing on areas that need improvement. Ultimately, this platform aims to foster a more confident, well-prepared generation of job seekers by offering a smart, efficient, and tailored mock interview experience.

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Millions of visually impaired individuals face daily challenges in navigation, object recognition, and independent living. Despite advancements in assistive technology, existing solutions often lack real-time functionality, affordability, or ease of use. This research presents the development of an AI-powered assistive mobile application designed to enhance accessibility and independence for blind individuals. The application integrates computer vision, natural language processing (NLP), and AI-driven voice assistance to provide real-time object recognition, obstacle detection, and navigation support. The study explores the limitations of current assistive technologies, highlighting the need for an intuitive, costeffective, and AI-enhanced mobile solution. The methodology includes the development of key modules such as real-time object detection, voice-based feedback, and GPS-assisted navigation. The application is developed using TensorFlow, OpenCV, Google Text-to-Speech API, and Android Studio.

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Glucose intolerance initially identified during pregnancy is the primary cause of gestational diabetes mellitus (GDM), a rapidly expanding global health issue associated with severe problems for both mothers and newborns. Current research on the physiological and dietary elements affecting the onset, course, and treatment of GD is summarized in this review. It emphasizes how important low glycemic index meals, weight control, macronutrient balance, and micronutrient adequacy are in regulating maternal glycemia. The molecular significance of physiological factors like insulin resistance and pregnancy-related hormonal changes in the beginning of GD are investigated. Risks for both short- and long-term health include preeclampsia, preterm birth, type 2 diabetes, cardiovascular diseases, and the requirement for customized dietary therapy, and lifestyle changes as needed. Furthermore, focus is placed on how maternal hyperglycemia affects prenatal programming and the offspring's later risk of metabolic disorders. Along with the socioeconomic obstacles preventing appropriate care, the review also examines new research topics such as genetic therapies and continuous glucose monitoring. This review promotes proactive, evidence-based strategies to lessen the burden of GDM and protect maternal and child health across generations through an understanding of dietary and physiological factors.

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This article explores the integration of Wireless Sensor Networks (WSNs) with machine learning algorithms, highlighting their synergistic potential across various real-time applications. WSNs, composed of spatially distributed sensor nodes, are vital in monitoring and collecting environmental or physiological data. However, their resource constraints and dynamic environments necessitate intelligent data processing and adaptive decision-making. Machine learning (ML) addresses these challenges by enabling predictive analytics, anomaly detection, data compression, and energy-efficient routing within WSNs. This paper reviews key ML techniques—such as supervised learning, unsupervised learning, reinforcement learning, and deep learning—and their implementation in WSNs. Applications covered include healthcare monitoring, environmental surveillance, smart agriculture, and industrial IoT. Additionally, the article discusses the limitations and future directions in achieving scalable, secure, and energy-efficient smart sensor networks powered by ML. The integration of WSNs and ML holds significant promise in transforming raw sensor data into actionable insights for intelligent, autonomous systems.

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The proliferation of plastic waste, particularly Polyethylene Terephthalate (PET) bottles, poses a significant environmental challenge. This research paper presents the design, development, and evaluation of a cost-effective and environmentally friendly machine capable of transforming waste PET plastic water bottles into high-quality 3D printing filament. The proposed system integrates a mechanical cutting mechanism, a precisely controlled extrusion unit, and a filament winding system. By repurposing plastic waste into a valuable resource for additive manufacturing, this project aims to promote circular economy principles, reduce reliance on virgin plastics, and offer a sustainable solution for both waste management and 3D printing material accessibility.

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