Volume 13 | Issue 9 |

 Abstract

The present study was conducted to investigate the profile characteristics of Soil hea;th card holders Khandwa (M.P.). One hundred twenty SHC Holders from Khandwa and Punasa were selected for the present study. The following conclusion has been drown from the finding of the study. Most the respondents who fall under the category of middle age group, educated Intermediate, medium level of farming experience, medium size of land holdings, Agri+ Animal Husbanry occupaied, medium level of annual income, medium level of social participation , medium level extension contact, mass media exposure, scientific orientation and innovativeness. The secondary data were obtained from various government offices, soil test laboratory, Krishi Vigyan Kendra and available publications. Data was analyzed with the help of suitable statistical tools. Analyzed data was tabulated and presented in the form of tables and charts. Percentage were used in the study .

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 Keywords

SHC, Socio-Economic, Respondents

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Titanium dioxide (TiO?) has emerged as one of the most promising photocatalysts for industrial wastewater treatment due to its exceptional properties including chemical stability, non-toxicity, and high photocatalytic activity. This review comprehensively examines the various synthesis methods for TiO? catalysts, their characterization techniques, and applications in removing diverse industrial pollutants. The paper discusses recent advances in TiO? modification strategies to enhance visible light activity and addresses the challenges and future perspectives in scaling up photocatalytic systems for industrial applications. Recent developments focus on overcoming TiO?'s inherent limitations such as low efficiency under visible light and rapid recombination of electron-hole pairs.

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 Keywords

TiO? photocatalyst, industrial wastewater, synthesis methods, photocatalytic degradation, environmental remediation

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The rapid growth of online financial transactions has increased the prevalence of fraudulent activities, posing significant challenges for banking and financial institutions. Traditional fraud detection systems often rely on static rule-based models that fail to adapt to evolving fraud patterns. This paper presents an advanced credit card fraud detection framework integrating state-of-the-art machine learning and deep learning techniques for improved accuracy and robustness. The proposed approach utilizes the publicly available Credit Card Fraud Dataset, which contains real-world anonymized transaction data with highly imbalanced class distribution. Multiple algorithms, including Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were implemented and evaluated. Various data preprocessing strategies were applied, including feature scaling, data balancing using SMOTE (Synthetic Minority Oversampling Technique), and outlier removal. The comparative analysis reveals that ensemble-based methods, particularly XGBoost, achieved superior classification accuracy and F1-scores while maintaining low false positive rates. Furthermore, the study emphasizes the importance of handling data imbalance, hyperparameter optimization, and evaluation metrics tailored to fraud detection. The proposed framework offers a scalable, adaptive, and efficient solution for real-time fraud prevention in financial systems.

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 Keywords

Credit Card Fraud Detection, Machine Learning, Deep Learning, Random Forest, XGBoost, Support Vector Machine, Artificial Neural Network, SMOTE, Class Imbalance, Financial Transaction Security.

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Anger, one of the seven deadly sins in the Bible, is a recurrent motif in the myths of other cultures as well. Indian epics are rife with examples of overwhelming rage. This paper analyses the psychology and actions of Amba, a relatively lesser-known character in the Mahabharata, whose anger at her mistreatment is so intense that she chooses to carry it to her next birth. Amba voluntarily undergoes severe penances and then immolates herself to hasten her rebirth to seek revenge that will assuage her unappeasable wrath. The root of Amba's self-destructive fury deserves a closer study, which has been attempted in this paper.

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Rage, revenge, Amba, Bhishma, The Mahabharata, svayamvar, marriage, gender

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ABSTRACT: The Integration of Artificial Intelligence (AI) into Knowledge Management (KM) systems is reshaping how organizations manage intellectual capital, enhance decision-making, and foster innovation. This paper aims to present the results of a systematic literature review (SLR), based on 745 peer-reviewed articles retrieved from ScienceDirect between 2000 and 2024, to investigate the evolving relationship between AI and KM. The review followed a structured methodology, including a predefined search strategy, selection criteria, and data analysis procedures, to ensure a comprehensive and unbiased assessment of current trends. Articles were selected based on their relevance to key AI technologies and their applications in KM processes, including knowledge creation, retrieval, and dissemination. Our analysis revealed that AI significantly enhances KM by automating knowledge processes, improving the conversion of tacit knowledge into explicit knowledge, and fostering collaborative learning and innovation (Williams & Li, 2023). AI-powered KM systems also enable the processing of large volumes of unstructured data, providing real-time, actionable insights that enhance decision-making and organizational agility (Jones et al., 2021). Furthermore, while AI can augment decision-making, there are limitations in how it captures the nuances of human tacit knowledge and cultural context. We also addressed future research directions, such as the development of more transparent and ethical AI frameworks and the need for further empirical studies that examine AI's role in KM across diverse cultural and industry contexts. By offering a comprehensive synthesis of recent literature, this study provides valuable insights into the opportunities and challenges of integrating AI into KM. The paper contributes to the understanding of AI's transformative potential in organizational learning and innovation while acknowledging the limitations and ethical considerations that must be addressed for sustainable AI-KM integration.

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Key words: Artificial Intelligence, Knowledge Management, Sustainable Growth, Augmented Productivity.

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This paper presents the Adaptive Dream Journey Analyzer (ADJA). This advanced AI platform leverages Natural Language Processing (NLP) and adaptive algorithms to interpret and analyze user-described dream experiences with remarkable contextual accuracy. In the modern era, where understanding The subconscious mind is growing in both clinical and personal significance. ADJA distinguishes itself by enabling dynamic, human-like interpretation of highly subjective dream narratives. [1] The system is engineered to comprehend, process, and generate insightful feedback on dream content, facilitating automated analysis for psychological insight, creativity enhancement, or digital self-reflection. ADJA employs sophisticated NLP techniques, including intent recognition (to understand the user's reflection aims), named entity recognition (extracting dates, characters, and settings from dreams), sentiment analysis (capturing the emotional undertone), and advanced contextual modeling, which preserves narrative coherence throughout a session. [2] personalized recommendations, and automating routine tasks. Blending state-of-the-art machine learning with rule-based interpretation patterns, ADJA adapts to unique user profiles and personal symbolism, delivering an evolving and personalized interpretation experience over time. Its applicability spans clinical psychology (assisting therapists with dream content exploration), creative industries (stimulating story generation and art), wellness technology (aiding with self-awareness and nightly mood tracking), and consumer-facing dream journaling tools. [3] (keeping the conversation coherent). The system combines machine learning models and rule-based logic, creating a hybrid approach that improves its adaptability and performance over time This work demonstrates how combining adaptive AI and modern NLP expands our potential to decode dream experiences, making subconscious analysis more accessible, engaging, and actionable than ever before. This project shows how using modern AI and NLP can improve user interaction and engagement through advanced automated dialogue systems.

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 Keywords

Adaptive Dream Journey Analyzer, Artificial Intelligence (AI), Natural Language Processing (NLP), Dream Analysis, Conversational Agents, Adaptive Systems, Machine Learning (ML), Deep Learning, Intent Recognition, Named Entity Recognition (NER), Sentiment Analysis, Emotion Detection, Personalization, Contextual Understanding, Natural Language Understanding (NLU), Natural Language Generation (NLG), Human-Computer Interaction (HCI), Automated Interpretation, Dialogue Management, Psychological Asses

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Sitakund, Rishikund and Rajgir hot spring site is a sulphur containing hot spring site. Bacteriological water analysis is a method of analyzing water to estimate the numbers of bacteria present. It is a micro biological analytical procedure which uses sample of water and form these samples determine the concentration of bacteria. The Biological parameters were analyzed as per WHO, APHA. The bacteriological parameter included SPC (standard plate count), MPN (most probable number). Bacterial contamination of drinking water sources is the most common health risk. The water sample were collected from three selected hot spring site Rishikund, Sitakund&Rajgir during period (October 2014 to June 2016) within and interval of four month. All the results were compared with WHO, APHA, BIS and found that water is potable.

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APHA, BIS, WHO, SPC, MPN

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Unexpected pavement breakdown issues, such as potholes, unevenness, cracks, etc., have been reported by a number of road agencies. This results in a poor understanding of roads and their lifespan. Conversely, rubbers, polymers, and other materials are being added daily. Waste materials such as plastic bottles, polymers, mugs, and used tires can be recycled by grinding them up or mixing them with cementers. They can also be used to cover asphalt and bitumen using any heating method.In this work, we employed crumbed rubber and plastic as binding agents in relation to bitumen and total. We employ bitumen, strength, and total (of various colored sizes) as accessories on bituminous roads. This study includes vivid tests on bitumen penetrate value, stiffness, and softening point in addition to summations like crushing value, impacting value, bruising value, and particular graveness. This paper uses a lot of sequences.Sustainability is a pressing requirement in the construction sector globally, and in order to lessen the impact on the natural environment, the usage of waste materials in road building is being reduced. To establish their suitability for the design, building, and preservation of the pavements, several apparent accessories and technologies have been built into the trace structure.Among them are rubber and polymers. Due to modifications to lifestyle and growing populations, the amount of plastic garbage in external solid waste is increasing. Additionally, the majority of tires, particularly those found on motor vehicles, are made of synthetic rubber. Getting rid of both is a major issue.At the exact same time, the constant rise in automobile traffic highlights the need for greater engineering and road condition. The desired mechanical properties for a certain road mixture can be improved by partially substituting the traditional material with this waste polymer and rubber.

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Plastic, Rubber, Pavement, Bitumen

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Ayurveda, the ancient Indian system of medicine, emphasizes preventive healthcare and the therapeutic use of natural resources. Value-added Ayurvedic products such as Amla sharbat, Amla syrup, Amla candy, Ale pak, Green chilli pickle, Amla supari, and Mixed fruit jam were prepared in the college laboratory by undergraduate students under faculty supervision. The study followed a hands-on, skill-oriented methodology including selection of raw materials, extraction, cooking, preservation, drying, packaging, and storage. Organoleptic and stability evaluations revealed that the products were acceptable in quality, with Amla-based preparations retaining significant nutritional value, especially Vitamin C.??The initiative not only demonstrated the medicinal and nutritional potential of traditional Ayurvedic products but also served as a practical educational model in alignment with NEP 2020. Students developed skills in product formulation, quality assessment, hygiene, and entrepreneurship while integrating with botany knowledge. This approach highlights the importance of experiential learning in higher education, promoting self-reliance, entrepreneurship, and preservation of traditional knowledge.?

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Ayurveda, Amla, Value-added products, NEP 2020, Experiential learning, Skill development

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Unmanned Aerial Vehicles (UAVs) have gained significant importance in defence, disaster management, and security applications due to their versatility and ability to operate in hazardous environments. This research presents the design and development of a multifunctional surveillance drone integrated with landmine detection capabilities and a robotic arm for remote handling tasks. The system is intended to enhance safety for military personnel by reducing direct human exposure to life-threatening conditions. This project is about a special drone that can move both in the air and on the ground as it work like rover. The proposed drone is built on a quadcopter platform equipped with gear motor and wheels to work like rover, a high-resolution camera for real-time aerial surveillance. For landmine detection, the system uses a metal detection module, enabling the identification of explosive threats from a safe distance. A robotic arm mounted on the drone provides additional functionality, allowing for object manipulation such as picking up suspicious materials, collecting samples. The drone is remotely operated through a wireless communication link, with an onboard flight controller managing navigation. The project demonstrates a hybrid approach that combines aerial mobility with ground-focused sensing, offering a cost-effective and scalable solution for security forces. Field testing showed promising results in detecting metallic landmine simulants, maintaining flight stability during arm operation, and providing continuous live feed for surveillance. This work contributes to the growing field of UAV-based defence applications by proposing a multipurpose platform that improves operational safety, and supports in reducing demining human efforts. Future developments will focus on improving detection accuracy and extending flight endurance.

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 Keywords

Aerial Surveillance, Ground Surveillance, Operational safety, Landmine detection, Material manipulation, GPS Tracking, Wireless Telemetry

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