Volume 14 | Issue 5

 Abstract

We can observe on present days the online reviews are significantly influencing the consumer behavior and purchasing decisions. This study used to exploring the role of Google Maps reviews in shaping consumer perceptions and purchase intentions, which is particularly in the East Godavari region of Andhra Pradesh. As we know on these days the businesses are increasingly rely on digital marketing strategies, mostly Google Maps has emerged as a vital tool for the enhancing of local visibility and trust. This study is highlighting that how the review attributes such as volume, polarity, and credibility are directly impact consumer trust and engagement. Most of the negative reviews, especially, to attract more attention and influence on customer judgments, which is notably among female consumers. Furthermore, the companies who are actively manage their Google Maps profiles, by responding to the reviews of customers and updating the business details, they tend to experience the improved customer interactions and also local search visibility. The research mostly underscores that Google Maps reviews act as the electronic word-of-mouth (e-WOM), which affecting the both brand image and competitive positioning. For this research collected survey data from various consumer demographics, the study is demonstrates that the businesses with higher ratings and favorable reviews will attract more customers. Thus, the Google Maps reviews are not just seeing as the feedback tools but it can be the powerful digital marketing assets to driving the consumers to take decision-making.

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Google Maps, Consumer Engagement, Trust in Reviews, Online Reputation, Digital Marketing.

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"A STUDY TO ASSESS THE EFFECTIVENESS OF VIDEO-ASSISTED TEACHING METHOD ON KNOWLEDGE REGARDING ROAD SAFETY MEASURES AMONG SCHOOL-GOING CHILDREN IN SELECTED SCHOOLS OF VIDARBHA REGION."

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Teenage years are a crucial period of development that are characterized by fast changes in biological, emotional, cognitive, and social aspects of a person's life. During this period of transition, teenagers are confronted with a variety of stressors that include academic demands, peer interactions, familial expectations, exposure to social media, and uncertainty regarding their future careers. An unhealthy amount of stress that is not properly controlled can have a detrimental impact on a person's mental and physical health, as well as their academic performance and personality development. Using a psychological point of view, the current study paper investigates the notion of stress among adolescents, focusing on its causes, the effects it has, and the methods that can be used to manage it. The article provides an explanation of how stress arises and how it impacts the functioning of adolescents by drawing on significant psychological theories such as Hans Selye's General Adaptation Syndrome, Lazarus and Folkman's Transactional Model of Stress and Coping, and the Bio-Psycho-Social Model. Significant factors that contribute to stress among adolescents are also discussed in the essay. These factors include academic pressure, peer influence, family problems, emotional instability, and exposure to digital media. In addition to this, it investigates the psychological, emotional, cognitive, behavioral, and social repercussions that result from stress that is not properly controlled. The latter part of the article is devoted to the presentation of evidence-based stress management treatments. These strategies include problem-focused coping, emotion-focused coping, mindfulness, counseling, social support, developing resilience, and school-based interventions. Furthermore, the study highlights the critical need for comprehensive mental-health support systems in schools and families in order to promote healthy adolescent growth and emotional resilience. Stress, adolescents, psychological perspective, coping strategies, academic stress, mental health, stress management, and emotional well-being are some of the keywords that can be used to describe this topic.

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Adolescents, Stress, Psychological Perspective, Coping Strategies, Academic Stress, Mental Health, Stress Management, Emotional Well-being.

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An adverse effect of changing titanium dioxide nanoparticle shapes and concentrations on the amount of friction and Nusselt number ratio of improvement for thermal solar collectors (TSCs) has been examined. Several nanoparticle shapes (PD = 20-50 nm) and varying concentrations of TiO2 nanoparticles (NC = 1-4%) have been used for preparation of nanofluid via stirrer mechanism. Both pure water and water/TiO2 flow under diverse Reynolds numbers, spanning from 5,000 to 30,000. The permitted amount Nusselt number is found in the water/TiO2 mixture with nanoparticle concentration (NC = 4%) and nanoparticle diameter (PD = 20 nm). With water/TiO2 nanofluid flow at Re = 5,000 and 15% flow at Re = 30,000, the average Nusselt numbers climb by 23% and 13%, respectively. It has been observed that for both fluids, the skin friction factor diminishes as the Reynolds number rises. The skin friction factor of a water/TiO2 nanofluid is higher than that of pure water. Employing water/TiO2 nanofluid as the working fluid and adjusting the concentration of TiO2 nanoparticles from (NC = 1%) to (NC = 4%), the average Nusselt numbers improved by 29% at the lowest Reynolds number and by 39% at the highest Reynolds number of 30,000. Across all Reynolds principles, the Nusselt number increased as the nanoparticle diameter shrank. The maximum Nusselt number is synthesized by TiO2 nanoparticles with the smallest dimension (PD = 20 nm). The most important ratio of improvement for the Nusselt number in TSC is offered by the smallest size TiO2 nanoparticles (PD = 20 nm). This investigation showed that infusing 5% Al2O3 nanoparticles to water/TiO2 (diameter of 20 nm) leads to significant heat transfer development, which raises TSC's thermal performance.

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Solar Collector; Nanoparticle; Nanofluids; Parameters; Performance.

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HIND-PRASHANT KSHETRA ME SANSKRITIK KUTNEETI OR SOFT POWER KA YOGDAN

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HIND-PRASHANT KSHETRA ME SANSKRITIK KUTNEETI OR SOFT POWER KA YOGDAN

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The Influence Of Family, Social And Economic Factors On Professional "Choice Of Teaching Career

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The Influence Of Family, Social And Economic Factors On Professional "Choice Of Teaching Career

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Information literacy is the ability to find, understand, evaluate, and use information properly. In engineering education, students must work with many types of information such as textbooks, research papers, technical reports, standards, and online resources. Simply having access to information is not enough. Students need skills to select correct and reliable information and use it in an ethical way. This article explains the meaning of information literacy, its importance for engineering students, common challenges faced by students, and methods to improve these skills. It also discusses the role of libraries and teachers in helping students develop strong information literacy abilities. Developing these skills helps engineering students achieve academic success and prepares them for professional careers.

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Information Literacy, Engineering Students, Academic Success, Research Skills, Digital Learning

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A Comparative Study of the Attitude of Teacher trainee of Central and State Universities Towards Inclusive Education

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A Comparative Study of the Attitude of Teacher trainee of Central and State Universities Towards Inclusive Education

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Managing heart health often requires a strategic combination of medications, and Nebivolol hydrochloride and Ramipril are two of the most effective tools in that kit. Nebivolol works as a selective beta-blocker, essentially helping the heart beat more calmly and efficiently to lower blood pressure. Ramipril, on the other hand, is an ACE inhibitor that does double duty: it treats high blood pressure and heart failure while providing a vital layer of protection for the kidneys, particularly for those living with diabetes. Research suggests that finding the "sweet spot" for a patient's dosage can significantly reduce the risk of major cardiovascular events and slow the progression of kidney disease. Because these medications are so frequently used together, scientists rely on advanced analytical techniques like UV spectrometry, HPLC, and HPTLC to ensure each dose is precise and pure. These methods allow researchers to detect and measure both drugs simultaneously, even when they are mixed with other substances. By refining these detection methods, the medical community ensures that these life-saving treatments remain consistent, safe, and effective for the patients who depend on them.

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Nebivolol hydrochloride, Ramipril, UV spectrometric, HPLC, HPTLC

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Mental health disorders such as depression, anxiety, and stress are increasing rapidly due to modern lifestyle changes, academic pressure, and workplace stress. Early identification of mental health conditions is essential for proper treatment and support. This paper presents a Mental Health Prediction System using Machine Learning techniques to predict mental health conditions effectively. The proposed system uses preprocessing techniques, feature extraction, and classification algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine for prediction. The experimental analysis shows that Random Forest achieved the highest prediction accuracy. The developed system can help healthcare professionals and institutions in early mental health assessment.

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- Mental Health, Machine Learning, Depression Prediction, Anxiety Detection, Artificial Intelligence

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????????? ???????? ?????? ?????? ??? ?? ???? ?????? ????????? ??? ?? ?? ?? ??? ??? ???? ??, ?? ???? ?? ??????? ?? ???? ?? ??? ?? ?? ????????? ??????? ?? ?????? ?? ???? ??? ??? ??? ??? ?? ??? ???? ????????? ???????? ?? ??????? ?? ??????? ?? ??? ????? ??, ??? ?? ???? ???? ?? ?? ??????? ?? ???? ??, ?????? ?? ?????? ?? ???????? ??? ?????? ?????? ??? ??? ????? ????????? ???????? ?? ?????? ???????? --?????????? ?? ???????? ?? ??????? ????? ??? ?????? ?? ???? ????? ?? ??????? ???? ?? ?????? ??-- ?? ????? ?? ?? ??, ?????? ?? ???????? ?? ????? ?? ?? ?????? ???? ?? ????????? ??????? ??? ??? ?? ??? ??? ????? ??? ???? ?? ???? ?????, ????? ?? ??????-????? ???? ????? ???? ???????? ????????? ??????? ?? ?? ??? ?? ????? ???? ??, ?? ?? ??? ?? ?????? ??? ?????? ??? ?? ????? ?? ????? ??? ????????, ???????????, ??? ?? ???? ???? ?? ???? ?? ?? ????????????? ????? ???????, ???? ???????? ?? ????? ???????? ???? ???????? (???????) ?? ?????? ???? ???? ?? ?????? ????? ?? ???????? ????, ?????? ??? ????? ?? ???????? ?? ?????? ??????? ?? ?????? ????? ??? ?????? ?????? ?? ???, ??? ???????? ?? ????? ?????? ????? ?? ??? ??? ????????? ??????? ???????, ????? ??????? ?? ?????? ??????? ?? ????? ?? ??? ???? ??? ???????? ????? ??? ?? ??????? ??????? ?????? ???, ????? ???????? ???????? ???????? ?? ???????? ?? ?????? ???? ??? ????? ??--???? ??????????, ???????? ?? ????? ?????????? ????????? ???? ?? ?????? ??? ?? ???? ??? ?????, ?? ???? ??? ??????????, ?????? ?? ???? ?????? ??????? ?????? ?? ?????? ???? ??? ??, ???? ????????? ???????? ?? ???? ?? ????? ????????? ?? ????? ??????? ?? ????? ?????????? ?? ?????? ????? ?? ??? ?

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????????? ????????, ??? ?????, ??????, ??????? ????? ?

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This project presents an automated waste segregation system that integrates Arduino technology with machine learning for classifying waste into three categories: metal, plastic, and glass. The system uses inductive sensors, ultrasonic sensors, and a camera module for detection and classification. Metallic objects are identified using the inductive sensor, while non-metallic items are analyzed through image processing and classified using a trained machine learning model. The Arduino microcontroller controls servo motors to sort the items into the appropriate bins. This combination of automation and artificial intelligence improves waste segregation accuracy, reduces manual labor, and promotes sustainable recycling practices.

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o To develop an automated system for segregating waste into metal, plastic, and glass categories. o To integrate sensors (inductive, IR, vibration/piezo) with an Arduino microcontroller for material detection. o To use machine learning algorithms to improve the accuracy of waste classification

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The present study was undertaken to examine the effect of phenthoate (50% EC), an organophosphate pesticide, on the respiratory metabolism of the freshwater fish Ctenopharyngodon idella. Oxygen consumption was used as an indicator to evaluate the physiological stress caused by sublethal and lethal exposure over a period of 24 hours. At the beginning of the experiment (0 h), no noticeable variation in oxygen consumption was observed between the control and treated groups. In the sublethal exposure group, oxygen consumption showed an initial increase and reached a peak at 4 h (17.92%), followed by a gradual decline during the later stages of exposure. In the lethal exposure group, oxygen consumption decreased continuously throughout the experimental period, with the maximum reduction observed at 22 h (37.98%). The decline in oxygen consumption suggests respiratory stress and disturbance in normal metabolic activity due to phenthoate exposure. The findings of the present study indicate that phenthoate can significantly affect the respiratory physiology of Ctenopharyngodon idella, particularly under lethal exposure conditions. The study also suggests that changes in oxygen consumption may serve as a useful biomarker for assessing pesticide-induced stress in aquatic organisms.

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Phenthoate (50% EC), Ctenopharyngodon idella, oxygen consumption, respiratory metabolism, organophosphate pesticide, aquatic toxicology

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Early problem identification is highly important as it can help guarantee effective process functioning, reduce expenses and support decision-making in areas like medicine, cyber security, banking, farming, IoT technologies and many others. The previous methods involved developing rules manually, determining thresholds and using elementary statistical methods that turned out to be insufficient to work with huge volumes of complex data. Artificial intelligence transformed this area with deep learning gaining unprecedented popularity and productivity. The computer algorithms are now able to analyse raw data without any human intervention and identify some patterns and anomalies that cannot be detected by humans. Also, the models based on deep learning can generate an extra layer of analysing numerous fragments of information allowing detecting unusual behaviour much faster than with traditional methods. It appears there are plenty of terms that should be familiar to you including CNNs, RNNs, ANNs and LSTMs, and many more models used to predict events, detect anomalies and patterns, and perform feature analysis in real life. This research paper will examine the use of artificial intelligence in identifying problems at their early stages paying attention to innovations in deep learning algorithms in particular.

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AI Based Early Detection Using Deep Learning

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The ability to notice the initial signs of potential problems can play a key role, as it can ensure effective operation of the process itself, minimize costs and improve the decision making process within the sphere of healthcare, cyberspace, finance, agriculture, IoT devices and much more. In previous approaches the rules needed to be created manually, threshold value to be set up and basic statistical methods were utilized which proved ineffective for large amounts of complex data. The emergence of artificial intelligence revolutionized the industry bringing the idea of deep learning to the new level of popularity and efficiency. Today's computer-based algorithms are capable of analyzing vast amounts of data independently, revealing patterns and anomalies invisible to humans. Moreover, due to the deep learning models the data can be additionally analyzed with the aim of quicker detection of abnormalities. There are numerous concepts that one must be aware of such as CNN, RNN, ANN and LSTMs, not mentioning other deep learning-based models utilized to forecast events, detect problems and analyze features in real-life situations. This research paper aims to explore how artificial intelligence helps detect issues during the very early stages focusing on innovative deep learning techniques. In this paper, there are several parts that discuss neural networks that have been employed by deep learning techniques. The math processes that help train neural networks, the structure and activation function of neural networks, along with ways of improving training processes in terms of efficiency will be examined. In addition, there are some popular data sets used by researchers, software packages and their advantages and disadvantages, along with the issues related to deep learning technology today. Moreover, feature learning via deep learning can bring about better prediction results, while large data management becomes easy with artificial intelligence.

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AI Based Early Detection Using Deep Learning

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The development of digital infrastructures has led to computer networks becoming an integral part of modern societies. Digital infrastructures serve many roles in organizations, including facilitating communication, conducting monetary transactions, storing vital data, and managing businesses. With the wide use of digital infrastructures, the probability of cyber-attacks targeting confidentiality, integrity, and availability of information systems has risen. Traditional security applications primarily use static rules and manually developed attack signatures to detect cyber-attacks. This method does not easily adapt to emerging attacks on computer networks. Hackers are always changing their tactics to avoid detection, hence rendering traditional detection methods ineffective. The growth in both volume and complexity of data in computer networks necessitates the need for automated systems to detect malicious activities. Machine learning offers a smart approach by detecting data patterns and unusual behaviour in data streams. Machine learning models undergo constant learning and enhance their ability to detect malicious activities through continuous training. This research will focus on the application of machine learning models in detecting cyber-attacks and malicious activities.

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Intelligent Cyber Attack identification using Machine Learning Techniques

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The rapid expansion of digital technologies has significantly increased the dependency of organizations on computer networks and internet-based services. While these technologies improve efficiency and connectivity, they also introduce serious cybersecurity risks. Cyber attacks such as malware, phishing, ransomware, and denial-of-service attacks continue to evolve in complexity, making traditional rule-based security systems less effective. Conventional intrusion detection systems rely heavily on predefined signatures and manual monitoring, which limits their ability to detect new or unknown threats. Machine Learning (ML) has emerged as a promising solution to enhance cybersecurity systems by enabling automated analysis of network data and identification of abnormal behaviour patterns. ML algorithms can learn from historical data and detect suspicious activities that may indicate potential cyber attacks. This research paper explores the use of machine learning techniques for cybersecurity threat detection. It examines different machine learning algorithms, discusses data preprocessing methods, and analyses the effectiveness of ML models in detecting malicious activities within network traffic. The study highlights the advantages, challenges, and practical applications of machine learning in cybersecurity systems. The proposed approach aims to improve detection accuracy, reduce false alarms, and strengthen the overall security infrastructure of modern digital environments.

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Cybersecurity Threat Detection using Machine Learning

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Pterygium is an ocular surface disease requiring accurate diagnosis and severity assessment for effective clinical decision-making; however, existing methods often lack detailed analysis and interpretability. This paper presents PteriGrade-Net, an explainable multimodal deep learning framework designed for automated pterygium detection and ordinal severity grading. The model integrates anterior-segment image processing, clinical features, and quantitative biomarkers. It employs advanced preprocessing followed by an Attention U-Net for lesion segmentation and biomarker extraction. These features are dynamically fused with visual representations from EfficientNet-B0 and structured clinical data using attention mechanisms to generate a unified embedding. A multi-task learning strategy optimizes three objectives: (1) binary classification (healthy vs. pterygium), (2) lesion segmentation, and (3) ordinal severity grading. Additionally, the framework enhances explainability by highlighting lesion regions and quantifying morphological characteristics, thereby improving clinical interpretability. Experimental results demonstrate superior performance compared to existing approaches in both detection and severity grading. By combining multimodal inputs with lesion-aware analysis, the proposed system aligns well with clinical workflows and offers a reliable, interpretable solution for real-world ophthalmic applications.

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Pterygium Detection; Multimodal Deep Learning; Lesion Segmentation; Ordinal Severity Grading; Explainable AI

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Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of reliable and early diagnostic systems. This paper proposes a hybrid machine learning framework designed to enhance the accuracy and robustness of heart disease risk prediction. The framework integrates multiple machines learning techniques, combining the strengths of both traditional classifiers and advanced ensemble methods to improve predictive performance. Initially, data preprocessing techniques such as normalization, missing value imputation, and feature selection are employed to ensure data quality and relevance. Subsequently, a hybrid model is constructed by integrating algorithms such as Decision Trees, Support Vector Machines, and Gradient Boosting, leveraging their complementary capabilities for improved classification. The system also incorporates feature importance analysis to identify key clinical indicators contributing to heart disease risk. Experimental evaluation on benchmark healthcare datasets demonstrates that the proposed hybrid approach outperforms individual models in terms of accuracy, precision, recall, and F1-score. The results highlight the potential of hybrid machine learning techniques in providing early, accurate, and interpretable predictions, thereby supporting clinicians in effective decision-making and preventive healthcare strategies.

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Heart Disease Prediction, Hybrid Machine Learning, Ensemble Learning, Feature Selection, Clinical Decision Support, Healthcare Analytics, Predictive Modeling, Early Diagnosis

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Women's empowerment is essential for achieving sustainable development, especially in rural areas where socio-economic challenges restrict women's access to education, healthcare, and financial independence. The Integrated Child Development Services (ICDS), launched in 1975, has been a key initiative in tackling these issues by providing vital health, nutrition, and education services to women and children. This study explores the role of ICDS in empowering women in the Bisra Block of Sundargarh District, Odisha, by evaluating its impact on health, education, economic participation, and social awareness. Employing a mixed-methods approach, the research combines primary data from surveys, interviews, and focus group discussions with ICDS beneficiaries and stakeholders, alongside secondary data from government reports and academic studies. The findings show significant improvements in maternal and child health, higher institutional delivery rates, enhanced nutritional awareness, and increased financial independence through Self-Help Groups (SHGs). However, challenges such as inadequate infrastructure, a shortage of trained personnel, and socio-cultural barriers remain, limiting the full potential of ICDS programs. Despite these obstacles, the study emphasizes the positive correlation between ICDS interventions and women's empowerment, highlighting the need for stronger policy implementation, increased community participation, and improvements in infrastructure. By addressing these challenges, ICDS can further help reduce gender disparities and promote inclusive socio-economic development. The study concludes that a collaborative effort among government agencies, community organizations, and local stakeholders is crucial for ensuring the long-term success of women's empowerment initiatives in rural India.

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Economic Independence, Health and Nutrition, ICDS, Women Empowerment.

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