Volume 11 | Issue 7

 Abstract

We are living 21st century. In today's' social media has become very necessity in our society. Social media is a technology enabled media that facilitates the sharing of ideas, thought and information to others. It also helps to the students to collect different information and learning related materials. At the present-day social media is a very important learning resource of the students. Through the present study an attempt has been made by the investigators to study the "Influence of Social Media on learning and social life style among the B.Ed. students in Jharkhand''. The investigators have used descriptive survey method for the present study. The sample consist 120 B.Ed. students in the district of Ranchi, Jharkhand. The simple random sampling technique has been used for the selection of samples. The investigators have been developed self-made questionnaire for the present study. For the analysis of data percentage and graph have been used by the investigators in the present study. The findings of the study reveal that most of the B.Ed. students are used social media. This study also observed that most of the students are using social media for learning purposes and social media significant influence on B.Ed. student's social life style.

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 Keywords

Social Media, Online Learning, Social life Style, B.Ed. Students.

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In the context of hyper-targeted digital marketing, this research examines scaled personalization, emphasizing data-driven methodologies. Essential methods and tactics the corporation uses to provide highly tailored customer experiences are investigated through an in-depth case study of Amazon. Customer segmentation, predictive analytics, real-time personalization, dynamic content production, cross-channel integration, automation, and AI-powered solutions are all examined in the research. The results demonstrate how these strategies may improve client engagement and loyalty. The ramifications for practitioners include having a customer-centric strategy, spending money on automation and AI technology, and prioritizing privacy and transparency. The study also suggests future research directions, including assessment metrics, cross-cultural personalization, and contextual personalization. The importance of data-driven strategies for highly focused digital marketing and their potential to increase client happiness and loyalty are emphasized throughout this study.

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personalization, hyper-targeted digital marketing, data-driven approaches, customer segmentation, predictive analytics, customer engagement.

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In this paper, concerns with power quality resulting from the grid integration of a distributed generating system with several power generators are investigated. The suggested hybrid sustainable energy system (HSES) takes into account a centralized DC bus design, where the power from the DC bus is conditioned by the grid-connected inverter to feed the linear and nonlinear loads coupled at the point of common coupling (PCC). But the PCC is less at risk of voltage and current harmonic pollution because of the presence of several power electronic converters and nonlinear loads. A traditional LC-based passive filter that is attached after the inverter is also useless in reducing the harmonics produced by such a system. In order to build and manage an active power filter (APF), an effort is made.

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Active power filter, hybrid energy system, power quality improvement, harmonic voltage and current compensation.

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 Abstract

A wireless sensor networks consists of light weight, low power and small size of sensor nodes. The areas of applications of sensor networks are military, healthcare, forest fire detection, etc. In WSN, sensor nodes process the measured data and transmit it to base station through a wireless channel. The Base Station collects data from all the nodes, and analyzes this data to draw conclusion about the activity in the area of interest.

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Securing Data in Network traffic Using Pattern Matching

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For driver state classification, the suggested system employed the k-NN approach. It has not before been explored in the context of a camera-based driver sleepiness detection employing blink features, to the best of our knowledge. Steering behavior, EEG measurements, and facial traits are examples of existing k-NN-based techniques. The research looks into the viability of a drowsiness classification system based on blink features collected using an EOG. The author attained a promising classification accuracy, demonstrating the utility of a k-NN classifier combined with blink-based features. When a high-dimensional feature space is available, the k-NN model requires a set of acceptable features as a basis for classification. The accessible data becomes scarce as the number of alternative configurations increases, according to the "curse of dimensionality" phenomenon. grows. As a result, one goal of this research is to discover an appropriate set of significant traits. Wrapper approaches are the most commonly utilized feature selection strategies in this work. Wrapper approaches choose feature subsets based on their predictive value during the classification phase. As a result, because it directly evaluates classification performance, this method can take into account dependencies between the feature subset and the classifier.

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Predicting Driver Drowsiness Using KNN Algorithm

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This paper is aimed toward vitality vanquish in routing that correlate the AODV with altered AODV. Our own shelves counseled a haul honest trounce protocol for migrant advert hoc networks. The tenet of the manner used hither selects a routing rail via bloating the heft a few of the expedient paths. There are 3 specs in mod-AODV which might be warn to forecast the substance of the viable route: the cumulative coalition echelon period, the itinerary electricity, and the hop depend. Itinerary choice is primarily based on the heft fee of each viable course. In a viable route, the better the heft cost, the higher is its suitability for site visitors distribution. Clone consequences pageant that the proposed AODV outrun AODV in particular in an state of affairs with slight mobility.

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Energy Dynamic Routing, AODV, M-AODV(Altered AODV)

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CNN algorithms built on deep learning were used to divide satellite images into three categories. Not only can this study categorize satellite photos, but it can also categorize three distinct classes and pinpoint the characteristics of those other classes, like cats and dogs. It is also simple because these other classifications have some standout features that make them simple to differentiate, making classification simple. The main issue with satellite photography is that different satellite images may have different characteristics, which makes satellite image classification challenging. Another issue is that the majority of safelight images include noise contamination. The wireless image's noise patterns are estimated using the CNN model.

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SATELLITE IMAGE CLASSIFICATION USING CNN

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There has been irreparable harm caused by the fashion industry, one of the world's biggest and most important industries and is well known for its adverse environmental effects. According to UN Environment estimates, the fashion sector is responsible for 10% of carbon emissions and 20% of the world's wastewater. The industry is known for its quick speed and ongoing search for new and inventive designs, which has led to a significant increase in synthetic materials, hazardous colors, and energy-intensive production methods, all of which hurt the environment. Thus, there is an increasing need to encourage sustainable growth in the fashion business and lessen its adverse environmental effects. In the fashion sector, sustainability is assessed using. The triple bottom line strategy, which takes into account social, environmental, and economic factors. It entails reducing the adverse environmental effects and advancing moral and ethical principles in developing, manufacturing, and disposing of clothes and accessories. The contribution of intellectual property rights (IPR) to the advancement of sustainability in the fashion industry has recently received more attention. Intellectual property rights are essential in the fashion business for protecting designers' and firms' distinctive designs, logos, and trademarks. By doing this, these designers and businesses may protect the exclusivity of their goods and stop illegal copying and inappropriate usage of their designs.

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Intellectual Property Rights, Upcycling and Recycling Fashion, Sustainable Fashion, Fashion Industry.

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Deep learning is one of the most unexpected machine learning techniques which is being used in many applications like image classification, image analysis, clinical archives and object recognition. With an extensive utilization of digital images as information in the hospitals, the archives of medical images are growing exponentially. Digital images play a vigorous role in predicting the patient disease intensity and there are vast applications of medical images in diagnosis and investigation. Due to recent developments in imaging technology, classifying medical images in an automatic way is an open research problem for researchers of computer vision. For classifying the medical images according to their relevant classes, a most suitable classifier is most important. Where we are proposing our model where the algorithm is trained for classifying medical images by deep learning technique. A pre-trained deep convolution neural network (GoogleNet) is used that which can classifies the various medical images for various body organs. This method of image classification is beneficial to predict the appropriate class or category of unknown images. The results of the experiment exhibit that our method is best suited to classify various medical images.

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Medical image classification, pre-trained DCNN, convolution neural network, deep learning

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Only after the initial fermenting phase were some of the tobacco leaf insect bites visible. Pest attacks on tobacco leaves cause the quality to decrease. To preserve quality, it is necessary to separate diseased and pest-affected leaves from healthy leaves. Typically, sorting is done by hand, which leaves room for human error. In this work, we attempted to automatically classify the leaves impacted by various pest infestations. One of the most recent classification techniques suggested in this paper makes use of the well-known VGG19 architecture: the convolutional neural network (CNN). If trained with random weight initialization, VGG19 training can take a while. To increase accuracy and shorten training time, we chose beginning weights using transfer learning. Considering the outcomes, We achieved a very good accuracy by training with a single class of the disease using VGG19 and transfer learning. To achieve the best outcomes, some scenarios are examined depending on a combination of the number of learnable parameters and types of the optimizer. As a result, it was established that the suggested architecture can accurately identify all training and validation data.

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Tobacco Leaf Pest, VGG19, Transfer Learning.

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This study suggests a concept called Augmented Education (Augmented) to initially address the issues raised above. This model primarily comprises of the following three modules, as indicated in Fig. 2: Three modules are included: (1) a Data Module that combines data from multiple sources on campus, (2) a Prediction Module that uses machine learning (ML)-based algorithms to predict academic performance, and (3) a Feedback Module that delivers visualized feedback. The Data Module evaluates characteristics and features that can represent students' behavioral change from three different perspectives. In the end, AugmentED is evaluated using data from 156 actual college students. In this work, a model for the proposed system called Augmented Education (AugmentED) is put forth in an effort to initially address the issues raised above. This model primarily comprises of the following three modules of the proposed system:.

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Predicting Performance of Students in Online Classes Using ML

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Abstract: Globally, a sport as an industry has a unifying appeal. The sports industry boasts of a dynamic appeal that many other, even bigger industries look at with envy. It has the power to define nations as a whole. Sports offer a perfect opportunity to explore scientific concepts such as force, motion, potential energy, velocity, and torque. For many students school-organised sports play an important role in their academic and social experiences. Every school has sports embedded in their curriculum as they understand the importance of physical health and activity. However, the importance given to sports ends there as this inclusion is just for the sake of a formality. Whether it be high performance sports at the Olympics or the development of sports locally, sports education is becoming increasingly important in the modern world. Even though there is a growing awareness of including sports education as a compulsory subject in Indian schools, there lie some endemic obstacles which are preventing its growth trajectory. Roselin Kiro of Elets News Network (ENN) finds out the scenario of sports education in schools in India. India For Sports (IFS) was set up with a vision to provide a same level playing field to the Indian athletes when they compete at international events.[3] The organization tries to solve the basic issue of funding for the deserving athletes. It also provides the athletes visibility by covering their stories, hardships, achievements and activities. The organization also connects the athletes to corporate sponsors and other sports aiding organization to provide facilities such as dietitians, nutritionists, psychologists etc. India For Sports was a crowd funding platform for Indian athletes. It was incepted in June, 2014 to help Indian athletes with funding. It is running 6 fund raising campaigns [2] for athletes in individual sports disciplines such as athletics, table tennis and wrestling.

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Key words : Relevance, Sports, Education, Schools, Concepts.

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The main objective of this study is to examine farmers' aperception towards purchasing crop insurance, aiming to help researchers understand how to mitigate or avoid risks faced by farmers through crop insurance subscriptions. The chosen location for this investigation is Erode District in the Western part of Tamil Nadu, as the attitudes and perceptions of farmers in this area are indicative of their behavior towards crop insurance. The researcher analyzed various factors that influence farmers' perceptions and attitudes towards buying crop insurance. For this study, a sample of 426 farmers from Erode District was selected using the convenience sampling method. To establish significant relationships between variables, the researcher utilized Cluster Analysis, Multiple Regression, Factor Analysis, Chi-Square Analysis, and Percentage Analysis. Additionally, Cronbach's alpha was used to assess the questionnaire's reliability.

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Attitude, Crop Insurance, Perception, Farmers' Risk, Natural Calamities, Government, Agro Economy, etc.

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In India by the year 2025, approximately 75% of the population would be employable. For the overall development of the country, we need better skills and opportunities at the same time. The population with proper skills could help the nation to generate revenue, create job opportunities and mitigate poverty. Small-scale industries in India are crucial for the development of the nation as a developed country, despite that this sector is facing a shortage of skilled workforce. Understanding and mitigating the challenge of skilled workforce shortage is crucial for the growth and success of small-scale industries in India. By analysing the factors contributing to the shortage, evaluating its impacts, and exploring potential solutions, this research paper aims to provide valuable insights for policymakers, industry leaders, and stakeholders in addressing this pressing challenge. Ultimately, the findings and recommendations from this study can contribute to fostering a skilled and competitive workforce that supports the sustainable development of small-scale industries in India.

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Skilled workforce, Sustainable development, employment generation, Vocational training, small-scale industry.

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Image dehazing is a critical task in computer vision and image processing that aims to restore clear visibility in hazy or foggy images. In recent years, significant advancements have been made in image dehazing algorithms, leading to improved results and enhanced visual quality. This paper presents a comprehensive review and comparative analysis of state-of-the-art image dehazing techniques. We categorize the existing methods into different groups based on their underlying principles and analyze their strengths and limitations. Additionally, we provide a comparative evaluation of these techniques based on various metrics, including visual quality, computational complexity, and robustness to different hazing conditions. This analysis aims to assist researchers and practitioners in understanding the current landscape of image dehazing methods and identifying avenues for future research.

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Image dehazing, computer vision, image processing, visibility restoration, hazy images, foggy images

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The Buddhist logician Dign?ga known as the father of the medieval Indian logic. He introduced some new concepts on the discussion of logic or inference. Here Pak??bh?sa or the fallacy of thesis, a notable concept of Dign?ga's philosophy, has been discussed after following his great work 'Ny?yaprave?a'.

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Pak??bh?sa, Hetv?bh?sa, D????bh?sa, Aprasiddha-bi?e?ana, Aprasiddha-bi?e?ya, Aprasiddh?v?ya, Prasiddha-sa?vandha.

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Reverse migration, a phenomenon characterised by the return of individuals to their places of origin, has been a growing trend in many parts of the world. Tehri District in Uttarakhand, India, is witnessing an upsurge in reverse migration, which has significant implications for its socio-cultural fabric. This research paper explores the factors contributing to reverse migration in Tehri District and its impact on the local socio-cultural discourse. The study aims to comprehensively understand this dynamic social phenomenon and its potential implications for the region by employing advanced qualitative and quantitative methodologies.

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Reverse migration, Himalayan region, socio-cultural implications, Tehri District

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Applications of creativity and innovative ideas have been taking place in the academic libraries for many years. But, over a period of years, technological changes have influenced the library users at a much faster rate than the academic libraries. This is causing a gap between the libraries and their users. To cope up with this, academic libraries need to strategically tackle the situation. The present study focusses on the role of ambient environment in the implementation of creativity and innovations in academic libraries.

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Creativity, innovation, academic libraries, environment, ambience

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Abstract: Channel's height-width ratio played a significant role on the HT-PEMFC's performance. In the current study, a rectangular parallel flow field design was used to analyze the three different ratios between the channel height-width to observe the various factors that includes velocity field vectors, pressure and water mass fraction in the anode and cathode compartments, membrane current density, the mass fraction of H2, N2 and O2, also polarization curve for HT-PEMFCs. In the first ratio 1:1 where the height and width of gas channel remains the same. By keeping the same channel's inlet-outlet area, the height in the second ratio (4:5) is smaller than the width, while the height in the third ratio (5:4) is larger. Using the COMSOL 6.0 Multiphysics software, we performed a numerical simulation modelling of the HT-PEMFC for this investigation. When channel height to width is kept at (1:1) shows good performance for polarization plot simulation results.

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H: W ratio, PEMFCs, Mass fraction, Ohmic voltage drop, Polarization plot, Velocity.

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Skin diseases pose significant healthcare challenges worldwide, and timely and accurate diagnosis is crucial for effective treatment and management. Teledermatology, which involves the remote diagnosis and consultation of skin conditions using telecommunication technology, has emerged as a promising approach to address the shortage of dermatologists in underserved areas and improve access to dermatological care. In this paper, we propose a novel methodology for the classification of skin diseases in teledermatology using image processing techniques and Support Vector Machines (SVM) as the classification algorithm. The methodology involves data collection from authoritative medical sources, preprocessing to enhance image quality, feature extraction from dermatological images, and model training using SVM.

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Teledermatology, Skin Disease, acne, eczema, psoriasis, classification.

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