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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 Keywords

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

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 Abstract

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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 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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 Abstract

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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 Keywords

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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 Keywords

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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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 Keywords

Tobacco Leaf Pest, VGG19, Transfer Learning.

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