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Road safety remains a casualty in India, with potholes wrecking asphalt pavements by the dozens. A study in 2017 recorded that potholes caused the budget for road safety to increase by a whopping 100.4 per cent, and even doubled the death toll from that of the year prior. To address this situation, an effective solution is required that ensures the drivers’ safety and can prove beneficial for long term measures. This can be established by employing an apt pothole detection system which is simple yet functional. In this paper, the method for such a system is described which uses accelerometer and gyroscope, both built in the modern day smartphones, to sense potholes. Pothole induced vibrations can be measured on the axis reading, making them distinguishable. Our proposed Neural Network model is trained and evaluated on the data acquired from the sensors and classifies the potholes from the non-potholes. The neural network gives a classification accuracy of 94.78 per cent. It also presents a solid precision-recall trade-off with 0.71 precision and 0.81 recall, considerably high for a problem with class imbalance. The results indicate that the method is suitable for creating an accurate and sensitive supervised model for pothole detection.

A Wireless Body Area Network (WBAN) is considered as a special type of a Wireless Sensor Network (WSN) with its own challenges and requirements. A traditional WSN is designed to achieve higher efficiency, however, WBAN is designed to achieve maximum throughput, minimum delay and to maximize the energy efficiency, all together. Our analysis indicates that available protocols for WSN are not easily portable for WBAN, specifically Medium Access Control (MAC) Protocols. Thus, we have proposed a new WBAN MAC Protocol based on Aloha protocol which differentiates and preferred various types of traffic based on emergency case. The proposed protocol has been simulated using different scenarios and the obtained results are analyzed and compared using NS-2 Simulator.

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In recent years, the World Wide Web (WWW) has established itself as a popular source of information. Using an effective approach to investigate the vast amount of information available on the internet is essential if we are to make the most of the resources available. Visual data cannot be indexed using text-based indexing algorithms because it is significantly larger and more complex than text. Content-Based Image Retrieval, as a result, has gained widespread attention among the scientific community (CBIR). Input into a CBIR system that is dependent on visible features of the user's input image at a low level is difficult for the user to formulate, especially when the system is reliant on visible features at a low level because it is difficult for the user to formulate. In addition, the system does not produce adequate results. To improve task performance, the CBIR system heavily relies on research into effective feature representations and appropriate similarity measures, both of which are currently being conducted. In particular, the semantic chasm that exists between low-level pixels in images and high-level semantics as interpreted by humans has been identified as the root cause of the issue. There are two potentially difficult issues that the e-commerce industry is currently dealing with, and the study at hand addresses them. First, handling manual labeling of products as well as second uploading product photographs to the platform for sale are two issues that merchants must contend with. Consequently, it does not appear in the search results as a result of misclassifications. Moreover, customers who don't know the exact keywords but only have a general idea of what they want to buy may encounter a bottleneck when placing their orders. By allowing buyers to click on a picture of an object and search for related products without having to type anything in, an image-based search algorithm has the potential to unlock the full potential of e-commerce and allow it to reach its full potential. Inspired by the current success of deep learning methods for computer vision applications, we set out to test a cutting-edge deep learning method known as the Convolutional Neural Network (CNN) for investigating feature representations and similarity measures. We were motivated to do so by the current success of deep learning methods for computer vision applications (CV). According to the experimental results presented in this study, a deep machine learning approach can be used to address these issues effectively. In this study, a proposed Deep Fashion Convolution Neural Network (DFCNN) model that takes advantage of transfer learning features is used to classify fashion products and predict their performance. The experimental results for image-based search reveal improved performance for the performance parameters that were evaluated.

In India around in the second week of March 2020, in order to avoid spread of COVID-19 virus, the Central Governments have declared LOCKDOWN in the country. It results in temporary shutdown of schools and colleges. By looking around, at severity of situation due to spread of the novel corona virus, there is no certainty when schools and colleges will reopen. This is a crucial time for the education field, including board tests, kindergarten admissions, entry tests of various universities and competitive exams. It has greater impact on Education field. It has affected continuous teaching-learning process, student counselling operation, assessment methods of teachers, students’ interest towards higher studies in abroad got lower. Information Communication Technology (ICT) may play important role in the lockdown period by providing online learning platforms. Its need in education is discussed in this paper. With the help of power supply, basic digital skills to teachers and students and internet connectivity digital learning can be made possible. Similarly, students those are from low-income groups or presence of disability, etc. for them distance learning program can be included. To mitigate such hazardous situations, ICT-based learning model is essential. In this paper, ICT-based Learner-Centric Blended Education Model is proposed which blends traditional classroom teaching with e-learning where students and teachers both are active, students can interact with teachers; also, teachers can monitor progress of students, etc. After surveying different research papers on Education Models, it has been observed that in learning based on ICT, the Learner-centric environment need to be implemented. In online platform instructors provide course material to students but personalization of these materials is missing. The proposed work presented an ICT-based Learner-Centric Evolutionary Learning Model which suggests appropriate course material to learner, according to learner’s profile built by our profile builder module which includes details like learning style, demographic details, recommendations, expectations, likes and dislikes. Our proposed work presented is seamless integration of traditional teaching-learning methods with online teaching-learning methods. The equal importance to teachers is given here to design and recommend study material to different types of learners, their preference is at highest priority in proposed recommendation model. Active engagement of students in learning process increases education graph using technologies and finally it will be helpful in development of our country.

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