Kisan360 (Agro App-A perfect Marketplace for all Farmers)

Six decades ago, Mahatma Gandhi said that agriculture is the backbone of Indian economy. The situation remains the same today almost the entire economy is sustained by agriculture, which is the mainstay of the village. Now a days, farmer sells their product at wholesale price to wholesalers, wholesalers sell them to retailers and make more profit then farmers. This application can help to break the chain between farmers and retailers. Through the application, farmers sell their products directly to customers and make more profit. In this application farmers can sell their equipment and buy new one. Farmers can direct knowledge about how to do digital farming. Once the farmers application is made available, any farmer can find relevant information about specific seed, fertilizer, farming equipment, weather forecasting, market rate, etc. This application is easily accessible by the farmers and other users too. Farmers as well as other users can ask specific question and provide valuable feedback through a specially designed feedback module. Keywords: Farmers, marketplace, wholesalers, retailer, users, admin.

Adaptive Facial Imagery Clustering via Spectral Clustering and Reinforcement Learning

In an era of big data, face images captured in social media and forensic investigations, etc., generally lack labels, while the number of identities (clusters) may range from a few dozen to thousands. Therefore, it is of practical importance to cluster a large number of unlabeled face images into an efficient range of identities or even the exact identities, which can avoid image labeling by hand. Here, we propose adaptive facial imagery clustering that involves face representations, spectral clustering, and reinforcement learning (Q-learning). First, we use a deep convolutional neural network (DCNN) to generate face representations, and we adopt a spectral clustering model to construct a similarity matrix and achieve clustering partition. Then, we use an internal evaluation measure (the Davies–Bouldin index) to evaluate the clustering quality. Finally, we adopt Q-learning as the feedback module to build a dynamic multiparameter debugging process. The experimental results on the ORL Face Database show the effectiveness of our method in terms of an optimal number of clusters of 39, which is almost the actual number of 40 clusters; our method can achieve 99.2% clustering accuracy. Subsequent studies should focus on reducing the computational complexity of dealing with more face images.

A Novel Framework Design of Network Intrusion Detection Based on Machine Learning Techniques

Traditional machine learning-based intrusion detection often only considers a single algorithm to identify intrusion data, lack of the flexibility method, low detection rate, no handing high-dimensional data, and cannot solve these problems well. In order to improve the performance of intrusion detection system, a novel general intrusion detection framework was proposed in this paper, which consists of five parts: preprocessing module, autoencoder module, database module, classification module, and feedback module. The data processed by the preprocessing module are compressed by the autoencoder module to obtain a lower-dimensional reconstruction feature, and the classification result is obtained through the classification module. Compressed features of each traffic are stored in the database module which can both provide retraining and testing for the classification module and restore these features to the original traffic for postevent analysis and forensics. For evaluation of the framework performance proposed, simulation was conducted with the CICIDS2017 dataset to the real traffic of the network. As the experimental results, the accuracy of binary classification and multiclass classification is better than previous work, and high-level accuracy was reached for the restored traffic. At the last, the possibility was discussed on applying the proposed framework to edge/fog networks.

Kctd15 regulates nephron segment development by repressing Tfap2a activity

ABSTRACTA functional vertebrate kidney relies on structural units called nephrons, which are epithelial tubules with a sequence of segments each expressing a distinct repertoire of solute transporters. The transcriptiona`l codes driving regional specification, solute transporter program activation and terminal differentiation of segment populations remain poorly understood. Here, we demonstrate that the KCTD15 paralogs kctd15a and kctd15b function in concert to restrict distal early (DE)/thick ascending limb (TAL) segment lineage assignment in the developing zebrafish pronephros by repressing Tfap2a activity. During renal ontogeny, expression of these factors colocalized with tfap2a in distal tubule precursors. kctd15a/b loss primed nephron cells to adopt distal fates by driving slc12a1, kcnj1a.1 and stc1 expression. These phenotypes were the result of Tfap2a hyperactivity, where kctd15a/b-deficient embryos exhibited increased abundance of this transcription factor. Interestingly, tfap2a reciprocally promoted kctd15a and kctd15b transcription, unveiling a circuit of autoregulation operating in nephron progenitors. Concomitant kctd15b knockdown with tfap2a overexpression further expanded the DE population. Our study reveals that a transcription factor-repressor feedback module employs tight regulation of Tfap2a and Kctd15 kinetics to control nephron segment fate choice and differentiation during kidney development.

RESEARCH ON THE MAKER TEACHER MOBILE TRAINING PLATFORM BASED ON BIG DATA ANALYSIS

In the era of "Internet +" and Big Data, it is of great practical significance on how to build a training platform that accurately matches the professional development of maker teachers, and to carry out personalized mobile training for maker teachers under the Big Data analysis technology. The construction of the maker teacher mobile training platform, based on the big data analysis technology, is designed to explore the personalized needs of maker teachers in professional development. It introduces a new concept of MOOC and community space design to build the maker mobile training platform framework structure, which contains three layers: application layer, service layer, and data layer. It designs five functional modules: diagnostic demand analysis module, personalized service customization module, online maker course module, seminar space module, and evaluation feedback module. The case analysis of the platform and its application effect shows that the maker teacher mobile training platform based on big data analysis has obvious effects on professional development for teachers and can provide reference for future research on related topics.

Kctd15 regulates nephron segment development by repressing Tfap2a activity

A functional vertebrate kidney relies on structural units called nephrons, which are epithelial tubules that contain a sequence of segments each expressing a distinct repertoire of solute transporters. To date, the transcriptional codes driving regional specification, solute transporter program activation, and terminal differentiation of segment populations remain poorly understood. We demonstrate for the first time that the KCTD15 paralogs, kctd15a and kctd15b, function in concert to restrict distal early (DE)/thick ascending limb (TAL) segment lineage assignment in the developing zebrafish pronephros by repressing Tfap2a activity. During renal ontogeny, expression of these factors co-localized with tfap2a in distal tubule precursors. kctd15 loss primed nephron cells to adopt distal fates by driving expansions in slc12a1, kcnj1a.1, and stc1 marker expression. These phenotypes were resultant of Tfap2a hyperactivity, where kctd15a/b-deficient embryos exhibited increased abundance of this transcription factor. Interestingly, tfap2a reciprocally promoted kctd15 transcription, unveiling a circuit of autoregulation operating in nephron progenitors. Concomitant kctd15b knockdown with tfap2a overexpression produced genetic synergy and further expanded the DE population. Our study provides strong evidence that a transcription factor-repressor feedback module employs tight regulation of Tfap2a and Kctd15 kinetics to control nephron segment fate choice and differentiation during kidney development.

Malaysian Sign Language Mobile Learning Application : A recommendation app to communicate with hearing-impaired communities

Malaysian Sign Language (MSL) is an important language that is used as the primary communication method for the deaf communities with the others. Currently, the MSL is poorly known by the Malaysians and the existing platform of learning the sign language is inefficient, not to mention the incomplete functionality of the existing mobile learning application in the market. Hence, the purpose of developing this application is aimed to increase the knowledge and recognition of the public towards the MSL and allows them to learn the sign language more effectively. One of the features in this application is sign detection, which could analyze the image captured by phone camera into sign meaning. The application also comprised various categories of the sign for efficient learning and quiz to test user knowledge against their learned sign language. Besides, there is a feedback module for the user to express their opinions and suggestions towards the application to the developer. This application is aimed to help the public to learn MSL efficiently by selecting the category of sign they wish to learn and test themselves by using the quiz module. Besides, the application could also detect the unknown sign by capturing the image and analyze it. The application helped to raise the recognition of MSL among the public and expose the public to the sign language knowledge. It had also become a small help in breaking the barrier of communication between the deaf communities and the public.