Agriculture is the major source of food and livelihood of many countries. In the recent years many developing countries are adopting technology to improve farming. Farming Drones are currently being used for spraying pesticides, seed bombing, data gathering for precision agriculture, etc. Farming drones are also used for aerial imagery to estimate the count of fruits and other produce. Extensive research has been done to use drones for aerial imagery and estimate of leaf disease proliferation in farms. Most of the research focuses on image gathering on front side of leaves as drones fly above the leaf foliage. However, there are several leaf diseases that proliferate in the rear side of the leaf. Few examples include Mildew and Cabbage Looper. In this work we propose a novel drone design (patented already) that captures rear side of the leaf images. Early disease detection can help in reducing the use of pesticides, increasing the quantity and quality of the yield. In the proposed solution, camera can go below the leaves - to capture the image of the affected area. The drone captured images are resized to 224x224. Feature extraction frameworks using Deep learning models including VGG16, efficientNetBo, ResNet or AlexNet were used to classify if the leaf is healthy or unhealthy. Experimentation provided Validation accuracy of 98 % and model training accuracy of 89 %. .

Social media sentiment is proven to be an important feature in financial forecasting. While the effect of sentiment is complex and time-varying for traditional financial assets, its role in cryptocurrency markets is unclear. This research explores the predictive power of public sentiment on Bitcoin trading volume. We develop a novel sentiment analysis pipeline for processing Bitcoin-related tweets and achieve state-of-the-art accuracy on a benchmark dataset. Our pipeline also leverages information gain theory to incorporate the impact of textual and non-textual features. We use such features to discern a nonlinear relationship between public sentiment and Bitcoin trading volume and discover the optimal predictive horizon for Bitcoin. This research provides a useful module and a foundation for future studies and understanding of Bitcoin market dynamics, and its interaction with social media buzzing. trading volume and lagged social media sentiments. However, experiments with LSTM losses indicate a more complex, non-linear relationship between the two features, and the maximum dependency is visible at an predictive horizon of 3-4 hours. Our study is an important step toward understanding the volatility and the future price of the cryptocurrency market and its interaction with social media content using textual analysis.

Waste management is an essential societal issue, and the classical and manual waste auditing methods are hazardous and time-consuming. In this paper, we introduce a novel method for waste detection and classification to address the challenges of waste management. The method uses a collection of deep neural networks to allow for accurate waste detection, classification, and waste size quantification. The trained neural network model is integrated into a mobile-based application for trash geotagging based on images captured by users on their smartphones. The tagged images are then connected to the cleaners’ database, and the nearest cleaners are notified of the waste. The experimental results using publicly available datasets show the effectiveness of the proposed method in terms of detection and classification accuracy. The proposed method achieved an accuracy of at least 90%, which surpasses that reported by other state-of-the-art methods on the same datasets.

E. Porter1, M.X. Luo2, B. Lit1, I. McKechnie1, J. Saha3, P. Ratra1, N. Lewis1, Z. Norman1, K. Cottenie1, S. Jacobs1, D. Gillis1 1University of Guelph (CANADA) 2University of Electronic Science and Technology of China (CHINA) 3Vellore Institute of Technology (INDIA)

Obstructive sleep apnea is a common problem arising in adults and children nowadays, determined by abnormalities in breathing gaps or incapability of air intake capacity during sleeping results in a decrease in oxygen level in blood.The brain detects this sudden decrease in the level of oxygen and sends a signal to wake the person up.Studies revealed the breathing stops for almost 10 seconds during a sleep apnea episode.There is no restriction on who can develop Obstructive Sleep Apnea(OSA), it can affect adults as well as infants.Our research primarily aims at assessing the various recent developments and studies made as a solution to this alarming problem.Their methodology and techniques have been studied and accuracy and sensitivity rates compared.A comprehensive and detailed study has been conducted on several research papers and studies done in the field of predicting sleep apnea.Sleep Apnea and classification of apneic signals have been mentioned in our study.The related researches have been studied extensively and compiled in our research work.The various techniques used by the researchers have been studied and tabulated along with the algorithm accuracies.It is observed that signal measurement along with AI algorithms has made significant advancements in OSA prediction.It is observed that Self Developed Algorithm on VAD showed the highest accuracy of 97%.PPG signal analysis and binary classification algorithm showed good accuracies of 86.67% and 86% respectively.AdaBoost, Decision Table and Bagging REPTree and SVM classifier also showed good accuracy of around 83% in the detection of Sleep Apnea episodes.The study highlighted the research works done to combat the rising problem of Obstructive Sleep Apnea.This comprehensive study of existing methods will help researchers to identify their drawbacks and find out more efficient solutions to them, which will help the humanity less prone to risks due to this alarming issue of sleep apnea.

Farmers are facing the VUCA environment (volatile, uncertain, complex and ambiguous) and data indicating the contribution of farming to India's GDP has come down from 52% to 18% between 1951 and 2018, which is alarming. At this juncture, developing countries like India, where over 70% of the rural people depend upon the agriculture fields, adoption of disruptive technology (creative destruction) becomes the need of the hour, to enhance the crop yield and quality. Weeds are one of the major issues which severely affect the crop output. Unmanned Aerial Vehicle (UAV) or drone is recommended, to address the problem. Globally, the market for agriculture drones to move from $1.3 billion to $ 6.52 billion by 2026. Globally agriculture is the second largest industry after construction in terms of drone adoption. But Indian farmers have difficulty in adopting (or) procuring UAV's, as the size of their farm is small, income is very less. Other problems associated with the adoption of UAV include knowledge transfer and training to farmers, service support and maintenance cost. DaaS (Drone as a service) model is proposed, for rural areas. This paper aims to focus on weed management by providing a safer and cost-effective solution. By integrating technologies like visible light (VIS), near-infrared (NIR) light on an Unmanned Ariel Vehicle along with a precise sprayer and a weed detection system backed up by a lithium-ion battery (for longer flight duration), can help the process of spraying weedicide efficiently. The accuracy of the tested model is 92.6% for far away detection module and 95.4 for close range detection. UAV's with sprayer protects the farmer and consumers from odour and side effects.

Agriculture is undoubtedly one of the biggest and most important professions in the world. Optimization of agriculture and aiming gradually and extensively toward smart agriculture are the need of the hour. IOT (Internet of Things) technology has already been successful in easing people’s lives with its wide range of applications in almost all arenas. In this paper, our work takes the help of IOT devices, wireless sensor network (WSN) and AI techniques and combines them for faster and effective recommendation of suitable crops to farmers based on a list of factors such as temperature, annual precipitation, total available land size, past crop grown history and other resources. Additionally, detection of unwanted plants on crops, namely weed detection, is implemented with frame-capturing drone and deep learning methods. Naïve Bayes algorithm for crop recommendation based on several factors detected by WSN sensor nodes has been used, resulting in an accuracy of 89.29%, which has proved to be better than several other discussed algorithms in the paper, like regression or support vector machine. Deep learning using neural network successfully identifies weeds present in a specific area of crop growth extending an additional protective measure to farmers. The comprehensive application developed for farmers not only reduces the physical hardship and time spent on different agricultural activities, but also increases the overall land yield, reduces possibility of losses due to failure of crops in a particular soil and lessens the chances of damage caused to crops by weeds.