Now-a-days, the video recording technologies have turned out to be more and more forceful and easier to utilize. Therefore, numerous universities are recording and publishing their lectures online in order to make them reachable for learners or students. These lecture videos encapsulate the handwritten text written either on a paper or blackboard or on a tablet using a stylus. On the other hand, this mechanism of recording the lecture videos consumes huge quantity of multimedia data in a faster manner. Thus, handwritten text recognition on the lecture video portals has turned out to be an incredibly significant and demanding task. Thus, this paper intends to develop a novel handwritten text detection and recognition approach on the video lecture dataset by following four major phases, viz. (a) Text Localization, (b) Segmentation (c) Pre-processing and (d) Recognition. The text localization in the lecture video frames is the initial phase and here the arbitrarily oriented text on video frames is localized using the Modified Region Growing (MRG) algorithm. Then, the localized words are subjected to segmentation via the K-means clustering, in which the words from the detected text regions are segmented out. Subsequently, the segmented words are pre-processed to avoid the blurriness artifacts as well. Finally, the pre-processed words are recognized using the Deep Convolutional Neural Network (DCNN). The performance of the proposed model is analyzed in terms of the performance measures like accuracy, precision, sensitivity and specificity to exhibit the supremacy of the text detection and recognition in lecture video. Experimental results reveal that at Learning Percentage of 70, the presented work has the highest accuracy of 89.3% for 500 count of frames.

The advent of internet has lead to colossal development of e-learning frameworks. The efficiency of such systems however relies on the effectiveness and fast content based retrieval approaches. This paper presents a methodology for efficient search and retrieval of lecture videos based on Machine Learning (ML) text classification algorithm. The text transcript is generated exclusively from the audio content extracted from the video lectures. This content is utilized for the summary and keyword extraction which is used for training the ML text classification model. An optimized search is achieved based on the trained ML model. The performance of the system is compared by training the system using Naive Bayes, Support Vector Machine and Logistic Regression algorithms. Performance evaluation was done by precision, recall, F-score and accuracy of the search for each of the classifiers. It is observed that the system trained on Naive Bayes classification algorithm achieved better performance both in terms of time and also with respect to relevancy of the search results

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Agriculture carries greater risk than nearly any other sector. In agriculture, the proverb “you harvest what you sow” is not necessarily true. Because there is so much going on at the farm, it is very challenging for farmers to concentrate on all the everyday concerns such as weather, crop disease, commodity pricing, and fertilization schedules. In order to fulfil these rising needs, farmers must also produce more food with the limited water and land resources available due to the growing global population and food consumption. There will be a huge increase in the number of people to feed in 30 years; thus, agricultural methods must change to match the need. Researchers and scientists are now working to implement new IoT technologies in smart farming to assist farmers in creating better seeds, crop protection, and fertilizers using AI technology. Both the country's general economy and the profitability of farmers will benefit from this. This chapter discusses predictive agriculture, its need, advantages, challenges, and future of predictive analytics in agriculture.