Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

In the current era, there are a plethora of mobile phone companies rendering different features. It is challenging to distinguish the best and create correlations among them. However, this can be accomplished through crowdsourcing. Crowdsourcing is the process of gathering information from multiple sources, and we use the AHP (Analytic Hierarchy Process) process to determine which company’s model is the best among many. The weight value of each model is compared to the assigned values, and if one of the company product weights is greater than the assigned weight, that product is the best. Eventually, we can use this process to select the most preferred and best mobile phone model from among all other models. Gray Relational Analysis (GRA) is one of the most popular models, employing a grey co-efficient that estimates the data items by ranking. This model defines a process’s situation or state as black with no information and white with perfect information. In this work, AHP initially assumes criteria weights and assigns rank with the CR (Consistency Ratio) of 1.5%. The criteria weights are re-assigned based on the outcomes, and the CR remains constant as 1.5%. This work also provides an environmental-based attribute access control system, which adds the strength to the system by providing security and the integrity. So, this proposed work performs as a decision support system combined with the security enhancements, and hence it becomes a complete framework to provide a solution to a target application. The novelty of the proposed work is the combination of the crowdsourcing with the recommender system on a secured framework.

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Today, people frequently communicate through interactions and exchange knowledge over the social web in various formats. Social connections have been substantially improved by the emergence of social media platforms. Massive volumes of data have been generated by the expansion of social networks, and many people use them daily. Therefore, one of the current problems is to make it easier to find the appropriate friends for a particular user. Despite collaborative filtering’s huge success, accuracy and sparsity remain significant obstacles, particularly in the social networking sector, which has experienced astounding growth and has a large number of users. Social connections have been substantially improved by the emergence of social media platforms. In this work, a social and semantic-based collaborative filtering methodology is proposed for personalized recommendations in the context of social networking. A new hybrid collaborative filtering (HCoF) approach amalgamates the social and semantic suggestions. Two classification strategies are employed to enhance the performance of the recommendation to a high rate. Initially, the incremental K-means algorithm is applied to all users, and then the KNN algorithm for new users. The mean precision of 0.503 obtained by HCoF recommendation with semantic and social information results in an effective collaborative filtering enhancement strategy for friend recommendations in social networks. The evaluation’s findings showed that the proposed approach enhances recommendation accuracy while also resolving the sparsity and cold start issues.