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Plant Leaf Disease (PLD) detection is helpful for several fields like Agriculture Institute and Biological Research. The country’s economic growth depends on the productivity of the agricultural field. Recently developed models based on deep learning give more accurate and precise results over the detection and classification of PLD while evolving through image processing approaches. Many image-processing approaches are used for the identification and classification of PLD. The quality of agricultural products is mainly affected by several factors like fungi, bacteria, and viruses. These factors severely destroy the entire growth of the plant. Hence, some outperformed models are needed to detect and identify the severity level of plant diseases yet, the identification requires more time and has a struggle to identify the appropriate type of disease based on its symptoms. Therefore, several automatic detection and classification models are developed to avoid the time complexity. Computerized image processing approaches are utilized for crop protection, which analyzes the color information of leaves from the collected images. Hence, image processing techniques play an important role in the identification and classification of PLD. It gives more advantages by lowering the task of illustrating crops on large farms and detecting the leaf diseases at the initial stage itself based on the symptoms of the plant leaves. While implementing a new model, there is a need to study various machine and deep learning-based structures for PLD detection approaches. This research work provides an overview of various heuristic approaches, machine learning, and deep learning models for the detection and classification of PLD. This research work also covers the various constraints like PLD detection tools, performance measures, datasets used, and chronological review. Finally, the research work explores the research findings and also the research gaps with future scope.

Recognition of natural emotion from human faces has applications in Human–Computer Interaction, image and video retrieval, automated tutoring systems, smart environment as well as driver warning systems. It is also a significant indication of nonverbal communication among the individuals. The assignment of Face Emotion Recognition (FER) is predominantly complex for two reasons. The first reason is the nonexistence of a large database of training images, and the second one is about classifying the emotions, which can be complex based on the static input image. In addition, robust unbiased FER in real time remains the foremost challenge for various supervised learning-based techniques. This survey analyzes diverse techniques regarding the FER systems. It reviews a bunch of research papers and performs a significant analysis. Initially, the analysis depicts various techniques that are contributed in different research papers. In addition, this paper offers a comprehensive study regarding the chronological review and performance achievements in each contribution. The analytical review is also concerned about the measures for which the maximum performance was achieved in several contributions. Finally, the survey is extended with various research issues and gaps that can be useful for the researchers to promote improved future works on the FER models.

Epilepsy is an unavoidable major persistent and critical neurological disorder that influences the human brain. Moreover, this is apparently distinguished via its recurrent malicious seizures. A seizure is a phase of synchronous, abnormal innervations of a neuron’s population which might last from seconds to a few minutes. In addition, epileptic seizures are transient occurrences of complete or partial irregular unintentional body movements that combine with consciousness loss. As epileptic seizures rarely occurred in each patient, their effects based on physical communications, social interactions, and patients’ emotions are considered, and treatment and diagnosis are undergone with crucial implications. Therefore, this survey reviews 65 research papers and states an important analysis on various machine-learning approaches adopted in each paper. The analysis of different features considered in each work is also done. This survey offers a comprehensive study on performance attainment in each contribution. Furthermore, the maximum performance attained by the works and the datasets used in each work is also examined. The analysis on features and the simulation tools used in each contribution is examined. At the end, the survey expanded with different research gaps and their problem which is beneficial to the researchers for promoting advanced future works on epileptic seizure detection.

Research on the theoretical aspects and practice of impact assessment (IA) is increasingly viewed as significant in enhancing its effectiveness. Several research articles have been published on various aspects of IA practice in Pakistan, but perhaps not well shared through a unified platform. This article presents an analysis of the areas well covered, identifies barriers and gaps in current research and what needs the most urgent attention in future endeavours. Most research to date has focused on the EIA process/system, public participation and the quality of EIA reports using evaluation criteria. There is a need to do more research on methods and techniques of assessment, environmental impact assessment follow-up, monitoring and possible application of strategic environmental assessment.