Cotton production per hectare is low in Pakistan due to many biotic and abiotic factors.  As boll is the basic determinant for yield in cotton crop, a study on within boll yield parameters was carried out using 24 cotton bulk and 2 check varieties to check their variability for within boll yield components.  The experiment was performed in the research area of ​​the Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad.  The genotypes were seeded in two replications following a randomized complete block design. Data were subjected to analysis of variance to check significance among different genotypes. Genotypes were significantly different. The genotype PB-132 performed best for most parameters including GOT, lint index, lint mass per seed, and seed density. Correlation analysis was applied to find out the association of these parameters.  Seed cotton yield was positively associated with the GOT, boll weight and number of seeds per boll while it negatively correlated with fiber fineness, seed volume, and seed surface area. The first principal component showed 26.37%, the second component showed 17.93%, the third component showed 14.88%, and the fourth component showed 14.40% of total variation. PCA analysis showed the genetic diversity among cotton genotypes. The current study's findings revealed the potential of different bulks of cotton for developing high-yielding varieties. This information may be used to develop breeding strategies to enhance cotton production and variety.

In today’s world, suspicious or unusual activities express threats and danger to others. For the prevention of various security issues, an automatic video detection system is very important. It is difficult to consecutively monitor camera videos recorded in public places to detect any abnormal event, so an automated video detection system is needed. The study objective is to create an intelligent and trustworthy system that will take a video stream as input and detect what kind of suspicious activity is happening in that video to reduce the time that consumes watching the video. In this work, we use three models Convolutional Neural Network (CNN), GRU, and ConvLSTM model. These models are trained on the same dataset of 6 suspicious activities of humans that are: Running, Punching, Falling, Snatching, Kicking, and Shooting. The dataset consists of various videos related to each activity. Different deep learning techniques are applied in the proposed work: preprocessing, data annotation model training, and classification. The frames are extracted from the source video, and then features are calculated through the model in Inception v3, a Convolutional Neural Network variant. On the same dataset, the CNN model attains 91.55% accuracy, the ConvLSTM model attains 88.73% accuracy, and the GRU model attains 84.01% accuracy. The performance of the proposed models is evaluated using a confusion matrix, F1-Score, precision, and recall. The proposed model proved better than other models in terms of performance and accuracy. The findings of this study prove helpful unusual events by examining a person's abnormal behavior.

In today’s world suspicious or unusual activities express threat and danger to others. For the prevention from various security issues an automatic video detection system is very important. The study objective is to create an intelligent system that will take a video stream as input and detect what kind suspicious activity is happening in that particular video to reduce the time that consume on watching video. It is difficult to consecutively monitor cameras videos that recorded in public places for the detection any abnormal event so an automatic video detection system is needed for that purpose. For that purpose, deep learning-based model is the best approach. In this work we use three models Convolutional neural network (CNN) model GRU model and ConvLSTM model. These models are trained on the same dataset of 6 suspicious activities of humans that are (Running, Punching, Falling, Snatching, Kicking and Shooting). The dataset consist of various video related to each activity. Different deep learning techniques are applied in the proposed work that are preprocessing, data annotation model training and classification. The frames are extracted from the source video and then features are calculated through model known as Inception v3 which is a variant of Convolutional Neural Network. On the same dataset the CNN model attains 91.55% accuracy the ConvLSTM model attain 88.73% accuracy and the GRU model attain 84.01% accuracy. The performance of proposed models are evaluated using confusion matrix, f1-score, precision, and recall. The proposed model proved better than other models in terms of performance and accuracy. The findings of this study prove helpful unusual event by examining the abnormal behaviour of person.

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