Haze hampers the performance of vision systems. So, removal of haze appearance in a scene should be the first-priority for clear vision. It finds wide spectrum of practical applications. A good number of dehazing techniques have already been developed. However, validation with the help of ground truth i.e. simulated haze on a clear image is an ultimate necessity. To address this issue, in this work synthetic haze images with various haze concentrations are simulated and then used to confirm the validation task of dark-channel dehazing mechanism, as it is a very promising single image dehazing technique. The simulated hazy image is developed using atmospheric model with and without Perlin noise. The effectiveness of dark-channel dehazing method is confirmed using the simulated haze images through average gradient metric, as haze reduces the gradient score.

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Prematurity refers to the birth of a baby before 37 completed weeks of pregnancy. This can be related to considerable parental anxiety and mental status changes. Anxiety can manifest as worrying thoughts, feelings of tension, and altered vital signs. This review aims to analyze the relationship between premature birth and parental anxiety, focusing on the emotional status of both mothers and fathers. The review was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 guidelines. A search was undertaken in PubMed, PubMed Central, MEDLINE, and ScienceDirect. Screening of articles was carried out to find relevant and appropriate articles. Articles were then quality-checked before inclusion. Our analysis showed that mothers of preterm infants had greater symptoms of anxiety, and comorbid anxiety and depression, than mothers of term infants. Mothers of preterm infants 5 years after discharge showed long-term consequences of stress and anxiety, including inappropriate responses and reduced praise for their children. Mothers of preterm multiples were more likely to experience stress and anxiety than mothers of preterm singletons. Fathers of preterm infants experienced higher levels of stress than fathers of term infants, but fathers of preterm infants experienced less stress than mothers of preterm infants. These findings suggest that routine mental health screening and intervention should be undertaken for both mothers and fathers of preterm infants.

Borderline Personality Disorder (BPD) is an extraordinarily complex and least-understood mental disorder. It is currently known that BPD is related to aggression and anger. However, fewer studies focused on the prevalence of BPD symptoms among criminal offenders and their association with crime. This systematic review act per Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines. We searched PubMed, PubMed Central (PMC), Medical Literature Analysis and Retrieval System Online (MEDLINE), and Science Direct for appropriate studies and research. Screening of articles was performed based on relevance and inclusion and exclusion criteria. To check for bias, we used relevant quality appraisal tools. Initially, we found 10026 articles. After removing duplicates and irrelevant papers, we finalized 18 studies based on titles, abstracts, and reading entire articles. We excluded eight studies because of poor quality, and the remaining ten papers were included in this review. BPD was related to criminal activities and suicidal and non-suicidal acts. Adverse childhood experiences positively predicted self-injurious behavior. There is a positive linkage between BPD symptoms and general offense, Inter-Partner Violence (IPV), and Child Abuse Potential (CPA).

Social media are interactive computer mediated technology that facilitates the sharing of information via virtual communities and networks. And Twitter is one of the most popular social media for social interaction and microblogging. This paper introduces an improved system model to analyze twitter data and detect terrorist attack event. In this model, a ternary search is used to find the weights of predefined keywords and the Aho-Corasick algorithm is applied to perform pattern matching and assign the weight which is the main contribution of this paper. Weights are categorized into three categories: Terror attack, Severe Terror Attack and Normal Data and the weights are used as attributes for classification. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are two machine learning algorithms used to predict whether a terror attack happened or not. We compare the accuracy with our actual data by using confusion matrix and measure whether our result is right or wrong and the achieved result shows that the proposed model performs better.