Abstract-Speech signal can be used to extract emotions. However, it is pertinent to note that variability in speech signal can make emotion extraction a challenging task. There are a number of factors that indicate presence of emotions. Prosodic and temporal features have been used previously for the purpose of identifying emotions. Separately, prosodic/temporal and linguistic features of speech do not provide results with adequate accuracy. We can also find out emotions from linguistic features if we can identify contents. Therefore, We consider prosodic as well as temporal or linguistic features which help increasing accuracy of emotion recognition, which is our first contribution reported in this paper. We propose a two-step model for emotion recognition; we extract emotions based on prosodic features in the first step. We extract emotions from word segmentation combined with linguistic features in the second step. While performing our experiments, we prove that the classification mechanisms, if trained without considering age factor, do not help improving accuracy. We argue that the classifier should be based on the age group on which the actual emotion extraction be required, and this becomes our second contribution submitted in this paper.

Based on the rapid increase in the demand for people counting and tracking systems for surveillance applications, there is a critical need for more accurate, efficient, and reliable systems. The main goal of this study was to develop an accurate, sustainable, and efficient system that is capable of error-free counting and tracking in public places. The major objective of this research is to develop a system that can perform well in different orientations, different densities, and different backgrounds. We propose an accurate and novel approach consisting of preprocessing, object detection, people verification, particle flow, feature extraction, self-organizing map (SOM) based clustering, people counting, and people tracking. Initially, filters are applied to preprocess images and detect objects. Next, random particles are distributed, and features are extracted. Subsequently, particle flows are clustered using a self-organizing map, and people counting and tracking are performed based on motion trajectories. Experimental results on the PETS-2009 dataset reveal an accuracy of 86.9% for people counting and 87.5% for people tracking, while experimental results on the TUD-Pedestrian dataset yield 94.2% accuracy for people counting and 94.5% for people tracking. The proposed system is a useful tool for medium-density crowds and can play a vital role in people counting and tracking applications.

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Crowd management becomes a global concern due to increased population in urban areas. Better management of pedestrians leads to improved use of public places. Behavior of pedestrian's is a major factor of crowd management in public places. There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment. In this paper, we have proposed a new method for pedestrian's behavior detection. Kalman filter has been used to detect pedestrian's using movement based approach. Next, we have performed occlusion detection and removal using region shrinking method to isolate occluded humans. Human verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each silhouettes. Gray Wolf Optimizer (GWO) has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient (XG) Boost classifier. Performance has been evaluated using pedestrian's data from avenue and UBI-Fight datasets, where both have different environment. The mean achieved accuracies are 91.3% and 85.14% over the Avenue and UBI-Fight datasets, respectively. These results are more accurate as compared to other existing methods.

With the dramatic increase in video surveillance applications and public safety measures, the need for an accurate and effective system for abnormal/suspicious activity classification also increases. Although it has multiple applications, the problem is very challenging. In this paper, a novel approach for detecting normal/abnormal activity has been proposed. We used the Gaussian Mixture Model (GMM) and Kalman filter to detect and track the objects, respectively. After that, we performed shadow removal to segment an object and its shadow. After object segmentation we performed occlusion detection method to detect occlusion between multiple human silhouettes and we implemented a novel method for region shrinking to isolate occluded humans. Fuzzy c-mean is utilized to verify human silhouettes and motion based features including velocity and optical flow are extracted for each identified silhouettes. Gray Wolf Optimizer (GWO) is used to optimize feature set followed by abnormal event classification that is performed using the XG-Boost classifier. This system is applicable in any surveillance application used for event detection or anomaly detection. Performance of proposed system is evaluated using University of Minnesota (UMN) dataset and UBI (University of Beira Interior)-Fight dataset, each having different type of anomaly. The mean accuracy for the UMN and UBI-Fight datasets is 90.14% and 76.9% respectively. These results are more accurate as compared to other existing methods.

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Objective: To determine the association of intrapartum CTG with fetomaternal outcome Material and Methods: A total number of 120 pregnant females who presented in the department of obstetrics and gynecology with labour pain were included in this cross-sectional analysis. A written informed consent was taken from all patients. The study was conducted in the department of Obstetrics & Gynaecology at Islam Teaching Hospital, Sialkot from January, 2021 to September, 2021. At presentation in the labor room, 20 minutes CTG was performed and patients were divided into two groups, those having abnormal trace including suspicious and pathological trace (Group A) and normal cardiotocography (CTG) pattern (Group B). After that the patients were followed till delivery to determine the feto-maternal outcomes e.g. APGAR score, NICU admission, perinatal mortality and caesarean section rate. Results: The mean age was 26.9±4.12 years in group A and 27. 1 ± 3.9 years in group B (p-value 0.78). On comparison of maternal outcomes, caesarean section was done in 38 (63.3%) patients in group-A and in 17 (28.3%) patients in group-B (p-value <0.0001).Regarding neonatal outcomes, NICU admission was needed in 9 (15%) patients in group A, versus in 4 (6.7%) patients in group B (p-value 0.14). Perinatal mortality occurred in 03 (5.0%) patients in group A and in no patient in group B (p-value 0.07). APGAR score at 5 minutes was >7 in 46 (76.7%) patients in group A versus in 52 (86.7%) patients in group B (p-value 0.18). Conclusion: The intrapartum abnormal CTG cannot be used as the only tool to identify fetal hypoxia during labor. It may lead to increased caesarean section rate because of high false positive rate of abnormal CTG. Keywords: Cardiotocography, Fetal Distress, Cesarean section

Objective: To determine the association of intrapartum CTG with fetomaternal outcome Material and Methods: A total number of 120 pregnant females who presented in the department of obstetrics and gynecology with labour pain were included in this cross-sectional analysis. A written informed consent was taken from all patients. The study was conducted in the department of Obstetrics & Gynaecology at Islam Teaching Hospital, Sialkot from January, 2021 to September, 2021. At presentation in the labor room, 20 minutes CTG was performed and patients were divided into two groups, those having abnormal trace including suspicious and pathological trace (Group A) and normal cardiotocography (CTG) pattern (Group B). After that the patients were followed till delivery to determine the feto-maternal outcomes e.g. APGAR score, NICU admission, perinatal mortality and caesarean section rate. Results: The mean age was 26.9±4.12 years in group A and 27. 1 ± 3.9 years in group B (p-value 0.78). On comparison of maternal outcomes, caesarean section was done in 38 (63.3%) patients in group-A and in 17 (28.3%) patients in group-B (p-value <0.0001).Regarding neonatal outcomes, NICU admission was needed in 9 (15%) patients in group A, versus in 4 (6.7%) patients in group B (p-value 0.14). Perinatal mortality occurred in 03 (5.0%) patients in group A and in no patient in group B (p-value 0.07). APGAR score at 5 minutes was >7 in 46 (76.7%) patients in group A versus in 52 (86.7%) patients in group B (p-value 0.18). Conclusion: The intrapartum abnormal CTG cannot be used as the only tool to identify fetal hypoxia during labor. It may lead to increased caesarean section rate because of high false positive rate of abnormal CTG. Keywords: Cardiotocography, Fetal Distress, Cesarean section