Adaptation and Concurrent Validity of Screen for Children Anxiety Related Emotional Disorders (SCARED)

The current study was designed to adapt and investigate the psychometric properties of SCARED (screening children for anxiety related emotional disorders) as the first anxiety related emotional disorder screening tool for Pakistani children. The sample consisted of 8 to 11-year-old (N=322, mean age= 9.48) primary class children, including 157 girls and 165 boys. The 41-item child version of SCARED was translated into Urdu language by following Brislin’s translation method. The convergent validity of this scale was determined by comparing it with DBDRS (Disruptive Behaviour Disorder Rating Scale). Cronbach’s alpha reliability of SCARED scale was 0.89 and its subscales demonstrated internal consistencies ranging from 0.68 to 0.76 i.e. moderate to high, except one subscale SH, 0.45. The total score of SCARED was positively correlated with conduct disorders (r= 0.16, p 0.05), inattention(χ2= 0.11, p > 0.05), hyperactivity/ impulsive(χ2= 0.23, p > 0.05), ODD (χ2= 0.05, p > 0.05) and with ADHD combined scores (χ2= 0.27, p > 0.05.The findings supported convergent validity for anxiety disorders with disruptive behaviours depicting the comorbidity of anxiety and disruptive behaviour. The study provides support for the convergent validity of SCARED with DBDRS

Adaptation and Concurrent Validity of Screen for Children Anxiety Related Emotional Disorders (SCARED)

The current study was designed to adapt and investigate the psychometric properties of SCARED (screening children for anxiety related emotional disorders) as the first anxiety related emotional disorder screening tool for Pakistani children. The sample consisted of 8 to 11-year-old (N=322, mean age= 9.48) primary class children, including 157 girls and 165 boys. The 41-item child version of SCARED was translated into Urdu language by following Brislin’s translation method. The convergent validity of this scale was determined by comparing it with DBDRS (Disruptive Behaviour Disorder Rating Scale). Cronbach’s alpha reliability of SCARED scale was 0.89 and its subscales demonstrated internal consistencies ranging from 0.68 to 0.76 i.e. moderate to high, except one subscale SH, 0.45. The total score of SCARED was positively correlated with conduct disorders (r= 0.16, p 0.05), inattention(χ2= 0.11, p > 0.05), hyperactivity/ impulsive(χ2= 0.23, p > 0.05), ODD (χ2= 0.05, p > 0.05) and with ADHD combined scores (χ2= 0.27, p > 0.05.The findings supported convergent validity for anxiety disorders with disruptive behaviours depicting the comorbidity of anxiety and disruptive behaviour. The study provides support for the convergent validity of SCARED with DBDRS

Unmet psycho-social needs, coping strategies and psychological distress among people with cancer: Evidence from Pakistan

Objective: To explore the unmet psychosocial needs, coping styles and psychological distress among people ith cancer in Pakistan. Methods: A cross-sectional correlational study design was used for data collection. The research was conducted in Shifa International Hospital, Islamabad and Hayatabad Medical Complex, Peshawar with a sample of 182 participants diagnosed with cancer. Only those who consented to participate were approached from May to July, 2017. Supportive Care Needs Survey-Short Form 34 (SCNS-SF34), Mini-Mental Adjustment to Cancer Scale (Mini-Macs) and Hospital Anxiety Depression Scale (HADS) were used for data collection. Results: It was found that all psychosocial needs were unmet among all the participants (100%) who were suffering from cancer illness. Among five sub-domains of psychosocial needs, health information needs (35.61%) and psychological needs (30.7%) emerged to be strikingly unmet. Moreover, anxious preoccupation and hopeless/helplessness were highly endorsed maladaptive coping styles. A statistically significant relationship existed among unmet psycho-social needs, maladaptive coping and psychological distress. Conclusion: This Study outcome pointed towards gaps in delivering quality care services in Pakistani healthcare settings, inadequate attention of health professionals and serious psychological health care neglect of patients fighting with life threatening disease. This negligence may jeopardize patient’s overall health, can raise health care costs and consequently can contribute to elevated psychological distress. Hence, there is a dire need for proper psychological interventions for effective and holistic treatment planning which can improve the whole process of illness and recovery. Keywords: Cancer, Psychosocial support system, Continuous..

AMDDLmodel: Android smartphones malware detection using deep learning model.

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques

Graphical explanation of training and validation loss.

Graphical explanation of training and validation loss.</p

Graphical explanation of training and validation accuracy.

Graphical explanation of training and validation accuracy.</p

Statistical values of the CNN model.

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications’ endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user’s privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.</div

State-of-the-art comparison with the existing techniques.

State-of-the-art comparison with the existing techniques.</p

Dataset description.

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications’ endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user’s privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.</div