Zia Ullah¹, Esra AlDhaen², Fatema Saleh AlDhaen², Bee-Lia Chua³, Heesup Han^4,*

International Journal of Mental Health Promotion, Vol.25, No.12, pp. 1245-1256, 2023, DOI:10.32604/ijmhp.2023.030577 - 29 December 2023

Abstract Apart from socio-economic disparities, indigenous people of the Himalayan range in Asia face an increasing trend of suicides. The tragic suicidal events usually go unaddressed, and no strategies are presently in place to mitigate suicides in the future. This study aims to explain the prevailing causes of suicidal ideation to come up with some policy recommendations. Through a preliminary survey, we identified social stigma, social isolation, lack of healthcare facilities, and domestic violence as the potential reasons for suicidal ideation. We identified individuals with suicide ideation for further data collection to test the relationships between… More >

Yixin Tang^*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 867-891, 2023, DOI:10.32604/cmc.2023.041550 - 31 October 2023

Abstract Cartoons serve as significant sources of entertainment for children and adolescents. However, numerous animated videos contain unsuitable content, such as violence, eroticism, abuse, and vehicular accidents. Current content detection methods rely on manual inspection, which is resource-intensive, time-consuming, and not always reliable. Therefore, more efficient detection methods are necessary to safeguard young viewers. This paper addresses this significant problem by proposing a novel deep learning-based system, AnimeNet, designed to detect varying degrees of violent and erotic content in videos. AnimeNet utilizes a novel Convolutional Neural Network (CNN) model to extract image features effectively, classifying violent… More >

Qurat-ul-Ain Arshad¹, Mudassar Raza¹, Wazir Zada Khan², Ayesha Siddiqa², Abdul Muiz², Muhammad Attique Khan^3,*, Usman Tariq⁴, Taerang Kim⁵, Jae-Hyuk Cha^5,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1103-1125, 2023, DOI:10.32604/cmc.2023.039752 - 08 June 2023

Abstract Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terrorism, can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations, busy sporting fields, airports, shopping areas, military bases, care centers, etc. Deep learning models’ learning capability is leveraged to identify abnormal situations with improved accuracy. This work proposes a deep learning architecture called Anomalous Situation Recognition Network (ASRNet) for deep feature extraction to improve the… More >

Sareer Ul Amin¹, Yongjun Kim², Irfan Sami³, Sangoh Park^1,*, Sanghyun Seo^4,*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3939-3958, 2023, DOI:10.32604/csse.2023.034805 - 03 April 2023

Abstract In the present technological world, surveillance cameras generate an immense amount of video data from various sources, making its scrutiny tough for computer vision specialists. It is difficult to search for anomalous events manually in these massive video records since they happen infrequently and with a low probability in real-world monitoring systems. Therefore, intelligent surveillance is a requirement of the modern day, as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies. In this article, we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance… More >

Abeer Abdulaziz AlArfaj¹, Nada Ali Hakami^2,*, Hanan Ahmed Hosni Mahmoud¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1423-1439, 2023, DOI:10.32604/iasc.2023.028067 - 19 July 2022

Abstract Social Media such as Facebook plays a substantial role in virtual communities by sharing ideas and ideologies among different populations over time. Social interaction analysis aids in defining people’s emotions and aids in assessing public attitudes, towards different issues such as violence against women and children. In this paper, we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social media. We searched for Arabic posts of many countries through Facebook application programming interface (API). We discovered that the stress state of a battered woman is usually related to her… More >

Batyrkhan Omarov^1,2,3,4,*, Sergazy Narynov¹, Zhandos Zhumanov^1,2, Aidana Gumar^1,5, Mariyam Khassanova^1,5

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 315-331, 2022, DOI:10.32604/cmc.2022.024566 - 24 February 2022

Abstract In this paper, we propose a skeleton-based method to identify violence and aggressive behavior. The approach does not necessitate high-processing equipment and it can be quickly implemented. Our approach consists of two phases: feature extraction from image sequences to assess a human posture, followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence. A video violence dataset of 400 min comprising a single person's activities and 20 h of video data including physical violence and aggressive acts, and 13 classifications for distinguishing aggressor and victim behavior were More >

Muzamil Ahmed^1,2, Muhammad Ramzan^3,4, Hikmat Ullah Khan², Saqib Iqbal⁵, Muhammad Attique Khan⁶, Jung-In Choi⁷, Yunyoung Nam^8,*, Seifedine Kadry⁹

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2217-2230, 2021, DOI:10.32604/cmc.2021.018103 - 21 July 2021

Abstract Violence recognition is crucial because of its applications in activities related to security and law enforcement. Existing semi-automated systems have issues such as tedious manual surveillances, which causes human errors and makes these systems less effective. Several approaches have been proposed using trajectory-based, non-object-centric, and deep-learning-based methods. Previous studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other methods. However, the their performance must be improved. This study explores the state-of-the-art deep learning architecture of convolutional neural networks (CNNs) and inception V4 to detect and recognize violence… More >

Tao Li¹, Fei Feng^2,*, Che Tong Nah³

International Journal of Mental Health Promotion, Vol.23, No.1, pp. 111-120, 2021, DOI:10.32604/IJMHP.2021.014433 - 19 February 2021

Abstract This study aimed to investigate the link between the exposure to intimate partner violence (IPV), core self-evaluations (CSE), and psychological adaptation of Chinese adolescents, through analysis of the results from the Survey of Children’s Exposure to Domestic Violence Scale, Core Self-Evaluations Scale, and Strengths and Difficulties Questionnaire, involving a total of 597 Chinese middle school students. It is indicated that the exposure to IPV is positively correlated with lower levels of psychological adaptation and CSE, and CSE is positively correlated with higher levels of psychological adaptation. Mediation analysis revealed that CSE partially mediated the association More >

Rashmi Rai¹, Ambarish Kumar Rai^2,*

International Journal of Mental Health Promotion, Vol.22, No.4, pp. 291-302, 2020, DOI:10.32604/IJMHP.2020.012153 - 22 December 2020

Abstract The intimate partner violence (IPV) against women has been identified as a violation of human rights and a serious public health concern. There is not only the immediate consequence of partner violence, such as injury or death but also the other long-term health consequences. IPV can be associated with psychological effects such as depressive disorder, posttraumatic stress disorder, and substance abuse. The study aims to explore the nature and causes of IPV on women’s life and their personal experiences to deal with. This is an NGO-based study. For better understanding of the issues, Purposive sampling… More >

Tao Li¹, Che Tong Nah², Fei Feng^3,*

International Journal of Mental Health Promotion, Vol.20, No.2, pp. 67-74, 2018, DOI:10.32604/IJMHP.2018.010839

Abstract This study aimed to investigate the link between community violence exposure, experiential avoidance and depression among Chinese adolescents. A total of 468 middle school students from China completed the Survey of Children’s Exposure to Community Violence, Acceptance and Action Questionnaire-Second Edition (AAQ-II) and Children’s Depression Inventory (CDI). The results suggested that the depression was positively correlated with the level of community violence exposure and experiential avoidance, and community violence exposure was positively correlated with experiential avoidance. Mediation analysis revealed that experiential avoidance partially mediated the association between exposure to community violence and depression. These results More >