A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed

Disruption in daily eating-fasting and activity-rest cycles in Indian adolescents attending school.

A lifestyle with erratic eating patterns and habits predisposes youngsters to obesity. Through a two-phase feasibility study among Indian students living in the Delhi area, we longitudinally examined the following: (1) the daily eating-fasting cycles of students (N = 34) in school and college using smartphones as they transition from high school (aged 13-15 years; nIX = 13) to higher secondary school (HSSS; 16-18 years; nXII = 9) to their first year (FY) of college (18-19 years; nFC = 12); and (2) daily activity-rest cycles and light-dark exposure of 31 higher secondary school students (HSSS) using actigraphy. In phase 1, students' food data were analyzed for temporal details of eating events and observable differences in diet composition, such as an energy-dense diet (fast food (FF)), as confounding factors of circadian health. Overall, the mean eating duration in high school, higher secondary and FY college students ranged from 14.1 to 16.2h. HSSS exhibited the shortest night fasting. Although FY college students exhibited the highest fast food percentage (FF%), a positive correlation between body mass index (BMI) and FF% was observed only among HSSS. Furthermore, the body weight of HSSS was significantly higher, indicating that FF, untimely eating and reduced night fasting were important obesity-associated factors in adolescents. Reduced night fasting duration was also related to shorter sleep in HSSS. Therefore, food data were supplemented with wrist actigraphy, i.e., activity-rest data, in HSSS. Actigraphy externally validated the increased obesogenic consequences of deregulated eating rhythms in HSSS. CamNtech motion watches were used to assess the relationship between disturbed activity cycles of HSSS and other circadian clock-related rhythms, such as sleep. Less than 50% of Indian HSSS slept 6 hours or more per night. Seven of 31 students remained awake throughout the night, during which they had more than 20% of their daily light exposure. Three nonparametric circadian rhythm analysis (NPCRA) variables revealed circadian disruption of activity in HSSS. The present study suggests that inappropriate timing and quality of food and sleep disturbances are important determinants of circadian disruptions in adolescents attending school

SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection

The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence of efficient intrusion detection systems. The network attacks are also dynamic in nature, something which increases the importance of developing appropriate models for classification and predictions. Machine learning (ML) and deep learning algorithms have been prevalent choices in the analysis of intrusion detection systems (IDS) datasets. The issues pertaining to quality and quality of data and the handling of high dimensional data is managed by the use of nature inspired algorithms. The present study uses a NSL-KDD and KDD Cup 99 dataset collected from the Kaggle repository. The dataset was cleansed using the min-max normalization technique and passed through the 1-N encoding method for achieving homogeneity. A spider monkey optimization (SMO) algorithm was used for dimensionality reduction and the reduced dataset was fed into a deep neural network (DNN). The SMO based DNN model generated classification results with 99.4% and 92% accuracy, 99.5%and 92.7% of precision, 99.5% and 92.8% of recall and 99.6%and 92.7% of F1-score, utilizing minimal training time. The model was further compared with principal component analysis (PCA)-based DNN and the classical DNN models, wherein the results justified the advantage of implementing the proposed model over other approaches

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors. BC can offer decentralized and secure data storage, while CV allows machines to learn and understand visual data. The integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed