Asian Journal of Research in Computer Science

NoSQL databases outperform the traditional RDBMS due to their faster retrieval of large volumes of data, scalability, and high performance. The need for these databases has been increasing  in recent years because data collection is growing tremendously. Structured, unstructured, and semi- structured data storage is allowed in NoSQL, which is not possible in a traditional database. NoSQL needs to compensate with its security feature for its amazing functionalities of faster data access and large data storage. The main concern exists in sensitive information stored in the data. The need to protect this sensitive data is crucial for confidentiality and privacy problems. To understand the severity of preserving sensitive data, recognizing the security issues is important. These security issues, if not resolved, will cause data loss, unauthorized access, database crashes by hackers, and security breaches. This paper investigates the security issues common to the top twenty NoSQL databases of the following types: document, key-value, column, graph, object- oriented, and multi-model. The top twenty NoSQL databases studied were MongoDB, Cassandra, CouchDB, Hypertable, Redis, Riak, Neo4j, Hadoop HBase, Couchbase, MemcacheDB, RavenDB, Voldemort, Perst, HyperGraphDB, NeoDatis, MyOODB, OrientDB, Apache Drill, Amazon, and Neptune. The comparison results show that there are common security issues among the databases. SQL injection security issues were detected in eight databases. The names of the databases were MongoDB, Cassandra, CouchDB, Neo4j, Couchbase, RavenDB, OrientDB, and Apache Drill.

With the increase in the number of Health Fitness Applications (Apps) available for free, there is a growing concern as to whether these apps actually help individuals achieve personal fitness. This research developed a system to validate three Health Fitness Apps before user download using user reviews.

Sentiment Analysis as the application of natural language processing, computational linguistics, and text analytics was used to identify and classify subjective opinions in the reviews of three most commonly used Health Fitness Applications; Samsung Health, Google Fit and Home Workout. Analysis showed that the Home Workout Fitness Application garnered a total of 99.9% Positive Reviews and can therefore be said to be the most effective of the three Apps considered, followed by Google Fit Fitness Application with a total of 37.4% Positive Reviews and Samsung Health Fitness Application recorded the most Negative Reviews of 96.6%.

Aims: To study and estimate the forest cover loss and gain across the 13 districts of Uttarakhand.

Place and Duration of Study: Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, between September 2021 and December 2021.

Methodology: We extracted forest cover time-series data from the year 2001 to the year 2020 from Hensen Global Forest Change Dataset. This data was then mapped to the shapefile created in ARC-GIS containing all 13 districts as a Feature Collection, which was then used to individually classify each region and to estimate the size of the loss of tree cover precisely over the district boundary.

Results: Our study shows forest loss of about (21,05,71,646 square meters) and forest gain of (6,00,79,072 square meters) cumulatively in all the districts of Uttarakhand from the year 2001 to 2020 at a spatial resolution of 30 meters where trees were identified as canopies greater than 5 meters in height.

Conclusion: Among the districts of Uttarakhand Udham Singh Nagar, Nainital, and Champawat alone contribute to the total tree cover loss area of 15061.7513801 ha. which is about 71.5 % of Uttarakhand’s total tree cover loss. These regions require monitoring and controlling deforestation and more detailed studies like this are required to analyze and prevent the causes of such great-scale deforestation. Analyzing districts apart from those mentioned above, it is observed that the amount of tree cover loss is greater than the reforestation.

Water is the most valuable means of life sustainability because it is a basic need of all households. The incessant water leakage levels in the water supply network on the mini campus, Federal Polytechnic Offa, is one of the most vital issues to address by the work unit on the School campus. Works unit provides services to all departments on campus, covering an area of the landmass of 1050 hectares with a population of staff and students of approximately 13 000. The work unit is in charge of correctional facilities and operation. The sub-service of the work unit on water supply system comprising a network of 1km of water pipes and 1500 litres capacity storage tower across units. The input system volume in the lose of water supply is approximately 56%, from which a significant proportion is about physical losses. This paper focused on intelligent-based leakage control to replace the traditional method used on campus parameters for backtesting tracing when physical damage becomes visible. However, the result of the study revolves around the design and implementation of a prototype system. The study created a management console as an intelligent decision support system. The realism of the study summarizes various operations by developing intelligent reporting software with data warehouse techniques to facilitate underground leak line pipes tracking.

An important data-driven model is the artificial neural network. Artificial neural networks have been widely used in many domains of chemical processes due to its robustness, fault tolerance, self-adaptive capability, and self-learning ability. For the chemical process with nonlinearity and strong coupling, artificial neural networks can model and control the process well and make up for the lack of traditional PID control technology. As a result, ANN has emerged as a significant positive trend for chemical process control. In this paper, the principle, development history, and common structure of artificial neural networks are first outlined. Then the role of artificial neural networks in chemical process control is introduced in three aspects: improved PID control, improved model predictive control, and for hybrid models. The important effect of artificial neural networks in chemical process control is reflected by comparison. Finally, it is proposed that chemical process control can be more developed by applying more deep learning algorithms and developing multiple neural networks and hybrid models in chemical process control.