Recent Advances in Computer Science and Communications - Volume 15, Issue 8, 2022

Background: In VTBDs (Vision Technology-Based Devices), vision technology is utilized to acquire abundant information about the external environment and process such information to achieve certain functions. They are used in various fields to solve practical problems. Various patents have been discussed in this article, hoping to provide ideas for solving practical problems in the future. Objective: The study aimed to provide an overview of the existing VTBDs and introduce their classifications, characteristics, as well as the stage and trend of development. Methods: This paper reviews various patents, especially Chinese patents related to VTBDs. The structural characteristics, differentiation, and engineering applications of VTBDs are also introduced. Results: The existing VTBDs are analyzed and compared, and their typical characteristics are summarized. The main applications, as well as the pros and cons, in the current development stage, are summarized and analyzed, as well. In addition, the development trend of VTBDsrelated patents is also discussed. Conclusion: VTBDs can be categorized into DsBMV (Devices Based on Monocular Visual), DsBBV (Devices Based on Binocular Visual), and DsBMCV (Devices Based on Multi-Camera Visual). All of these categories exhibit their own relative advantages and disadvantages. Therefore, it is of much importance to analyze the specific problems, followed by selecting appropriate machine vision technologies and reasonable mechanical structures to design VTBDs accordingly.

Objective: The short video applications have achieved great success in recent years. The number of videos being shot and uploaded to these platforms has significantly increased. In this way, mining and recommending videos for users based on their interests has become a challenging problem in these video distribution platforms. Under this case, it becomes particularly important to design efficient video recommendation algorithms for these platforms. In order to solve the problem faced by high sparsity and large scale data sets in the field of media big data mining and recommendation, a heuristic video recommendation algorithm for multidimensional feature analysis and filtering is proposed. Methods: Firstly, the video features are extracted from multiple dimensions, such as user behavior and video tags. Then, the similarity analysis is carried out. The video similarity degree is calculated by weighting to obtain the similar video candidate set and filter the similar video candidate set. After that, the videos with the highest scores are recommended to users by sorting. Finally, the video recommendation algorithm proposed in this paper is implemented by using the C language. Results: Compared with the benchmark, the proposed video recommendation algorithm has improved the accuracy by 6.1%-136.4%, the recall rate by 19.3%-30.9%, the coverage rate by 55.6%-59.5%, the running time by 42.7%-60.4%, and the cache hit ratio by 10.9%-47.4%. Conclusion: The proposed algorithm can effectively improve the accuracy, recall rate, coverage rate, running time, and cache hit ratio.

Objective: Newborn malware has increased significantly in recent years, becoming more dangerous for many applications. So, researchers are focusing more on solutions that serve the defense of new malware trends and variance, especially zero-day malware attacks. The prime goal of our proposition is to reach a high-security level by defending against malware attacks effectively using advanced techniques. Methods: In this paper, we propose an Intelligent Cybersecurity Framework specialized in malware attacks in a layered architecture. After receiving the unknown malware, the Framework Core layer uses malware visualization technique to process unknown samples of the malicious software. Then, we classify malware samples into their families using: K-Nearest Neighbor, Decision Tree, and Random Forest algorithms. Classification results are given in the last layer and based on a Malware Behavior Database; we are able to warn users by giving them a detailed report on the malicious behavior of the given malware family. The proposed Intelligent Cybersecurity Framework is implemented in a graphic user interface that is easy to use. Results: Comparing machine learning classifiers, the Random Forest algorithm gives the best results in the classification task with a precision of 97.6%. Conclusion: However, we need to take into account the results of the other classifiers for more reliability. Finally, obtained results are efficient and fast, meeting the cybersecurity frameworks' general requirements.

Objective: The cotton textile industry, as a competitive industry in China's international competition, is confronting new opportunities and challenges brought about by the growing popularity of mechatronics. To further improve the traditional drive control of combing machines made in China and the automatic level of machines as a whole, some of our cotton textile enterprises have undertaken necessary technical transformations on the combing machines so as to advance the operational efficiency and production technology of domestic textile equipment. Methods: This paper focuses on the basic status and dynamic characteristics of the drive part of the domestic new comber, and analyzes the operation process of the comber and the prominent problems from the production practice. Results: The technically improved drive control system uses an industrial control computer (IPC) as the core of the system, which effectively improves the overall working efficiency of the comber, and improves the production accuracy and production efficiency. Conclusion: The combers that are textile machinery equipment with a comprehensive application of machines, electricity, gases and instruments, play a vital role in enhancing product quality and production efficiency. Highly intelligent and integrated process control, real-time monitoring and accurate data acquisition and data analysis have become the mainstreams in the development of auto-control. Therefore, the commitment of high technology to transform the traditional production mode has also been important research.

Objective: Smart grid is an evolved grid with smart features. The smart grid is responsible for the uninterrupted flow of energy and transmission of data generated by the stakeholders. The versatility and integrity of the entire network completely rely on the protection of data which is generated by the smart meters. These smart meters are deployed at the user’s end. By various possible attacks, smart communication network can be hijacked. Also, existing security approaches are not sufficient for handling the generated enormous amount of data. These issues facilitate the need to work on designing a security algorithm that provides better security and better efficiency in smart meter communication. Methods: This paper presents a proposed model with enhanced security that not only encrypts enormous amount of data but also does it in a very little time. The proposed algorithm is based on a bi-key computational algorithm where two different entities are used as keys. With the use of this algorithm, breaking the originality of message becomes NP Hard problem. Results: The proposed method has been analyzed with the existing cryptographic algorithm and this method has shown promising results. Conclusion: This paper presents a method. The working of the method is simulated in Matlab. The obtained results confirm the adaptability and acceptability of the proposed method for the enormous data.

Background: Social influence estimation is an important aspect of viral marketing. The majority of the influence estimation models for online social networks are either based on Independent Cascade (IC) or Linear Threshold (LT) models. These models are based on some hypothesis: (1) process of influence is irreversible; (2) classification of user’s status is binary, i.e., either influenced or non-influenced; (3) process of influence is either single person’s dominance or collective dominance but not both at the same time. However, these assumptions are not always valid in the real world, as human behavior is unpredictable. Objective: To develop a generalized model to handle the primary assumptions of the existing influence estimation models. Methods: This paper proposes a Behavior Balancing (BB) Model, which is a hybrid of IC and LT models and counters the underlying assumptions of the contemporary models. Results: The efficacy of the proposed model to deal with various scenarios is evaluated over six different twitter election integrity datasets. Results depict that BB model is able to handle the stochastic behavior of the user with up to 35% improved accuracy in influence estimation as compared to the contemporary counterparts. Conclusion: The BB model employs the activity or interaction information of the user over the social network platform in the estimation of diffusion and allows any user to alter their opinion at any time without compromising the accuracy of the predictions.

Objective: The use of smartphones has exponentially increased over the past decade. Nowadays, the use of a cellphone has not just been restricted to make calls, but it's also actively used to connect people throughout the globe through social media and sharing multimedia files over the internet. Smartphones have made these things possible and easily available with just a single touch. But along with this development and digitalization, an increase in the rate of cybercrime has also surfaced, which includes crime like illicit possession, distribution, and modification of multimedia files. Hence, smartphones are seen as a rich source of evidence-based on the crimes discussed. This process is carried out to analyze smartphone’s multimedia files to determine their origin and to verify if the multimedia files originated from the same device or transferred through any process. Methods: An examiner must analyze, recover, and authenticate the files stored in a smartphone device. Android version 9 was used for analysis since it is the most common and abundant platform generally found on most people’s phones. Examination of computer files is achieved by analyzing the file in hexadecimal editor software; the software used in this analysis is HxD. Results: File signature and metadata analysis of smartphones’ multimedia files was performed to render the source of the files. Conclusion: The conducted file signature and metadata analysis clearly stated that by using hexadecimal editor software- HxD origin and source of smartphones’ multimedia file can be rendered.

Background: Electricity is considered as the essential unit in today’s high-tech world. The electricity demand has been increased very rapidly due to increased urbanization,(smart buildings, and usage of smart devices to a large extent). Building a reliable and accurate electricity consumption prediction model becomes necessary with the increase in demand for energy. From recent studies, prediction models such as support vector regression (SVR), gradient boosting decision tree (GBDT), artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGBoost) have been compared for the prediction of electricity consumption and XGBoost is found to be the most efficient method that leads to the motivation for the research. Objective: The objective of this research is to propose a model that performs future electricity consumption prediction for different time horizons: short term prediction and long term prediction using the extreme gradient boosting method and reduce prediction errors. Also, based on the prediction of the electricity consumption, the best and worst predicted days are being recognized. Methods: The method used in this research is the extreme gradient boosting for future building electricity consumption prediction. The extreme gradient boosting method performs predictions for different time horizons(short term and long term) for different seasons(summer and winter). The model was designed for a house building located in Paris. Results: The model has been trained and tested on the dataset and its prediction is accurate with the low rate of errors compared to other machine learning techniques. The model predicts accurately with RMSE of 140.45 and MAE of 28, which is the least value for errors when compared to the baseline prediction models. Conclusion: A model that is robust to all the conditions should be built by enhancing the prediction mechanism such that the model should be dependent on a few factors to make electricity consumption prediction.

Recently, predicting the buying behaviour of customers on e-commerce websites is a very critical issue in business management. This could help merchants understand the tendencies of consumers in choosing and buying products. It has become increasingly common these days that predicting buying behaviour on online systems. Although this is a challenging task, it is an exciting and hot topic for researchers. This article intends to be proposed as a predictive model for buying behaviour on online systems. This model may be represented as a two-stage process. First, a sequence database is built from a shopping cart. Second, the prediction will be performed by using the CPT+, which is an improved model of CPT (Compact Prediction Tree). The main contribution of this paper is that we proposed a solution for predicting buying behaviour in the e-commerce context (a case study of an e-commerce company). The core prediction is mainly based on sequence prediction, in particularly, CPT+ (Compact Prediction Tree).