Asian Journal of Research in Computer Science
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792 research outputs found
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Block Chain and Cryptography based Secure Communication System
No full textThe block chain is a cutting-edge technology that reduces risks by allowing vital tasks to be decentralized procedure, while retaining a high level of security. It eliminates the dependable intermediaries from the network. The block chain technology, which records all previous transactions, these accessible to all network nodes. The goal of our paper is to create a block chain-based secure communication system. We also explain about why block chain would improve communication security? It also proposes a model design for block chain-based messaging that focuses on training the performance. Security of data recorded on the block chain, using a smart contract to identities and their associated public keys, as well as validating the user’s certificate
Specific Attenuation Estimation in Perturbed Radio Wave Propagation Using Artificial Neural Networks
No full textPath loss modeling is a crucial consideration in radio engineering for wireless networks. Over the years, diverse techniques have been implemented in attempts to accurately predict path loss across a given terrain. In this study, path loss predictors created on the bases of artificial neural networks (ANN) were used to estimate path loss across a rural section of the Nigerian middle-belt grassland. The ANN structures considered were the Generalized Regression Neural network (GRNN) and the Radial Basis Function Neural Network (RBFNN), which exhibit a few differences and similarities. These ANN based predictors were trained, validated and tested for path loss prediction using path loss values computed from received power measured at 900MHz from six Base Transceiver Stations (BTSs) situated along the rural terrain. Findings show that the RBFNN predictor with a Root Mean Squared Error (RMSE) of 5.17dB and the GRNN with 4.9dB are slightly more accurate than the COST 231 Hata model with 6.64dB, while the Hata-Okumura with 25.78dB is simply not suitable for the terrain under investigation. Overall, the GRNN, which proffers a 26.21% improvement over the COST 231 Hata is recommended for the terrain in question
Integration of AI for Routine Tasks Using Salesforce
No full textAims: In the previous decade, developments in machine learning techniques have drawn interest from the literature and corporate organizations to artificial intelligence technologies. However, despite the immense promise of AI technology for problem resolution, there are still limitations with practical application and a lack of expertise in employing AI strategically to produce business benefits. Customer Relationship Management (CRM) has recently experienced substantial developments. Companies have implemented AI-based CRM to effectively react to client inquiries and increase customer loyalty.
Study design: Qualitative analysis.
Methodology: We have selected 15 publications from the research database for further investigation. The rapid growth of modern sales technology literature has resulted in a rich but fragmented representation of what sales technology is, raising the question of how its position within the sales process can be effectively defined.
Results: The results of the literature review enabled the author to recognize three major subfields of AI literature within the CRM domain (AI and machine learning techniques used for CRM activities, strategic management of AI-CRM integrations, and AI integration in Salesforce) and gather promising future development paths for each of these subfields. This study also proposes a three-step theoretical framework for AI deployment in CRM, which may help scholars further improve their expertise in this sector and managers create a suitable and consistent approach.
Conclusion: A conceptual framework is offered, with four sources of value creation discussed: (i) decision assistance; (ii) consumer and employee involvement; (iii) automation; and (iv) new services and products. These findings add to both conceptual and administrative views, with several prospects for developing new theories and management techniques
Securely Compressed Extensive Text Messages: The Interactive Mobile Learning for Distance Education in the COVID – 19 Pandemic
No full textThe COVID-19 pandemic has made a massive impact on the higher education system in the present globe. Universities had to close abruptly due to keeping up social distancing to prevent the spread of the Coronavirus in the world community. As a result, the existing face-to-face education style is rapidly shifting to distance education environment through the mobile technology. Here, an extensive text messages, one of the most popular applications, make a significant contribution on higher education. However, in this scenario, the single text message is not sufficient in various academic activities such as sending short lecture summaries, feedbacks, assessment timetables, useful web links, detailed news, notices, and examination results to achieve educational prospects to obtain better results in distance education. Moreover, standard SMSs do not provide message confidentiality when sending sensitive data, such as examination results. Here, we propose a novel technique for extensive text message compression to create an interactive mobile learning environment for distance education in the present pandemic situation. This proposed secure mechanism provides message confidentiality, authenticity, and integrity with cryptographic protection. Initially, the teacher inserts academic-related information as an extensive text message. The extensive text message is then compressed and secured with the initialization vector and the secret key in the proposed mechanism. Finally, the securely compressed single text message transmits to students who will decompress it into its original form on their mobile devices. The result shows that students\u27 extrinsic motivation in their distance learning environment effectively and efficiently in the COVID-19 pandemic
Unveiling the Potential of Artificial Intelligence and Machine Learning in the 5G Network Landscape: A Comprehensive Review
No full textExploring successful case studies, scholars and industry experts concur that artificial intelligence (AI) and 5G technology, as holistic solutions, exhibit remarkable efficiency. The integration of AI has notably resolved wireless communication dilemmas that defy traditional modeling approaches, substantially diminishing technological uncertainties. Furthermore, 5G technology is poised to amalgamate communication, computation, sensing, and control across diverse industries. However, the convergence of these capabilities also introduces complexity, a challenge that can be effectively addressed through the application of artificial intelligence and machine learning functionalities. These cutting-edge technologies not only ensure data security but also satisfy stringent latency requirements while minimizing the burden on both communication and computation resources. In this context, a thorough examination of machine learning and artificial intelligence applications geared towards optimizing communication, computation, and resource allocation within the realm of 5G technology holds paramount significance.
In pursuit of this objective, the study endeavors to offer a comprehensive outlook on the current landscape of artificial intelligence research within the 5G domain. By scrutinizing recent studies, we aim to encapsulate the contributions and prevailing trends associated with these technologies. Ultimately, our aim is to empower researchers and industry practitioners with insights that will facilitate informed decision-making when selecting the most suitable machine learning and artificial intelligence approaches for their endeavors
Comparison of Data Fluctuations that Lead to Cyber Security Attacks: A Difference between Surface, Deep and Dark Net
No full textThe term "darknet" refers to the address space on the internet that is not being used, and users do not anticipate that this area will interact with their machines. Darknet is a source of cyber intelligence. In order to develop network security, it is necessary to conduct studies of the many dangers that comprise the network. In this research, we offer brand new machine learning classifiers that go by the name stacking ensemble learning. Their purpose is to evaluate and categorize darknet traffic. This novel approach employs predictions created by three different base learning techniques in order to deal with the issues relating to darknet attacks. The software was validated using a dataset that had more than 141,000 records and was derived from the CIC-Darknet 2020 database. The findings of the experiment indicated that the classifiers used in the investigation were able to easily differentiate between benign and malignant traffic. The classifiers have the ability to efficiently recognize known as well as unknown threats with a high degree of precision and accuracy that is greater than 99% in the training and 97% in the testing phases, with increments ranging from 4 to 64% based on the algorithms that are currently in use. As a consequence of this, the suggested system will become more reliable and accurate as more data is collected. Additionally, in comparison to other AI algorithms already available, the suggested system has the lowest standard deviation
Perceived Factors Analysis for Depression and Suicidal Ideation among Bangladeshi University Students Using Association Algorithm
No full textDepression stands as a prominent and prevalent mental health issue, representing a significant global public health concern. Its emergence can be attributed to diverse factors. Suicide stands as a prominent global cause of death, eliciting concern on a widespread scale. This study was to analyze the perceived factors for depression and suicidal ideation among Bangladeshi university students in Bangladesh. There are so many factors such as Loneliness, Hopelessness, Helplessness, Relationship Issues, Grade problems, Academic Pressure, Parental problems, Money problems, Social Comparison, Social Media Influence, Family Expectations, Lack of Sleep, Uncertain Future, Health Issues, Bullying, Substance Abuse and Unemployment etc. These factors vary among male and female students. Apriori association algorithm were used to calculate support, confidence and lift of factors sets. Frequent factors sets and relationship were found from the work using Apriori association algorithm. The work is an online survey-based study about psychological and stress status of participants and statistical analysis is used for concluding the results. The research participants are Bangladeshi university students, Data collection carried out by online questionnaire. The findings from data analysis indicated that academic pressure (72.41%), uncertain future (56.32%), hopelessness (48.28%), family expectation (47.13%), financial crisis (42.53%), loneliness (41.38%) and unemployment (37.93%) are the key factors. The prevention of suicides is achievable. Hence, identifying depression and forecasting the potential for suicide risk serves as a means to prevent instances of self-harm within the university student population
Reflective Safety Clothes Wearing Detection in Hydraulic Engineering Using YOLOv3-CCD
No full textThe construction site of the hydraulic engineering has a high danger factor and the correct wearing of reflective safety clothes ensures the safety of workers. Therefore, the detection and testing of reflective safety clothes wearing is an important task in the construction site of a hydraulic engineering. However, the traditional manual supervision strategy has the problems of low efficiency, narrow scope, and poor real-time performance in complex working conditions. Based on YOLOv3 that is a classical target detection model, this paper proposes a reflective safety clothes detection algorithm (YOLOv3-CCD) based on an attention mechanism and an improved loss function. By adding the CA (Coordinate Attention) mechanism module to the backbone network, the characterization ability of the target feature is enhanced, so as to solve the Long-Term dependencies problem in the detection process; The loss function is changed from IOU-Loss (Intersection Over Union Loss) to CIOU-Loss (Complete-IOU Loss), so that the network takes the aspect ratio into consideration when selecting the prediction box, which improves the accuracy of target positioning; In the post-processing of the algorithm, we improved NMS (Non-Maximum Suppression) to solve the problem of dense target detection being missed. Experimental results show that compared with the original YOLOv3 network model, the algorithm has stronger robustness and the overall detection accuracy is 1.8% higher than that of the original network. Moreover, the detection speed is 32 frames per second, which is faster than the original network
Machine Learning based Employee Attrition Predicting
No full textNow a day’s variety of reasons for job resignations due to this, we have to take different types of measurements for prediction of job seekers. They have different reasons for not doing jobs well and fell like pressure. Many employees suddenly come to an end of their service without any reason. Techniques of machine learning have full-grown in fame in the middle of researchers in current years. It is accomplished of propose answer to a broad range of problems. Help of machine learning, you may produce prediction concerning staff abrasion. So machine learning model we will be using TCS employee attrition a genuine time dataset to train our model. The aim of this study is to at hand a comparison of different machine learning algorithms for predict which employees are probable to go away their society. We propose two methods to crack the dataset into train and test data: the 75 percent train 25 percent test split and the K Fold methods. Three techniques are three methods that we employ to train our model for correctness comparison, and we will compare the exactness of the models generate using these three Boosting Algorithms
Security Concerns and Solutions for Enterprise Cloud Computing Applications
No full textComputing in the cloud is now one of the most interesting developments in technology owing to the fact that it is both flexible and cost-effective. Despite the enormous stakes, the implementation of cloud computing into an existing business model creates significant safety risks. Enterprises need to foresee and account for possible risks, threats, vulnerabilities, and mitigation strategies before using cloud computing. The concept of cloud computing may be simplified by dividing it into three separate models: "Infrastructure as a Service," "Software as a Service," and "Platform as a Service." It also talks about the current crop of cloud-based security tools. Before using this technology, we think businesses should assess their security risks, threats, and existing defences. We’ve also discussed the advantages and drawbacks of cloud computing as well as places where it might be used for information risk management