ISSN: 2393-8749 Scientific Journal Impact Factor (SJIF): 6.04 Index Copernicus (ICV: 60.42) Journal DOI no.: 10.37591/RTPC
STM JOURNALS ( Consortium eLearning Network Pvt Ltd)
Big Data: How Universities Use Big Data for Admission
The advent of big data has transformed the manner in which businesses handle, store, and oversee data. In the realm of extensive analytics, cloud computing has emerged as an indispensable resource, offering scalable and budget-friendly solutions for managing substantial data loads. This paper delves into the influence of big data on the university admission process, commencing with a definition of big data. It explores the advantages of employing big data for data processing and storage, alongside addressing the challenges associated with its implementation. Big data has seamlessly integrated into the domain of data science, presenting a methodology that addresses the dynamic intricacies of developing analytics applications from large-scale datasets. To operate with Hadoop, this paper attempts to integrate how does big data is useful in the admission process and how it enhances the strength of admission of students. For the same, big data can play a significant role in the university admission process by helping institutions make more informed decisions and improve the efficiency of their admissions processes.
Copyright (c) 2023 RECENT TRENDS IN PARALLEL COMPUTING
An Integrative Approach to Implementing Green Cloud Computing, Artificial Intelligence, and Big Data Management in Modern Library Systems
The expansion of digital resources and the integration of emerging technologies have significantly transformed modern library systems. With the rise in digital collections, libraries are now dealing with a vast amount of data that needs efficient management and storage. This research will explore the role and benefits of leveraging green cloud computing, artificial intelligence, and big data management to improve efficiency and sustainability in library science. This research explores the integrative implementation of green cloud computing, artificial intelligence, and big data management to enhance the efficiency, user engagement, and environmental sustainability of modern libraries. These libraries house a broad range of resources, including music files, research content, reading articles, and other entertainment material.
The Future of Multi-cloud Infrastructures in Data Engineering for Challenge of Handling Large and Dynamic Datasets
This paper explores the future of multi-cloud infrastructures in data engineering for the challenge of handling large and dynamic datasets. It delves into the opportunities and challenges presented by multi-clouds, focusing on their role in managing large volumes of data and complex computational tasks. The paper discusses the advantages of multi-clouds, such as manageability, flexibility, and cost-effectiveness, and their potential to enhance the efficiency and accuracy of recommender systems. It also addresses the challenges associated with implementing multi-clouds, including data privacy, resource management, and compatibility issues. The paper concludes by highlighting the promising future of hybrid cloud infrastructures in driving significant advancements in data engineering and their potential impact on various sectors.
Quantum Leaps: How Quantum Computing Shapes the Evolution of Artificial Intelligence
Quantum computing leverages quantum mechanical phenomena to enable powerful new forms of computation. With the ability to dramatically exceed the capabilities of classical supercomputers on select complex problems, many researchers anticipate revolutionary impacts of quantum computing on a variety of fields, including artificial intelligence. This research proposal outlines an investigation analyzing the intersection of quantum computation and artificial intelligence. The key research questions explore how quantum algorithms and hardware may practically enhance future artificial intelligence, along with a rigorous methodology to evaluate quantum techniques for machine learning and neural networks tasks against classical benchmarks. The anticipated results include demonstrable advantages of quantum machine learning over classical approaches, providing unique insights into forthcoming advances at the leading edge of artificial intelligence as quantum platforms continue maturing. Ultimately, this research aims to elucidate the emerging symbiotic relationship between quantum physics and artificial intelligence which could shape the landscape of intelligent technologies in the coming decades.
The Future of Cloud Computing and Its Potential Impact on Business
Cloud computing continues to evolve as a pivotal technology shaping the modern business landscape. This article delves into the anticipated trends and potential implications of cloud computing for businesses. It outlines a comprehensive journal structure, including key topics such as the current state of cloud computing, emerging trends, impact on business operations, implications for business growth, challenges, future projections, case studies, and concluding recommendations. Furthermore, it offers a list of valuable reference documents and additional resources, providing a thorough understanding of the economic, strategic, security, compliance, and application development aspects related to the future of cloud computing. By examining these insights, businesses can effectively prepare for the dynamic changes brought about by the advancing cloud computing ecosystem, ensuring they remain competitive and adaptive in an increasingly digital and interconnected world.
An Application of Recommendation Systems to Cluster-based Pattern Recognition
Information overload is a common problem faced by internet users; so, the recommendation system is becoming increasingly important to this service. Collective filtering is used by recommendation systems to offer recommendations to individuals or for products based on their shared interests. Data or people should be grouped into clusters so that they have more in common with those in the same cluster and stand out from those in other clusters. Parameters like average, correlation, mutual information, etc. are used in pattern discovery and matching. To extract and organise patterns into categories, PR may be used as a classification approach. PR is a way of categorising. Public relations (PR) is a hybrid method. In this survey, we investigated data mining, clustering, recommendation systems, and pattern recognition. We defined numerous clustering and pattern recognition algorithms and discussed their applications.
Personalized Or Non-Personalized Recommendation System
A Comprehensive Examination of Cloudflare's IP-based Distributed Denial of Service Mitigation
This study dives deep into the world of DDoS (Distributed Denial of Service) attack prevention, with a keen focus on Cloudflare. As a powerhouse in the cybersecurity world, Cloudflare uses smart tactics, like acting as a reverse proxy, to keep online services safe from DDoS attacks. What does Cloudflare do exactly? It acts as a bouncer, checking all incoming server traffic to weed out harmful requests. This unique position lets Cloudflare effectively spot and deal with DDoS attacks. How does it do this? By looking at several factors, including the reputation of an IP, how often requests are coming in, and the information in HTTP headers. By doing this, Cloudflare can distinguish between legitimate user traffic and nasty attack traffic. What occurs when a DDoS attack is initiated? Cloudflare's robust setup jumps into action, soaking up the attack traffic and keeping it away from the target server. This means that regular users can keep accessing the server, keeping the service running without a hitch. One of the coolest things about Cloudflare's system is that it can scale up to deal with huge DDoS attacks that would otherwise crash a server's capacity. In this study, we will look at how Cloudflare's defense mechanisms against DDoS attacks work, particularly its system for managing IP reputation, how it limits the rate of requests, and how it inspects HTTP headers. We will also have a chat about the pros and cons of using Cloudflare for DDoS protection. The aim of all this? To provide solid insights for organizations looking to beef up their protection against DDoS attacks.
Addressing Security Challenges in Cloud Computing for Healthcare Systems: A Comprehensive Survey
Mobile cloud computing (MCC) emerges as an innovative technology revolutionizing mobile web services by combining the domains of mobile computing, cloud computing, and wireless networks. In the healthcare sector, MCC plays a pivotal role in transforming medical services, offering novel facilities and services to patients and caregivers alike. The adoption of MCC by healthcare professionals (HCPs) has led to a surge in medical software applications designed for these platforms, consequently enhancing medical services while reducing costs and fostering competition among healthcare providers. The integration of MCC has proven beneficial for HCPs, aiding them in making informed decisions and providing improved patient care. Utilizing cloud computing services, MCC makes it easier to address the continuously expanding demands of the healthcare sector. However, this advancement has not been without challenges. Constraints related to network bandwidth and mobile device capacity have introduced concerns regarding energy consumption and latency delays. Numerous factors influence the performance of MCC systems, necessitating a comprehensive investigation to optimize their efficiency. Furthermore, addressing security and privacy issues associated with MCC in healthcare is of paramount importance. Despite its significant role in cloud computing, the domain lacks an in-depth study of these concerns. This study aims to bridge the gap by conducting a systematic analysis of 43 relevant papers published between 2010 and 2022 in reputable national and international journals, including Springer and IEEE. Through this study, we endeavor to explore the challenges faced by mobile cloud computing in healthcare and propose strategies to enhance healthcare services in the cloud environment.
Context-aware Multipath Load Balancing Routing for RPL in IoT
The Internet of Things (IoT) is revolutionizing the world with unprecedented levels of insights and intelligence, connecting the physical world to the digital age. In order to provide dependable and efficient routing, IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) has been adopted as the foundation network for the Internet of Things (IoT). Although many IoT routing criteria have been met thanks to RPL's distinctive features, load balancing and congestion control are still the outliers. A context-aware Multipath Load Balancing Routing Protocol (CAMLB-RPL) is proposed that balances the traffic load across multiple paths, minimizing congestion, and increasing network reliability and lifetime. CAMLB-RPL leverages the Context-aware Load Balancing Routing metric (CALBR) to evaluate the queue occupancy and expected lifetime for the chain of parent nodes in the network. A context-aware Load Balancing Objective Function (CALB-OF) is also presented to identify the best-optimized load-balanced route during the parent selection process considering the CALBR and the Child count (CC) metric. A multipath routing strategy is employed to mitigate the congestion due to burst traffic in the network. The performance of CAMLB-RPL was compared to the typical RPL objective functions OF0 and MRHOF using the Cooja simulator of Contiki OS 3.0. CAMLB-RPL showed outstanding results, with power consumption decreasing by 39%, end-to-end delay decreasing by 36%, queue loss ratio declining by 68%, packet receiving rate increasing by 12.5%, and network lifetime increasing by 69%, on average.
Cyberattack Detection System in Private Cloud
As cloud computing adoption in colleges continues to rise, the security of private cloud systems has become a paramount concern. Data breaches resulting from cyberattacks can inflict severe damage to a university's revenue and reputation. This research proposes a novel machine learning-based cyber threat detection system tailored to the university's private cloud environment. The system's main objective is to continuously monitor the cloud infrastructure and employ advanced machine learning algorithms to analyze network traffic, and identifying and preventing unusual activities that may indicate potential cyberattacks. By leveraging the potential of machine learning, this innovative system aims to enhance the university's cybersecurity protections. It considers the dynamic and evolving nature of cyber threats, enabling real-time detection and proactive measures against malicious activities. The integration of cutting-edge machine learning models and feature extraction techniques empowers the system to identify patterns of anomalous behavior, even in the face of sophisticated attacks. Essential elements of the suggested system encompass the analysis of network traffic, the identification of anomalies, and the incorporation of threat intelligence. Through the analysis of network packets and access logs, the system can effectively detect signs of unauthorized access, data exhilaration, and other cyber threats. Additionally, threat intelligence feeds provide the system with up-to-date information on emerging threats, enabling quick responses to potential risks. Moreover, the system's implementation adheres to privacy and data protection regulations, ensuring secure handling of sensitive information within the private cloud environment. Regular updates and adaptive learning capabilities enable the system to evolve with changing cyber threats, ensuring continued robustness in the face of new challenges. In conclusion, the proposed machine learning-based cyberattack detection system presents a powerful solution to safeguarding the university's private cloud infrastructure. By promptly detecting and mitigating potential cyber threats, the system acts as a proactive defense mechanism, safeguarding valuable data and preserving the university's reputation in the ever-evolving landscape of cyber security.
Using a Lightweight Cryptographic Algorithm to Improve Cloud Computing Data Security
The use of cloud computing technology allows for the storage of vast amounts of data. It is now crucial to protect the integrity, confidentiality, privacy, and handling processes of cloud computing data from attackers. To improve data security in cloud computing, a novel, lightweight cryptographic algorithm was put forth in this study. The algorithm, which is a 16-byte or 128-bit block cipher, requires a 16-byte or 128-bit key to encrypt data. The algorithm's complexity is increased by incorporating Feistel and substitution-permutation architectural techniques. The algorithm uses logical operations such as XOR, XNOR, shifting, swapping to get Shannon’s theory of diffusion and confusion. Moreover, this algorithm offers flexible features regarding the secret key length and the number of turns used in the encryption process. This proposed algorithm provides a strong security level, low computation cost and less processing time compared with other cryptographic algorithms used in cloud computing.
Study and Analysis of Components and Impact of AWS on Cloud Computing
This study aims to analyze the components and impact of Amazon Web Services (AWS) on cloud computing. AWS is one of the most widely used cloud computing platforms in the world, providing a wide range of services such as computing, storage, networking, and databases. In this study, we will examine the components of AWS, including EC2, S3, RDS, and Lambda. EC2 provides scalable computing capacity in the cloud, while S3 offers secure and durable object storage. RDS is a managed relational database service, and Lambda is a serverless computing service that allows users to run code without managing servers. Furthermore, this study will also explore the impact of AWS on cloud computing. AWS has significantly transformed the way businesses operate by reducing the cost and complexity of IT infrastructure. It has provided organizations with the ability to scale resources as needed, thus enabling them to adapt to changing market demands quickly. Additionally, AWS has enabled businesses to innovate and develop new products and services faster by providing a platform for experimentation and testing. The study will conclude by summarizing the components and impact of AWS on cloud computing. It will highlight the benefits of AWS, including scalability, cost-effectiveness, and innovation, and the challenges that organizations may face when adopting AWS. Overall, this study will provide a comprehensive understanding of AWS and its impact on cloud computing.
A Securing Infrastructure Against Signature-based Assaults Using Karnaugh Detection Systems
Cloud computing is one of the efficient business models for offering customers online services. Approximately 81% of businesses worldwide rely on the cloud; however, one of the most pressing issues is cloud security. Malware of several kinds can cause havoc in the clouds. Attacks on the cloud might come from both the inside and the outside. The signature attacks that can occur from outside the cloud server will be the main topic of this study. Brute Force and Signature-Threats are the two outside attacks that attack cloud servers the most. Numerous procedures can stop brute force assaults, but few solutions can detect signature attacks from the network. In this study, to secure data from attacks, a secure architecture will be created, and externally signature-based attacks will be done on the cloud server. The development of an effective method to identify signature attacks coming from outside the cloud is structured. The performance of the mechanism will then be determined using the various keys implemented on a Karnaugh Map, and the tool's effectiveness will then be assessed.
Role of Data Science in Enhancing Privacy in E-Healthcare Systems
The test is that while learning and data science are basic empowering agents of productivity and viability in present day associations, exploiting them often implies entrusting delicate data to outer sellers. The overall privacy model will in general be binary: either the data science as a specialist resource has the association's full trust and gets immediate admittance to the data, or they do not have the trust and they get no entrance by any means. This "full trust" model makes an either/or circumstance that sets associations up to need to pick security or advancement. Fortunately, there are a few promising data privacy innovations that can facilitate that strain. These are "fractional trust" models that give more space to both security and advancement. In this study we try to throw sufficient light on the various works related to privacy in healthcare systems and also discuss the various dimensions of privacy where suitable data science mechanisms are been deployed for the greater good of patients who are always worried about their sensitive data being leaked.
Inligence Cloud: Cloud-based Financial Analytics Platform
Data analysis is an essential aspect of modern research and business decision-making. With the increasing amount of data being generated, the need for efficient and effective data analysis software is becoming more crucial. This software offers a range of features, including data visualization tools, statistical analysis capabilities, and machine learning algorithms. Users can import data from various sources and formats, including spreadsheets, databases, and CSV files. The software’s interface is simple and very user-friendly, allowing users to easily navigate through the different functions and tools. The visualization tools include a variety of charts and graphs, such as scatter plots, histograms, and box plots, which can be customized to suit the user’s needs. Overall, this data analysis software is a comprehensive tool for exploring and analyzing data, offering a range of features and capabilities to suit the needs of both novice and experienced users.
Data Anonymnization for Privacy Protection Using Deep Learning Techniques
The majority of real data are unbalanced, which makes them challenging to classify. Unbalanced data has been the subject of some research. However, deep learning has not taken into account how to manage such data. In this paper, we suggest deep learning-based data mining with imbalanced data for privacy protection. We discuss and analyse the aspects of current privacy-protection data mining as well as a deep learning based anonymizing tool. Analyzing data anonymously does not affect deep learning's accuracy, according to experiments employing various anonymization technologies. Undersampling and upper-sampling are both employed for imbalanced data; however, it is unknown whether they have a tendency to lower data accuracy because of anonymization.
Copyright (c) 2022 RECENT TRENDS IN PARALLEL COMPUTING
Exploring the World of Big Data: Understanding Impact and Possibilities
Big data refers to the management and analysis of large and complex data sets that cannot be handled by traditional data processing software. It involves techniques for extracting useful information from these data sets, and involves challenges such as data storage, analysis, sharing, and privacy. The three main characteristics of big data are the large volume of data, the variety of data types, and the fast pace at which data is generated and processed. Rather than sampling, big data often involves observing and tracking data as it occurs, and the data sets are often too large for traditional software to process within a reasonable amount of time and value. Advancements in big data, artificial intelligence, and data-driven innovation have the potential to bring significant benefits to society and various industries. However, if these technologies are not used in an ethical and socially responsible manner, they can lead to the misuse of data and violation of privacy and ethical standards. This phenomenon is referred to as the "creep factor" of big data, and it must be addressed urgently, especially as we move towards a "datafied" society where data collection and processing are becoming faster, cheaper, and accessible. While using big data in a transparent and ethical framework can facilitate sustainable development, using it without such a framework, poses numerous threats and ethical challenges. For instance, new surveillance tools and data gathering techniques, including group privacy, high-tech profiling, automated decision-making, and discriminatory practices, can impact privacy and lead to unfair practices. In today's society, life-changing opportunities are often determined by predictive algorithms applied to data, and it is crucial to ensure the fairness and accuracy of these scoring systems and decision-making processes to avoid stigmatization and bias. Moreover, there is a risk of "social cooling," which refers to the long-term negative effects of data-driven innovation, such as selfcensorship, risk-aversion, and lack of free speech, generated by intrusive big data practices lacking an ethical foundation. In addition, the increasing volume, speed, and variety of data sources collected in IoT environments raise questions regarding data ownership and other hurdles that require further investigation.
Big Data and HR Analytics in Talent Management: A Study
The discussion of the potential talent analytics provides HR practitioners is the goal of this article. The ability to analyses enormous volumes of data has become much more accessible over decades, and businesses are now using talent analytics to manage their workforce. There will be a lot of studies on how talent analytics may improve organizational decision-making. Majority of businesses use databases to hold complex data. A database is a great tool for displaying data and running reports, and they are typically housed in the Cloud. Big data exploitation, or use of enormous amounts of structured and unstructured data produced by organizations’ everyday operations, has gained a lot of popularity among businesses over the past several years. Talent can signify different things to different people and in different situations. The idea of a "data lake" has gained significant traction in the field of big data management. Data warehouses do not require data to be converted in order to meet a specific data model, allowing businesses to store a variety of data kinds at little expense. Instead of analysis, data warehouses are incredibly helpful for discovering new insights. Big data may assist HR teams in better understanding their staff and implementing better employment policies, as demonstrated by the way that marketing department is utilizing it to know the viewpoints of their customers. This essay has compared big data with human resources management.
General Data Protection Regulation (GDPR)
Analyzing the Best Framework for Web Development
Examine the field of World Wide Web (WWW) webpage development to provide a specific solution that can assist with the development cycle. Currently, web apps are extremely complicated and sophisticated software products, with user perception of application quality being one of the most important variables influencing the application's success or failure. Lately, design patterns in the framework of web conceptual modelling methodologies have been offered as a way to provide web designers and developers with proven viable solutions that may be applied in multiple situations when the correspondent difficulties arise. This study looks at the various aspects that IT consulting firms should take into account before implementing a fresh web framework. This is done by putting out a web framework adoption model that takes into account the most important technical and nontechnical considerations for practitioners. The purpose of this study was to provide guidance and aid in enhancing how an IT consulting firm might choose a web framework in a systematic and trustworthy manner in order to maximize cost effectiveness and provide high-quality web applications.
A Review Paper Based on Methodologies and Applications of Data Mining
Data mining is like extracting record from a heap of data. Understanding database concepts is vital for data mining. Knowledge Discovery in Databases (KDD) is used to discover crucial data-related information. Our motive is being inundated with exceptional types of information science statistics, environmental records, financial records, and mathematical records. Summarizing the information manually is not possible because of the notable data boom inside the age of community and records trade. This carried out study realizes the principles of record mining and shows studies on cumulative uncertainty into data mining significantly boost new techniques for combined unpredictable management into records mining.
A Comprehensive Study in Lattice Cryptography
A significant portion of the new advancement in lattice-based cryptography will be covered in this comprehensive study. There is sufficient research work proving to be conclusive regarding the strength of lattice-based cryptography. Even in adverse security design scenarios, the degree of security provided by the lattice-based encryption proves to have a fair chance to be considered for critical applications. With more and more modern-day transactions demanding secure nature in business aspects, the current cryptographical systems are becoming expensive to maintain and not providing absolute security or privacy against technologically adept adversaries. Hence the need has come to design more robust secure system. This is forcing the security designers to work in the exciting area of lattice-based cryptography with computationally complex mathematical strategies. These are complex systems which will instill trust of highest degree in current scenario where the malicious attackers will try to use quantum computing in their highest ability to break the security of the encryption systems. Cryptography with lattice-based systems is based on techniques with lower computational complexities which have paved the way for research directions in homomorphic encryption. With the use of public key cryptography becoming expensive day by day for maintenance purposes, the advent of lattice-based cryptography is inevitable. Due to the threats arising during poor system design of cryptosystem, the need of lattice-based cryptosystems are increasing every year. Many organisations are thinking to move their security paradigm towards a more computationally complex encryption system. For provably worst-case hardness of security problems, lattice-based principles can be more secure, and they can act as versatile objects for powerful cryptographic frameworks.
Quantum, Encryption, Security, Lattice, Cryptography
Big data is identified by extreme measure of information generated at a standard premise by devices and internet-based sources. To construct valuable solutions for specific fields, this data can be treated and dismembered. Different techniques of computational and data analysis have been identified for usage in this domain. It has provided upward advancement of voluminous data processing registering models and tools. In traditional systems and innovations, the capability and handling needs are overpowering. Subsequently, there is an infrastructure need that can adjust the capacity and capability of managing as per the developing data measurements. As a potential answer in this concern, cloud computing fills in the gap. Processing Big data in the cloud environment has its own challenges. This study focusses on user’s concerns about significant structures used in medium to large organizations.
Cyber security, big data, data analytics, hadoop technology, cloud computing, cyber crime
Cloud Computing Technology and its Impacts on Libraries
The library is a knowledge resource centre in society. It serves as a repository for knowledge that future generations will use. Disseminating well-organized knowledge is the prime responsibility of the librarian. Information and communication technologies are being applied by library professionals to provide library services smoothly and effectively. Cloud computing is a pool of connected computers for applications, data, file storage, and usage. This article aims to give a general overview of cloud computing technology and how it can benefit libraries. It reveals the contributions and impacts of cloud computing in libraries. It mainly focuses on various aspects of cloud computing including cloud components, models, services, features, advantages, cloud hosting providers, and areas for applying cloud computing in libraries.
Secure Cloud Computing Using Cryptographic Algorithms
Cloud computing has been a game changer altogether in the field of IT communications and has enabled to widen the scope and horizon of the Software Engineering and IT revolution. So, more and more studies are being conducted on cloud computing. The study conducted by us focuses on cloud computing. From a promising commercial concept, cloud computing has developed into one of the IT industry’s fastest-growing segments. IT Organizations have voiced worry over important problems (like security) that are present given the broad putting cloud computing into practise. These kinds of issues arise from the fact that data is kept in remote locations, away from. It can actually be kept anywhere, including the customer’s location. Security, in particular, is one of the most contentious issues in this field. To address and solve this issue, we would use cryptographic algorithms to present a secure cloud simulation. In this study, we outline a method for protecting data using various compression and encryption algorithms while also keeping its location a secret from the users who store and access it.
Cloud computing process, types of deployment models, file-hosting, Decryption-encryption, Denial of Service, Counter measures, cryptography, cloud security, AES and DES, Triple-DES.
Managing and Maintaining Container inside Kubernetes Clusters
It is an open-source orchestration tool developed by Google, its historical name is bought after it has been changed to Kubernetes, now it is part of the Cloud Native Computing Foundation. Kubernetes is mostly used for maintaining micro servers and containers. Here, container refers to an existing application that has been containerized using Docker and can only be implemented within Kubernetes. Micro servers are used when there are multiple little services that convert one large application into a container application. The advantage of this technique is its resilient infrastructure and zero-time deployment. One application may frequently be deployed inside of Kubernetes and utilise three or four replications therein. It automatically rolls back scaling within Kubernetes in case of any issues. If a container fails, metrics-based scaling, replacement, and restart are automatically applied.
Kubernetes, Cluster Kubernetes, micro server ,cluster replication, role back
The goal of this research work was to highlight the impact of the Coronavirus (COVID-19) on the entire world. This has had a wide-ranging and far-reaching impact across industries, particularly in the information technology (IT) sector. Basically, before COVID wreaked havoc on the world, how were IT professionals working, what was the state of the IT sector, how much credibility it had in the economy and when COVID disrupted our lives what was the impact of COVID on the IT industry and on the economy. In this review, we examined some of the consequences we have observed in the IT services business, the responses organizations have made and made our predictions for future challenges and transformations. This study explains how the IT sector is coping after the pandemic has stabilized the situation and what effect it has on people who want to work in the IT industry. In addition, we circulated a survey form for working IT professionals that included some questions about this topic, and we have provided a detailed analysis of the form in this study.
Covid-19, Work From Home, IT professionals, IT Industry, Digitalization
The Internet of Things (IoT) is not a new concept or industry, but recent technological advancements, particularly in communication technology, have elevated IoT to the next best thing for the industry and heralded a new era in human-technology interfacing. Sending a wireless communication to turn on or off your transistor would have won an award a decade ago; now, using a mobile to control your home temperature will almost certainly result in a support ticket if there is any delay in control execution. Everything can be connected to the internet, all communications between relays, sensors can work seamlessly, and complete process automation be enabled via Internet of Things (IoT). It is important in many fields, including healthcare, manufacturing, agriculture, transportation, smart homes and so on, and it improves our daily lives. It serves as a shield for all factors such as protocols, key elements, technologies, and so on. IoT has many capabilities and mechanisms, but security concerns are slowing down the era.
Internet of Things (IoT), Industrial Internet of Things, Web-of-Things (WoT), Machine to Machine (M2M), Connected Devices, Smart Devices, Smart Cities, Smart Wearables
Role of Virtualization in Cloud Computing
Appropriate arithmetic in cloud computing is rapidly expanding, and there is no doubt that it is the front-line advancement that people will use at any time and in any place. IT is being developed today through hazes with virtualization. Disengaged from its notoriety, it has two or three businesses that are turning into bundles for its dynamically wide assembling. As of now, the study has been made on virtualization concerns. Cloud computing technology is one of the most significant milestones in ushering in next-generation technology and boosting the business and IT sectors. It aids in the resolution of issues such as data loss, data access when needed, and data security. This technology focuses on cost reduction, hardware reduction, and the pay-as-you-go model. Virtualization is the process of creating a virtual image of storage devices, servers, or network resources so that they can be used on multiple machines at the same time in cloud computing. Apart from its popularity, it has some drawbacks that prevent it from being widely adopted. A study on virtualization concerns has been conducted in this research work.
Cloud computing, Security in clouds, network security, Virtualization, Effects of virtualization
For past more than half of the decade, the detection and recognition of the traffic signs are an active research area; especially in the field of automation of driving that is critical for driverless driving, as there is drastic increase in road accidents due to ignorance of traffic signs and rules. This is frequently used for recognizing permanent or temporary various road signs which are displayed on the side of every small and long road. A complete recognition system may consist of detection of traffic signs as well as their recognition. The detection of traffic sign as well as their recognition is typically used on portable devices. This helps the devices to make better decisions and improve their driving algorithms. The preferable parameters that need to be considered are size of the traffic sign boards and speed of the vehicles. This study addresses the real time detection and recognition of traffic sign from the traffic sign boards through convolutional neural networks. Furthermore, image segmentation techniques such as edge-based segmentation, region-based segmentation, cluster-based segmentation, and so on, play an important role in traffic sign detection and recognition. Also, convolutional neural networks lead to a robust system that achieves higher accuracy in detection phase as well as higher performance ability in training and recognition phase. The proposed model achieves recognition accuracy up to 98.13% at the training.
A Study on Cloud Computing Security Issues and Challenges
Cloud computing is a network of servers that are connected via the internet. Its primary function is to share data, resources, and software. The internet serves as an invisible link that connects everything. This includes all physical and virtual servers around the world. Cloud computing is the most common topic of research these days because of its cost efficiency. It is the most arousing and attractive technology in the present era which is providing its services to the cloud users over demand. Due to the storage of the data and widespread use in cloud computing, its security has become a major hurdle which is causing a hindrance in the implementation of cloud habitat. There are a large number of users who use the cloud to store their private information; this is the reason why security is required. The main interest of cloud is security while exchanging or storing data on cloud. This review examines the security issues in cloud computing.