Reviewerly/exHarmony huggingface dataset:
A Conceptual Analysis of Machine Learning Towards Digital Marketing Transformation

Man-made brainpower has been underexplored. Machines with profound learning skills can take advanced showcasing higher than ever with their Man-made consciousness having a significant effect. This paper tries to find discoveries from an investigation of responses across various socioeconomics to robots and their selling powers. It’s also been discovered that software engineers need to work in tandem with digital marketers using machines with deep learning to consider consumer attitudes, behaviors, and preferences while designing the architecture. Because of this, in the future, marketers will have far more access to correct information on customers, which will have enormous advantages for the company. While search engine marketing automation has a lot of potential, marketers know that humans still have an essential role to play in the formulation of abstract strategies. The review that is being given here centres around how promoting offices, media organizations, and sponsors use and utilize ML-driven investigation instruments. The exploration features four central issues, including 1) the meaning of wise scientific apparatuses in the turn of events and execution of promoting strategies; 2) the absence of familiarity with arising advancements, for example, Al (ML) and man-made consciousness (simulated intelligence); 3) the imminent utilization of ML instruments in publicizing; and 4) the low degree of improvement and use of ML-driven logical devices in advertising. To help organizations in distinguishing valuable open doors and completing drives zeroed in on the sending and acknowledgement of quantitative ML devices in computerized showcasing, a system comprised of facilitators and a task plan was laid out.Data collection and analysis will perform using SPSS software, and findings will be drawn from a combination of a fuzzy-approach to determining how best to persuade customers to utilize the machine’s services and a variable-oriented, quantitative examination of the obtained data will consider.
A Critical Analysis on Neural Networks and Deep Learning based Techniques for the Cloud Computing System and its Impact on Industrial Management

Due to the rising use of the internet for communication, commercial financial transactions, and other purposes, cloud computing is described as a crucial feature that has risen lately. Through network systems, this entails providing a flexible growth of different services. The fog is often used as a concept to describe different internet.People often trust cloud service providers to store enormous amounts of data and information with the intention of accessing it later. This paper is mainly concerned with doing a critical examination of the usage of supervised learning and deep learning in cloud applications, as well as their effects on production management.Modern businesses are concentrating on using technology to increase returns on investments, assemble vast quantities of data, and archive it securely so that it may be utilised for organisational decision-making. Notwithstanding, they are more concerned with the cost and security of storing the information. so in order to accommodate the customers, the cloud services have used a wide range of approaches, including human brains, supervised learning, advanced analytics, and others. This helps to increase the users’ credibility and help them use cloud storage to achieve their objectives.In order to understand the overall efficacy of employing neural networks and advanced learning processes connected to cloud technology, this research is capable of using both secondary and primary information. The efficacy will be assessed by the researchers using correlation matrix and other non-parametric approaches. The conclusion is reached after thorough data examination and interpretation.
Design and Implementation of IoT-ML Based Data Management for Sustained System Integrity

IoT &ML are two examples of recent developments in the management of vast amounts of data (ML). In the past, humans were responsible for all aspects of the big data lifecycle, including the collection, analysis, and acquisition of data. The IoT and Machine Learning, on the other hand, have made it possible for machines rather than people to collect vast amounts of data, analyses that data, and establish new business models. The management of big data has proven to be challenging for larger companies, despite the fact that industry 4.0 has seen tremendous success. Among these are problems with data management accuracy brought on by insufficient training, cloud computing security concerns, and blockchain security flaws. Cloud computing security concerns have also been linked to blockchain security flaws. As a consequence of this, this research has provided an explanation of how machine learning can be utilized to both bolster the safety of blockchain technology and enhance the management of large amounts of data. A primary poll was conducted with fifty individuals, and those individuals were asked a series of questions regarding big data technologies and blockchain security. The findings indicated that providing employees with training and establishing laws and regulations that are based on blockchain technology might, respectively, increase the accuracy of data management and cloud computing security.
A Comparative Approach on Enhancing Lifetime of Wireless Sensor Networks

Wireless Sensor Networks are crucial elements that are employed to assess the environment, in addition, to utilizing the sensor information for additional computing including climate forecast, human medical-related forecasts, transportation management, and so on. Because of the significant operational expense of the system, the sensor network of the system is anticipated to run independently in the area for an extended length of time in these circumstances. As a consequence of this, the exhaustion of single sensor nodes may have an impact on the network’s operations. As a result, in an attempt to extend the network lifespan, an energy-efficient alternative must be proposed. Wireless sensor network technologies have emerged in past decades as an evolving innovation for monitoring & recording ambient physical variables utilizing many sensors. For the organizing of gathered data, centralized sites are required. Multi-hop communication is used to transfer data to the sink nodes communications are needed. To eliminate undesired transmissions, the most well-known WSN approach known as information aggregating is utilized. It must gather information from various sensors & transmit it to the ground station. The procedure of gathering information is repeated many times to maximize information effectiveness. This technology contributes to lower transmitting information power costs.
Segment Analysis for Nonparametric Image Registration of Spaceborne Imagery of Farmland

Segment analysis is a daunting problem in image processing. There is interest in its use along with nonparametric image registration to align meaningful data of multitemporal remote sensing imagery, which is critical for the successful longterm monitoring of a specific land feature. This study deals with the application of the aforementioned technologies to effectively align segments of different spaceborne images. On the one hand, the mathematical apparatus for segment analysis is leveraged to extract significant information. The concept of a contour is used. The improved model of a segment is introduced, as well as the formal description of a composite object is enhanced. On the other hand, nonparametric registration techniques for optimizing an objective function, quantifying the similarity measure, and regularization term provides a flexible approach for image registration. As a result, the registration approach is based on segment analysis, multilevel registration, curvature regularisation, and normalised gradient field distance measure. A formal description of the segment has been developed. The technique can register segments of remote sensing imagery precisely. This is proved by experiments.
Analyzing Robotics and Computer Integrated Manufacturing of Key Areas Using Cloud Computing

The rising customer demands are fulfilled by the industrial sector through increased output and expedited shipping. The objectives or satisfied through the manufacturers by using cloud technologies. Tiers of manufacturing sectors adopt with the proposed technology in the future for maintaining the connection shows with the supply chain as well as customers. Cloud computing is referred as abstraction of storage, computation and infrastructure of network altogether as the platform which allows application deployment in speedy manner and dynamic scaling as well. The crucial area in cloud computing is self-service that can allow users to fill the web-form easily and quickly. A novel technical approach to job execution and resource sharing compared to conventional industrial robots is provided by Smart Manufacturing Environments, which uses cloud robotics. SME can be built up on the combination of cloud computing and industrial robots. The main focus of the proposed study is to successfully enabling the manufacturing services on demand which is provided over the internet. Manufacturing services with Cloud-based equipment can also be used by companies attempting to convert Collaborative Robots, Pa-as-you go business models, and production of increasing the productivity of production facilities, raising the caliber of products, enhancing producers’ capacity to adapt to shifting consumer needs, and entering new markets.
A Survey on Encryption Algorithm with Hybrid Block Chain in Wireless Body Area Network

The recent electronic Medical monitoring system focuses on maintaining the database of patients with their medical history and up-to-date prescriptions and treatment maintained with high degree of confidentiality. The E-Medical services where Wireless Body Area Network (WBAN) is integrated face certain challenges such as data sharing, network reliability, database management and so on. The greatest threat in WBAN remained the data security and integrity. Several new methodologies supports to overcome these threats with secure hash function, digital signature with effective routing protocols to provide enhanced solutions for secure data maintenance and sharing in WBAN. The security system based on Block Chain Technology(BCT) are the most common trust building measure for WBAN, where the data are stored in distributed ledgers. This technology faces problems on minimal storage size and unauthorized access of the distributed data. This was overcome by the technique called sequential aggregate signature scheme with designated verifier with block chain based cloud transaction in WBAN in recent trends. The above scheme faces problem of wastage of storage space in the ledgers and it could be rectified by the proposed system of Encrypted Multi map block chain in WBAN which supports high degree of data access with reduced computational complexity of storage. This proposed system aims to simulate the WBAN with improved access of distributed authentication key of users in ledger pools and provides confidential sharing of data. This system deals with the comparative study of different encryption used in block chain storage to analyze their performance in WBAN
A Study in Understanding the Major Factors influencing the Investment Decision Making in Cryptocurrency Markets by Gen Z and Millennials

As a method of exchange, cryptocurrency, which is a digital version of money made possible by a system called “blockchain technology,” functions very similarly to the fiat currency that it is based on. A cryptocurrency is also referred to as payment tokens, crypto tokens, electronic money, cyber money, virtual products, and virtual assets. These are all different names for the same thing. Because of the proliferation of cryptocurrency usage, it is now feasible to engage in direct monetary transactions, so sidestepping the need for conventional monetary authorities such as banks and governments to be involved. Bitcoin was the first kind of digital currency that existed only in the online world. This is the ultimate effect of dispersing operating expenses, which is a common strategy used by financial organizations in order to maximize their revenues. Cryptography is a tried-and-tested method that may be used to protect sensitive information from being seen by unauthorized parties. When sending data via a secure encryption method, the information must first be encrypted. The name given to the decentralized peer-to-peer network that Bitcoin uses is “blockchain.” The primary objective of this change is to make it more difficult to spend money twice. The fact that neither the server nor the administration is centralized lends credence to the theory that the system in question is decentralized. Only businesses and members of the general public are able to independently validate the value of these currencies. The basic objective of the paper is to understand the major factors influencing the investment decision making in cryptocurrency markets by Gen Z and millennials. A well-structured questionnaire used for collection and convenience sampling is utilized rather commonly for the purpose of participant selection since, in comparison to other methods from a11152 participants. Despite all of the fuss, bitcoin may still be used today for genuine online transactions of items and services. Cryptocurrencies are a game-changer because they represent a significant divergence from the existing system of centralised and tightly regulated financial institutions. While professionals are responsible for the development, administration, and assessment of traditional financial reports. It’s possible that the proliferation of blockchain’s usage is to blame for the growth in transactions using digital currencies. Adding cryptocurrencies like Bitcoin to your investment portfolio could be a smart move if you’re wanting to broaden its scope. There are numerous similarities between the market for cryptocurrencies and markets for other types of financial assets, and certain cryptocurrencies are becoming more often utilized as payment methods..
Data Management and Visual Information Processing using Machine Learning

“Machine learning (ML) ”-based large-scale data insights are the foundation of many contemporary data-driven technologies. ML systems offer ways to define and carry out certain ML workloads effectively and flexibly. Because of information-driven application highlights, information-driven responsibility elements, and framework designs affected by customary information the executives draw near, data the board is at the centre of numerous ML frameworks. Moreover, a ton of present-day information-driven arrangements are based on AI (ML)- based on enormous data bits of knowledge. A few ML occupations can be characterized and executed effectively and deftly utilizing ML frameworks. Data the executives are at the underpinning of numerous ML frameworks are reasonable to information-driven application qualities, information-driven responsibilities attributes, and framework models affected by old-style information the board techniques. Various ML clients with different ML lifecycle obligations and ML abilities are available in many Calculations. Information researchers are ML clients who have specialized information in ML and examination to control the information and make ML models for the business. The potential issues with data management that might arise in settings where machine learning procedures are used in the real world. Because we have first-hand experience with such huge pipelines, we can home in on issues relating to the interpretation of training data as well as its validation, cleaning, and enrichment. The objective of this article is to draw attention to these issues, establish links to previous efforts in the database area, and identify research questions that have not yet been solved.
Application of Blockchain and Internet of Things (IoT) for Ensuring Privacy and Security of Health Records and Medical Services

The research’s introduction primarily focuses on the advantages of putting in place a data security framework to protect all health information. The medical associations should employ IoT and a wifi system to create an effective data security system. It is essential to think about all the benefits and the importance of using an information security management system to keep everything documents pertaining to medical facilities secure. In the study’s evaluation of the data, the advantages of utilising a security control system for information to protect health history are primarily the emphasis. All essential benefits and technology, though, must be primarily focused on managing all data security systems. It is crucial to possess all the knowledge necessary to keep healthcare data safe in order to correctly access all patient data. It is essential to create this data security management to keep all medical records secure and private secure. Two tests—a a one-sample T test and a regression test — were run during the data analysis phase, using the year and the prevalence of cryptography in the healthcare sector as independent variables in each test. An enhanced level of medical record security is the dependent variable. The outcomes were contrasted with the calculated values. The threshold for significance is fixed at 0.05.
Applications of Artificial Intelligence and Blockchain Technology in Improved Supply Chain Financial Risk Management

With greater frequency and granularity than ever before, artificial intelligence (AI) and blockchain technology can help businesses achieve next-level performance in the supply chain. Blockchain can reliably construct the framework for recording data across endpoints, whereas AI can assist in data matching. This study investigates how to apply blockchain technology and artificial intelligence together in supply chains to push the boundaries of operational effectiveness, promote sustainable growth, and monetize data. This study has examined how blockchain can be used to track financial operations in the supply chain and also aims to determine how artificial intelligence contributes to supply chain management. The study’s anticipated results will increase knowledge of blockchain technology and artificial intelligence in supply chain risk management and encourage practitioners and researchers to think about using these technologies in future context-aware research.
Detection of Malicious Social Bots with the Aid of Learning Automata on Twitter

Violent social bots automate social interactions, create fictitious profiles to spread destructive propaganda, or assume the identities of followers to make misleading tweets. Furthermore, malicious social bots disseminate malicious root URLs, which reroute requests from online social media agents to certain malicious servers. Therefore, one of the most crucial jobs of the Twitter network is to distinguish between actual drug users and active social bots. Instead of taking as long to remove as social graph-based features, URL-based features can identify the cruel conduct of social bots. It’s difficult for malicious social bots to change URL redirect chains. This part offers a literacy automaton-grounded vicious social bot discovery (LA-MSBD) for safe (drug) agents on the Twitter network by fusing URL-based functionality with a trust computation model. The research discussed in this paper focuses on designing, utilizing, and evaluating robotic sensors based on deep literacy models rather than adding metadata about position or birthpoint counting. This paper also demonstrates how deep literacy models can compete with conventional machine-ability idioms. The findings of this study demonstrate that in-depth comprehension models can be made more complex by utilizing pre-trained models.
Gesture Controlled Robotics arm using Image Processing

The combination of mechanical, electronic, informative, computer science and many other engineering that aims to replicate the behavior of humans, or to make tasks automated and easier is called Robotics. Robots can be used to perform many different types of tasks under various environments. Most of these environments are those that can be lethal to humans like those of radioactive radiations. Other environments include those that are beyond the reach of humans such as far into space or deep underground. There are tasks like carrying a moving a lever up and down twenty thousand times a day, which is not possible for a human. This is where a robot steps in. A robot can go to far off space like Mercury and send high resolution pictures back here to our planet earth. A robot can go deep inside our earth, study the soil and send back data. A robot can work on nuclear power plants with radiations have zero to negligible effect on it. A robot can be used to do automated tasks without getting tired. In this paper, our aim is to make a gesture-controlled arm robot that is better, and an improvement over the existing system.
A Framework of Artificial Intelligence for the Manufacturing and Image Classification system

Artificial intelligence (AI) has been successfully employed in industries for decades, beginning with the invention of expert systems in the 1960s and continuing through the present ubiquity of deep learning. Data-driven AI solutions have grown increasingly common as a means of supporting ever-more complicated industrial processes owing to the accessibility of affordable computer and storage infrastructure. Despite recent optimism, implementing AI to smart industrial applications still offers major difficulties. The present paper gives an executive summary of AI methodologies with an emphasis on deep learning before detailing unresolved issues in AI safety, data privacy, and data quality — all of which are necessary for completely automated commercial AI systems.
An Essential Review of the Primary Variables Impacting Network Security measures just using Big Data Circumstances with Manova

Businesses may utilize big data to spread across a network, analyses vast amounts of complex data, and find hidden patterns and connections. The deployment of a framework that is both effective and secure is required since the information that moves across an infected system is typically sensitive. In the realm of big data, network security has thus been raised to the early phases of assessment. In order to identify and stop potential attacks in real time, network security protocols must manage a significant volume of data which is both complex and diverse. Unfortunately, since such systems are usually created using outdated techniques, securing vast volumes of information may be difficult. In this post, we'll mostly talk about information protection for massive volumes of data and network security solutions. We start by talking about the variables that affect network security design in the Big Data environment. Then, we'll talk about a few big data solutions that provide network-wide data security as well as recent research results.
Electronic Transactions Mechanism for Messaging Privacy

We communicate our private information online on a regular basis since we live in the world of the internet. Therefore, it’s important to confirm the veracity of communications in order to prevent transmitter. To prevent – anti, we are utilizing an unique identifier. There are several variations of digital signatures on the markets. However, we transmit both the previous post and the delayed reply to the recipient in every method. Thus, the original communication has no protection applied. We provide a method in this work that can protect the original and its validity. In this work, the decryption algorithms are both RSA, and the hashing technique is SHA256.