Ethical Challenges and Privacy Concerns Associated with Big Data in the Hiring Process: A Mixed-Methods Study | Chapter 6 | Mathematics and Computer Science: Research Updates Vol. 6

 

Background: The advent of big data in recruitment processes has introduced more efficient, quicker, and scalable enhanced decision-making. Big data technologies enable recruiters to analyse vast amounts of candidate information, ostensibly improving the precision with which suitable candidates are identified. However, this technological advance also presents significant ethical challenges.

 

Aims: This study aims to explore the ethical challenges and privacy concerns associated with the use of big data in recruitment processes, focusing on algorithmic bias, data privacy, and fairness in hiring practices.

 

Methodology: The research employs a mixed-methods design, integrating qualitative interviews with HR professionals and quantitative data analysis to assess the implications of big data utilisation in recruitment. The study was conducted across various organisations, focusing on their recruitment practices, over six months. Qualitative interviews were conducted with HR professionals to gather insights on real-world experiences related to ethical challenges in recruitment. Additionally, a quantitative analysis of recruitment algorithms was performed to identify prevalent biases and their impact on hiring decisions, using statistical evidence to highlight significant findings. By triangulating these methods, the research robustly examined how big data applications alter recruitment landscapes, identifying ethical challenges and laying a foundation for potential solutions.

 

Results: The findings reveal that algorithmic bias is a profound issue in recruitment, with 62% of surveyed HR professionals acknowledging its influence on hiring decisions. Moreover, significant concerns regarding data privacy emerged, with 75% of respondents indicating that handling sensitive candidate information lacks adequate safeguards, increasing the risk of unauthorised access. Addressing ethical concerns in big data recruitment necessitates the collaboration of multiple stakeholders, including HR professionals, data scientists, and ethicists. Integrating fairness-aware algorithms is a pivotal strategy, as they aim to rectify biases at different stages of data processing, ensuring equitable decision-making. By encouraging collaboration and implementing comprehensive strategies, organisations can mitigate the ethical challenges associated with using big data in recruitment, ultimately fostering a more inclusive and fair hiring environment.

 

Conclusion: The study concludes that while big data enhances recruitment efficiency, it simultaneously raises critical ethical challenges that must be addressed. Organisations need to implement robust frameworks to ensure fairness and transparency, thereby safeguarding candidates' privacy and fostering equitable hiring practices. These insights provide crucial guidance for HR professionals seeking to navigate the complexities of big data in recruitment.

 

 

Author(s) Details

 

Kevwe Onome-Irikefe
University of Rochester, United States.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/mcsru/v6/5930

 

Big Data-Artificial Intelligence as a Systemic Procedure in the Field of Legal and Forensic Medicine | Chapter 7 | Science and Technology: Developments and Applications Vol. 6

The combination of Big Data (BD), Artificial Intelligence (AI) and Systemic Procedure (SP) constitutes a transversal technological approach focused on improving decision-making in the field of Medicine and, in particular, of Legal and Forensic Medicine. It is advisable that medical professionals become increasingly involved in the knowledge of new concepts, technologies and tools that are becoming increasingly essential for daily work. The terms Big Data and Artificial Intelligence are increasingly used, seeking a symbiosis between them. If we add to these terms the term Systemic Procedure, we have the necessary concepts to approach any field with greater efficiency.

In recent years, Big Data in Medicine has become a phenomenon and the field of Legal and Forensic Medicine could not be left out. The complexity of the data processed in this field is evident and, therefore, requires special treatment. The application of Artificial Intelligence to forensic sciences is increasingly less questionable since reliable, verifiable and reproducible results are obtained. The characteristics (the V’s) with which Big Data is endowed are those that define it. The sequence of characteristics continues: 3, 5, 7, 9. We will focus on the development of Big Data through seven characteristics (7 V’s).

The concepts (BD) and (AI) must go hand in hand with the growth of the greater computational capacity that is being developed and the improvement of the algorithms used.

The objective of this article is to analyze, from a theoretical point of view, Big Data (BD) and Artificial Intelligence (AI) within the field of Legal and Forensic Medicine as a Systemic Procedure (SP) with a holistic perspective. Implementing Fig. 2 through algorithms of both Big Data and Artificial Intelligence with a Systemic Procedure structure will help decision-making in general and, in particular, in the field of Legal and Forensic Medicine.

 

Author (s) Details

 

Elena Martín Pérez
Department of Medicine, University of Salamanca, Salamanca, Spain.

 

Quintín Martín Martín
Department of Medicine, University of Salamanca, Salamanca, Spain.

 

 Please see the book here:- https://doi.org/10.9734/bpi/stda/v6/4494

Streamlining Big Data Mining with Cutting-Edge Approaches | Chapter 10 | Research Updates in Mathematics and Computer Science Vol. 2

 Big data refers to any assortment of data which are outsized and intricate in nature such that conventional database administration systems and data processing tools cannot process. Feature selection serves primarily to reduce the processing burden of data mining models. To expedite the processing of large volumes of data, parallel processing is implemented using the MapReduce (MR) technique. MapReduce model is applied to big datasets, which is further divided into smaller partition. However, existing algorithms often fall short in enhancing classifier performance significantly. This research advocates for the use of the MR method to conduct feature selection in parallel, thereby improving performance. Additionally, to augment classifier efficacy, this study introduces an innovative approach combining Online Feature Selection (OFS) with an Accelerated Bat Algorithm (ABA) within a framework that pre-processes features ahead of time, without prior knowledge of the feature space. The proposed OFS-ABA method is designed to select relevant and non-redundant features within the MapReduce (MR) framework. Furthermore, an Ensemble Incremental Deep Multiple Layer Perceptron (EIDMLP) classifier is employed to classify dataset samples. The outputs from homogeneous IDMLP classifiers are aggregated using the EIDMPL classifier. The proposed feature selection method and classifier are extensively evaluated across three high-dimensional datasets. The results indicate that the MR-OFS-ABA method outperforms existing feature selection methods such as PSO, APSO, and ASAMO (Accelerated Simulated Annealing and Mutation Operator). Additionally, the performance of the EIDMLP classifier is compared with other existing classifiers including Naïve Bayes (NB), Hoeffding Tree (HT), and Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC)-KNN (K Nearest Neighbor). The methodology is applied to three datasets, and the results are compared across four classifiers and three state-of-the-art feature selection algorithms. Overall, the findings of this research demonstrate improved accuracy and reduced processing time.

Author(s) Details:

Renuka Devi D,
Department of Computer Science Stella Maris College (Autonomous), Chennai, Tamil Nadu, India.

Swetha Margaret TA,
Department of Computer Science Stella Maris College (Autonomous), Chennai, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/RUMCS-V2/article/view/13960

Using Machine Learning Algorithms for Cancer Image Dataset: A Predictive and Prescriptive Analysis | Chapter 3 | Research Updates in Mathematics and Computer Science Vol. 2

 This paper focuses on the problem of using machine learning techniques on cancer/tumor prediction. One of the biggest applications of big data and machine learning is in the field of medical domain. Consequently, a health care organization that uses the techniques of machine learning and big data to treat patients see fewer mishaps or gets enough time to deal with them in advance. Classification and prediction of the images are fairly easy task for humans, but it takes more effort for a machine to do the same. Machine learning helps to attain this goal. It automates the task of classifying a large collection of images into different classes by labelling the incoming data and recognizes patterns in it, which is subsequently translated into valuable insights. Furthermore, the prediction of Osteosarcoma case for one of the four classes of tumor namely Non tumor, Non-Viable tumor, viable tumor, Viable: Non-Viable tumor has to be done. The quantitative analysis is done using various machine learning libraries of python. The three classification algorithms used for image analysis are random forest, SVM, and logistic regression. The metrics used for performing perspective analysis are precision, recall and F1 Score. The domain of medical imaging helps providing important information on anatomy and organ function subsequently detecting disease states. Although the characteristics of medical data make its analysis a big challenge notwithstanding that machine learning techniques could make the task easier. The results show that the random forest algorithm has performed best amongst the three classification algorithms when given with less complicated scenario, with prediction accuracy, precision, recall and f1 score of 100%. But the performance of every classification algorithm degrades when provided with the cases of Osteosarcoma which has got more complicated scatter graph. However, the logistic regression retains its performance by predicting tumor cases with 99% accuracy. For future scope, various other machine learning algorithms can be applied to observe their performance on the same set of features extracted from the cancer image data set.

Author(s) Details:

Divya Chauhan,
Department of Computer Applications, Government College Rampur, Himachal Pradesh, India.

Kishori Lal Bansal,
Department of Computer Science, Himachal Pradesh University, Shimla, India.

Please see the link here: https://stm.bookpi.org/RUMCS-V2/article/view/13942

Investigation on Sentiment Analysis in Big Data using Machine Learning | Chapter 11 | Research Highlights in Mathematics and Computer Science Vol. 9

 With the arrival of Web 2.0, users have developed a increasing interest in sharing their content, which has happened further amplified apiece emergence of various friendly networking sites. These platforms have supported an excellent opportunity for consumers to share their opinions accompanying people all over the realm. The opinions expressed by consumers on the internet can have a significant affect the service manufacturing. As a result, various industries to a degree educational institutions, analysts, and business organizations are concentrating on opinion excavating, also known as sentiment study (SA), to extract the views and opinions posted for one public. The primary objective of this study search out provide a inclusive overview of sentiment study using machine learning approaches. Furthermore, this paper sheds come to rest on the significant challenges faced by SA, that present a vast purview for future research.

Author(s) Details:

L. Sudha Rani,
CSE Department, GPREC, JNTUA, Anantapur, Kurnool, A. P., India.

S. Zahoor-Ul-Huq,
CSE Department, GPREC, Kurnool , A.P., India.

C. Shoba Bindu,
CSE Department, JNTUA, Anantapur, A.P., India.

Please see the link here: https://stm.bookpi.org/RHMCS-V9/article/view/10419

A Review on Transdisciplinary Approach and Challenges on Wearable Technology | Chapter 6 | Recent Progress in Science and Technology Vol. 7

 Wearable Technology is an arising technology that impacts in our regularly life as a major part. This wearable science become appealing due to its amount, speed, storage, feeling behavior, and multifunctional capability.  All wearable designs must have sensing powers. This paper focuses on various reviews, features, and requests of wearable technologies and is emphasize with basic plans on the major electronics. This paper helps the researchers the one are beginners in this wearable electronics. This platform keeps us more handy and comfortable by trusting in allure various competencies. It gives a summary of the interaction between persons and technology. It handles big data states that can capable to handle huge amounts of news at a very high speed which maybe able to process over occasion which designs perfect value and communicates to humans according to schedule. Wearable technology plays a fault-finding role as the heart of bureaucracy to deliver the dossier transformation. It is a type of gadget that create our life smooth by keeping us fit, flexible, and general updates according to schedule. This survey is towards the various instruments on wearable, their challenges, and research from now on.

Author(s) Details:

D. Bhuvana Suganthi,
BNM Institute of Technolgy Bengaluru, Karnataka, India.

M. K. Vidhyalakshmi,
Department of Computing Technologies, SRM Institute of Science and Technology, Chennai, India.

A. Punitha,
MAM School of Engineering, Thiruchirappalli, India.

S. Raghupathi,
University of Technology and Applied Science, Ibri, India.

M. Subhapradha,
MAM School of Engineering, Thiruchirappalli, India.

Please see the link here: https://stm.bookpi.org/RPST-V7/article/view/9973

Prospects of Big Data Industry under Mathematical and Physical Sciences System | Chapter 10 | New Frontiers in Physical Science Research Vol. 9

 Science and electronics are rapidly evolving as institution develops and progresses, and communication science is particularly intriguing at the present. The information dossier generated by the progress of communication technologies is evenly becoming a strategic advantage, that is contended by everybody. The information age is rapidly nearing us. Information is a resource in this framework, and timely and direct information resources are like money codes just waiting to be discovered and secondhand. Additionally, a lot of creative trades are advancing significantly and progressing into the modern unicorns as a result of facts resources. This study is established the characteristics of the era of the facts explosion and discusses the inceptions and applications of information while enlightening the essence of information dossier resources. And I hope to clarify some different outlooks on the use of information and dossier. Big data is becoming an main information resource for the movement of the entire society.

Author(s) Details:

Hong Xiong,
University of California, Los Angeles, USA.

Please see the link here: https://stm.bookpi.org/NFPSR-V9/article/view/9859

Positional Isomerism in N-propyl and Isopropyl Alcohol Using Ultrasonic Characterization | Chapter 9 | New Frontiers in Physical Science Research Vol. 9

 In the work, ultrasonic parameters like speed and attenuation are calculated in mixtures of n-propyl and isopropyl alcohols in nitrobenzene at a 10 MHz pulse-echo method. Ultrasonic attenuation is sensitive to the synthetic composition of the combination.Ultrasonic velocity and attenuation calculations using the pulse_echo method operated at 10 MHz have existed taken at various concentrations of two together alcohols in nitrobenzene, from 283.15K - 293.15K. The data is analysed based on the verdicts, which revealed that both systems exhibit fault-finding nature and non-linear alternative in ultrasonic diffusion velocity and attenuation. In comparison to isopropyl intoxicating in nitrobenzene, ultrasonic attenuation at lower concentrations of n-propyl intoxicating in nitrobenzene reveals the dominant fundamental relaxation. Mixing of mixed and non-associated liquids leads to dipole-inferred interactions.

Author(s) Details:

Rupali Jayant Thete,
G H Raisoni College of Engineering, Nagpur-440016, India.

Satish Sharma,
Department of Electronics, Rashtrasanta Tukadoji Maharaj Nagpur University, Nagpur, India.

S. Rajgopalan,
Department of Electronics, Rashtrasanta Tukadoji Maharaj Nagpur University, Nagpur, India.

Please see the link here: https://stm.bookpi.org/NFPSR-V9/article/view/9859

An Analytical Study on Disruptive Technologies in Zimbabwe| Chapter 5 | Recent Progress in Science and Technology Vol. 2

 Our sink this study was to speak to causing trouble innovations in spite of cyber fraudsters who act the prowl. The goal concerning this study was therefore, to illustrate Zimbabwe's financial services area is embracing disruptive electronics and the preliminary benefits; one of the country's best financial institutions. The study selected the qualitative methodology accompanying a desk content study. The findings of the study are that: banks, as part of the monetary services industry, have the important roles in the development and economic development of a country with its own government; hence the necessity to insulate these institutions from the shenanigan of fraudsters. The study further discovered that Zimbabwean government's approach to novelty should be focused on big market changes alternatively on existing infrastructure. The study again found out that cyber-attacks are certain; however, skilled are various advantages to accepting disruptive technologies for the fiscal industry as labeled. There is need therefore, to fully understand disruptive technology and form its use more consummate. Apart from insulating information systems, calculatings, devices, programs, data and networks from within or external dangers, harm, damage, attacks or unauthorized access, - causing trouble technologies identified in the study to a degree Cloud computing, Artificial Intelligence, Block chain science, Cyber security, and Big Data; have other real benefits, ranging from serving more consumers, expanding retail share, to increasing revenue at a lower cost. The study suggested a couple of recommendations; that the economic service subdivision in Zimbabwe should adapt to causing trouble technologies and move from conservatism. That Regulators and the Government concede possibility soften investment laws and regulation to admit quick adoption to new causing trouble technologies what the financial service subdivision must invest in financial and workforce to enable nimble uptake of technological changes.

Author(s) Details:

Nyasha Kaseke,
University of Zimbabwe, P.O.Box MP167, Mt Pleasant Harare, Zimbabwe.

Enesiti Chirume,
Catholic University of Zimbabwe, P.O. Box H200, Hartfield, Harare, Zimbabwe.

Please see the link here: https://stm.bookpi.org/RPST-V2/article/view/9236

The QoS in Big Data| Chapter 8 | Current Overview on Science and Technology Research Vol. 2

 The information on the Internet is developing enormously and this dramatic development is it self-turning into a test to access, interaction and show the legitimate data. As the data is developing so does, the innovation and the calculations to get to the information is likewise developing. The rate at which data is developing on the Internet is far more prominent than the advancement of new calculations that are getting to this information. As right now, developing information is of heterogeneous kind which implies it contains blend of text, sound, video, designs which is making a confined admittance concerning the innovation stylish. This enormous amount of heterogeneous information is for the most part known as Big Data.

Author(s) Details:

Masroof Amin,
Department of Computer Technology and Business Management, Maharishi Markandeshwar Deemed to be University, Mullana, Ambala, 133207, Haryana, India.

Munishwar Rai,
Department of Computer Technology and Business Management, Maharishi Markandeshwar Deemed to be University, Mullana, Ambala, 133207, Haryana, India.

Please see the link here: https://stm.bookpi.org/COSTR-V2/article/view/7991

An Overview of Optimal Input-Output Planning Model and Cross-Boundary Economic Management Information System — Real-time Analysis of Input-output Optimal Planning Models and Timely Analysis of Input-output Statistical Models | Chapter 12 | New Innovations in Economics, Business and Management Vol. 8

 The important phrase in the IT business in 2010 was cross-boundary, which swept down from upstream industries to downstream terminals, PC, software, internet... with no exception in all fields. A dramatic shift has occurred as a result of the convergence of mobile and internet. Traditional fields' power, on the other hand, can never be duplicated in new places. The second cross-boundary is a theoretical basis of cross-boundary economic management information system as well as a scientific method for realising the integration between the optimal input-output planning model and Big data, new technologies of Cloud Computing, and Internet of Things or new internet industry by using mathematical tools from the thinking mode of discrete mathematics and continuous mathematics. The area, scale, strength, and effectiveness of its application with the second cross-boundary . The cross-border economic management information system may be utilised to boost not only enterprise management, but also modern management quality and governance capability, based on modernisation of enterprise management. It is not only a scientific foundation for constructing "One World," fairness, mutual benefit, and equitable competition, but it is also a strategy for entirely eliminating waste and corruption breeding grounds. A brief explanation based on real-time analysis of the optimal input-output planning model and timely analysis of the input-output statistical model will be presented in this work.

Note: The following are the shorter versions referenced in this document for your convenience: input-output model, short for input-output planning model and input-output statistical model; the optimal micro input-output planning model and the optimal macro input-output planning model; the optimal input-output planning model, short for the optimal micro input-output planning model and the optimal macro input-output planning model; the optimal input-output planning model, short for the optimal micro input-output planning model and the optimal macro input-output planning model; input-output statistical model, short for real-time analysis of the optimal macro input-output planning model and timely analysis of macro input-output statistical model; real-time analysis of the optimal input-output planning model and timely analysis of input-output statistical model; real-time analysis of the optimal micro input-output planning model and timely analysis of micro input-output statistical model; real-time analysis of the optimal micro input-output planning model and timely analysis of micro input-output statistical model; real-time analysis of the optimal micro input-out Combinations between the optimal input-output planning model and new technology and new industry, short for combinations of the optimal micro input-output planning model and new technology and new industry as well as the optimal macro input-output planning model and "new technology" and "new industry"; new technology, short for Big data and new Cloud Computing technologies; new industry, short for Internet of Things or new internet industry; Manufacturing refers to the integration of these three elements: objects of labour, labour force, means of labour, and relevant advancements; operation management refers to market management and finance management.


Author(S) Details

Ning Kang
Training and Evaluation Centre, Guizhou Power Grid Company, China Southern Power Grid, Guiyang, China.

View Book:- https://stm.bookpi.org/NIEBM-V8/article/view/6744

Scientific Foundation of Real-Time Input-Output Tabulation Method and AI — Organic Combinations and Connections between the Optimal Input-output Planning Model and Automation, Information, Intellectualization, Big Data, New Cloud Computing Technology, Internet of Things or New Internet Industry & AI Technology | Chapter 10 | New Innovations in Economics, Business and Management Vol. 8

 The application of Big Data, new Cloud Computing technology, Internet of Things or new internet industry, and AI will usher in a massive revolution in human beings based on combinations of automation, information, and intelligence, which will consequently rock the foundations of modern economy, politics, social sciences, management and accounting, and even bring about fundamental changes and development, and thus an era for technology industry will go novelty, i.e. During the Industrial Revolution, the standing of continuous mathematics as a mainstream, represented by calculus, shifted, and the relevance of discrete mathematics was progressively recognised. Many things that currently appear to be conceivable will prove to be impossible in the future, while the seemingly impossible will become achievable. The smooth operation of the national economy can only be directed by the objective law of coordinated development, and thus achieve maximum economic efficiency, if the passive situation of tabulating and interpreting is completely changed under such complicated, constantly changing, and rapidly developing circumstances. The scientific foundation for the real-time input-output tabulation technology and AI is correctly built on the basis of the aforementioned dreaming realisation. This thesis will employ real-time analysis of the optimal input-output planning model and timely analysis of input-output statistics model as examples of how to accomplish the good wish.

Author(S) Details

Ning Kang
Training and Evaluation Centre, Guizhou Power Grid Company, China Southern Power Grid, 550002 Guiyang, China.

View Book:- https://stm.bookpi.org/NIEBM-V8/article/view/6741

A Brief Introduction of Scientific thought, Theory and Method of “Real-Time Input- Output Tabulation Method” | Chapter 06 | New Innovations in Economics, Business and Management Vol. 8

 The contribution of "Real-time input-output tabulation technique" scientific thinking, theory, and method to human civilization, 1) The tabulation method has been established based on information technology to gather the basic data needed for the real-time analysis of the optimal micro material-based input-output planning model, and it can objectively reflect the actual situation of the enterprise environment and production activities at the time of the planning period; 2) the tabulation method has been established to gather the basic data needed for the timely analysis of the micro material-based input-output planning model, and it can objectively reflect the actual situation of the enterprise environment and production activities at the time . 3) Tabulation methods can be established based on real-time analysis of the optimal micro material-based input-output planning model and timely analysis of the micro material-based input-output statistical model to gather the basic data required for real-time analysis of the optimal micro value-based input-output planning model and timely analysis of micro value-based input-output statistical model; 4) The tabulation methods can be established based on the real-time analysis of the optimal micro material-based input-output planning model and the timely analysis of the micro material-based input-output statistical model, as well as the optimal micro value-based input-output planning model and the timely analysis of the micro value-based input-output statistical model, to gather the basic data needed for the real-time analysis of optimal macro material-based input-output statistical model. 5) By merging Big data, new Cloud Computing technology, and AI technology, scientific techniques may be built to automatically finish tabulation as well as its application, based on market need. Furthermore, from Material Requirements Planning (MRP) through Manufacturing Resource Planning (MRPII) to Enterprise Resource Planning (ERP) and lean manufacturing, which includes demand, production, and supply, Information integration and synchronous operation across virtual factories (dynamic alliance) are the same in agile manufacturing, which means there is no mathematical model. Despite the fact that all resources can be combined into a complete and unified system, such as engineering design, manufacturing, and management, which have formed some systems, and even though the automation level of internal information and material processing is very high in those systems, they are not fully automated.

Author(S) Details

Ning Kang
Training and Evaluation Centre, Guizhou Power Grid Company, China Southern Power Grid, 550002 Guiyang, China.

View Book:- https://stm.bookpi.org/NIEBM-V8/article/view/6737

Text Sentiment Analysis for Intelligent Transportation Systems by Using Web-Based Traffic Data: A Descriptive Study | Chapter 2 | Recent Advances in Mathematical Research and Computer Science Vol. 10

  In essence, transportation systems serve people, yet modern intelligent transportation systems (ITSs) have failed to consider public opinion. It is required to collect and analyse public wisdom and opinions in order to complete the ITS space. However, only a few research concentrated on the subject of transportation, which failed to meet the severe standards of intelligent transportation systems in terms of safety, efficiency, and information sharing (ITSs). We suggest traffic sentiment analysis (TSA) as a novel approach for addressing this issue, which gives modern ITSs a fresh perspective. This paper proposes TSA methods and models, as well as a comparison of the benefits and drawbacks of rule- and learning-based approaches using online data. We used the rule-based approach to solve real-world challenges, presented an architectural design, built associated bases, illustrated the process, and talked about online data collecting. The "yellow light rule" and China's "fuel price" were used to demonstrate the efficacy of our strategy. Our efforts will contribute to the advancement of TSA and its applications. This research paper proposes a new classification system for future development. To solve this problem, we employ a fuzzy extension known as the Fuzzy Naive Bayes classifier for the opinion mining process, which generalises the meaning of an attribute so that it has a collection of values to a specific degree of truth rather than a single value. This fuzzy naive Bayesian classification uses a hybrid classifier that combines Fuzzy Set Theory with a Naive Bayes classification.

Author(s) Details:

R. Jayavadivel,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka - 560064, India.


N. Rajkumar,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka - 560064, India.


B. Prabhu Shankar,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka - 560064, India.


E. Vetrimani,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka - 560064, India.


C. Viji,
Department of Computer Science and Engineering, HKBK College of Engineering, Bangalore, Karnataka – 560045, India.

G. Aarthy,
Department of Computer Science and Engineering, HKBK College of Engineering, Bangalore, Karnataka – 560045, India.

Please see the link here: https://stm.bookpi.org/RAMRCS-V10/article/view/6270

Environmental, Climate and Resource Issues on Earth. Governing Dynamics in Complex Systems | Book Publisher International

 This book is based on the author's prior articles, which were published at the International Dialogo Conference between 2018 and 2021, a conference on science-theology dialogue. It is primarily aimed at a general readership, as it is simple to comprehend and only contains a few portions that require specific mathematics and statistical skills. This book also raises some moral problems about today's excessive consumption and discusses how a fall in religious belief has had terrible consequences for Earth and its inhabitants, particularly in Western civilization. Today's "intelligent" person does not believe in God, preferring instead to prioritise things like money, possessions, and toys, with devastating consequences for his home planet. Its first chapter, based on a set of assumptions, discusses transitions between states of an economic system and describes the early evolution of the first human economy in ancient times, as well as a first transition inside this first economy. This set of assumptions and ideas, which were used to describe the transition between the first two phases of the human economy, are likewise true for all subsequent human economy transitions. The second part of this book examines data available for Earth during the previous fifty years, utilising statistical analysis in the time and frequency domain as a tool, and highlights the magnitude of tragedy and danger that people have inflicted on their own planet. The third and final chapter examines the dynamics of an economic complex system, based on additional assumptions made in each of the models described in the study. These assumptions allow for the estimation and forecasting of a complex economic system's evolution. This estimation can be done in a "classical" manner, that is, without using big data, in which case the results are more prone to errors, or in a "big data" manner, in which case the results are considerably more precise estimations of evolution for the relevant complex systems. This chapter's final chapter employs an additive utility model as a general economic model to estimate additional value in an economy, based on topic assumptions for the various models discussed in this chapter. It also provides a practical solution and instructions for lowering the dangerously exceeded limitations (boundaries) in relation to Earth, building on the principles and theory of utility functions described in the first chapter of the book. Throughout this chapter, the term "with sustainable growth" refers to "ecological growth," which refers to long-term economic systems that consider environmental issues, whereas "without sustainable growth" or "conventional growth" refers to economic systems that do not consider environmental issues. The standard definition of GDP is likewise shown to be inaccurate, inadequate, and antiquated in this third chapter, utilising sustainability as a term. The value generated inside the global economy is significantly lower than computed GDP when practically all costs are taken into consideration. As a result, if the traditional concept of GDP is not to be fully abandoned, it must be thoroughly updated, taking into account all expenditures spent, employing big data information.

Author(s) Details

Catalin Silviu Nutu
Constanta Maritime University, Romania.

View Book:- https://stm.bookpi.org/ECRIEGDCS/article/view/5404

Evolution of Distributed File System and Hadoop: A Mathematical Appraisal | Chapter 11 | Recent Advances in Mathematical Research and Computer Science Vol. 2

 The rapid advancement of technology has had a significant impact on human life. Many conventional systems are being phased out or being replaced by their electronic counterparts. For example, the conventional postal system is being phased out in favour of mobile phones and email. The electronic system offers more features than its traditional counterparts. People can communicate with one another through social media, sharing their views and memories in the form of messages, photographs, or videos. On the other hand, many research efforts are pushed forward in order to improve technologies and knowledge, and data from many sources is gathered in vast quantities for further analysis. In short, today's world is surrounded by a massive amount of data in many forms. This necessitated proper handling of these billions of gigabytes of electronic data, sometimes referred to as BIG DATA. Effective management must be founded on mathematical concepts that have been proved to lower the risk of casualties. The goal of this research is to provide a mathematical evaluation for the evolution of file data distribution and to explain some basic solutions to primitive difficulties using probability theory. If we try to solve an issue like Big Data, probability theory can assist us formalise the basic tree-like architecture of a distributed file system.

Author(S) Details

Atul Saurabh
Department of Java Software Engineer, Rishabh Software, Vadodara, Gujarat, India.

Swapnil M. Parikh
Department of CSE, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India.

View Book:- https://stm.bookpi.org/RAMRCS-V2/article/view/4458

Deming’s: Management’s Responsibility for the Utilization of Statistical Techniques in Industry | Chapter 14 | New Visions in Science and Technology Vol. 6

 William Edwards Deming said in his primary work released in 1953 that better quality through the use of statistical techniques can give enterprises with a competitive edge. He also claimed that in order to accomplish a larger and more effective use of these tools, senior management should understand organisational challenges in addition to becoming familiar with the results of statistical methods. This study examines Deming's initial assumption and evaluates its contribution and lessons for the present post-industrial era, in which BIG Data is gathered and exploited by many companies across a wide range of industries in order to obtain a competitive advantage through statistical analysis. It also dispels the popular idea that analytics-based competition is something fresh and novel, serving as a wake-up call for managers in analytically challenged organisations who will suddenly be underinvesting in a function that has been around for over half a century.

Author (S) Details  

 David Fogarty

Baruch College, Columbia University in the City of New York, New York University, University of Liverpool / City University of New York, New York, USA.

View Book :- https://stm.bookpi.org/NVST-V6/article/view/4145

Collaboration and Entrepreneurship within Digital Environment | Chapter 13 | Insights into Economics and Management Vol. 10

 Companies engage in a variety of international relationships with external stakeholders in order to develop appealing offerings. This book compares and describes the most common online business collaboration concepts in an international setting. The concepts are assessed in terms of their implications for businesses, particularly in the contexts of innovation and big data collaboration. The research is based on the findings of the literature on common online business collaboration concepts. Analyses of their characteristics, as well as an assessment of the implications from the standpoint of a company The key implications for a company include the need for interaction with various external stakeholders in order to improve innovation, opportunities, and access to critical resources, as well as the need to prepare for changes in online networks. While many collaboration concepts are used in contemporary business literature, their application in traditional research varies, and the concepts have not been analyzed and compared in an online environment.

Author (s) Details

Olga A. Shvetsova
School of Industrial Management, Korea University of Technology and Education, South Korea.

View Book :- https://stm.bookpi.org/IEAM-V10/article/view/1475

Efficient Synergetic Filtering in Big Dataset Using Deep Neural Network Technique | Chapter 12 | Advanced Aspects of Engineering Research Vol. 11

 Speech recognition, computer vision, and natural language processing have all benefited from deep neural networks. In this mission, we concentrated on neural network techniques to address the major challenge in synergetic or collaborative -filtering based on the concept of hidden feedback. While deep learning has been employed in a few recent research, it has primarily been employed to create supplementary facts like textual metaphors for things and the acoustic capabilities of music. Matrix factorization is still used for the most significant part of synergetic filtering, communication between customer and object capabilities, and a core product based on hidden customer and object capabilities has been introduced. Artificial Neural Synergetic Filtering (ANSF) is a typical framework for replacing the fundamental makeup with a neural design that can be very efficient in analysing data using a random function. The ANSF is a prominent matrix-factorization framework that is both common and potentially unique. To improve ANSF modelling with non-linearities, we propose utilising a multi-layer perceptron to examine the customer–object contact mechanism. Experiments on real worldwide databases reveal that our suggested ANSF outperforms current approaches significantly. According to research findings, using core layers of artificial neural networks improves overall efficiency. This work improves on existing shallow models for synergetic filtering, paving the way for a new line of research into deep learning-based recommendation.

Author (s) Details

B. Mukunthan
Department of Computer Science, Sri Ramakrishna College of Arts and Science (Autonomous), India, Tamil Nadu, Coimbatore-641021, India.

M. Arunkrishna
PG & Research Department of Computer Science, Jairams Arts and Science College (Affiliated to Bharathidhasan University), Karur - 639003, Tamilnadu, India.

View Book : https://stm.bookpi.org/AAER-V11/article/view/1257

Study on Big Data Options for Small and Medium Enterprises | Chapter 3 |Current Strategies in Economics and Management Vol. 8

 In the media, the terms Cloud Computing and Big Data are also used. Small and medium-sized companies know that in their data, the amount of information they manage could be very large and meaningful. Making and planning decisions. This paper examines the options for small and medium-sized companies to manage large volumes of, or "big" data. This analysis takes into account not only the amount of data, but the variety of data types, the speed at which data is generated and transmitted, as well as the value of data veracity. A possible alternative is cloud computing. Small and medium companies need to deal with big data in view of the above-noted needs. It is shown by all measures that the volume Data is on the rise and will continue to do so simply because of consumer engagement patterns. It leaves small and medium companies with the question of how to interact. A strong choice is the cloud. Several reports, however, say that SMEs are reluctant to reach the cloud. This is probably linked to control issues, particularly ownership. In addition, there is an access problem. Because of the disparity in the way the Internet is used by small and medium enterprises. Realistically, because many smaller businesses still rely on residential broadband and wireless networks for connectivity, the gap is not that shocking. As a recent IDC[1] report estimated, only one-third of small and medium-sized companies have currently invested in networking infrastructure.

Author (s) Details

Dr. Donna M. Schaeffer
Marymount University, USA.

Dr. Patrick C. Olson
National University, USA.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/345