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A novel approach using steganography and cryptography in business intelligence
In the information technology community, communication is a vital issue. And image transfer creates a major role in the communication of data through various insecure channels. Security concerns may forestall the direct sharing of information and how these different gatherings cooperatively direct data mining without penetrating information security presents a challenge. Cryptography includes changing over a message text into an unintelligible figure and steganography inserts message into a spread media and shroud its reality. Both these plans are successfully actualized in images. To facilitate a safer transfer of image, many cryptosystems have been proposed for the image encryption scheme. This chapter proposes an innovative image encryption method that is quicker than the current researches. The secret key is encrypted using an asymmetric cryptographic algorithm and it is embedded in the ciphered image using the LSB technique. Statistical analysis of the proposed approach shows that the researcher's approach is faster and has optimal accuracy. 2021, IGI Global. -
A Novel Auto Encoder- Network- Based Ensemble Technique for Sentiment Analysis Using Tweets on COVID- 19 Data
The advances in digitalization have resulted in social media sites like Twitter and Facebook becoming very popular. People are able to express their opinions on any subject matter freely across the social media networking sites. Sentiment analysis, also termed emotion artificial intelligence or opinion mining, can be considered a technique for analyzing the mood of the general public on any subject matter. Twitter sentiment analysis can be carried out by considering tweets on any subject matter. The objective of this research is to implement a novel algorithm to classify the tweets as positive or negative, based on machine learning, deep learning, the nature inspired algorithm and artificial neural networks. The proposed novel algorithm is an ensemble of the decision tree algorithm, gradient boosting, Logistic Regression and a genetic algorithm based on the auto-encoder technique. The dataset under consideration is tweets on COVID-19 in May 2021. 2024 Taylor & Francis Group, LLC. -
A Novel Hybrid Model for Time Series Forecasting Using Artificial Neural Network and Autoregressive Integrated Moving Average Models
Enhancing forecast accuracy while using time series is a potential area of research. Evidences exist in the literature to show that hybrid models can significantly improve the forecasting performance, as they combine the exclusive strengths of different models. This paper presents a novel hybrid model by combining forecasts from Autoregressive Integrated Moving Average (ARIMA) and artificial neural network (ANN) models with suitable weights, thereby improving the forecast accuracy. The methodology employs appropriate error metrics to construct the weights. The paper further demonstrates the efficiency of the proposed methodology through an empirical study, based on two real-world time series data sets. Thus, the new methodology can be used for enhancing the forecast accuracy in a number of fields of research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A novel secured ledger platform for real-time transactions
The present disclosure relates to a new centralized ledger technology with a centralized validation process. It offers a single platform for all categories of real-time transactions and validations, unlike existing conventional blockchain technology. It offers three levels of hashing placed at the generator, server, and validator end for data security from data tampering and two levels of encryption for communication lines between generator-server and server-validator for packet security. This system ensures trustworthiness, authenticity, and CIA (confidentiality, integrity, and availability) to its end users while being real-time in execution. The proposed system does not follow a chain-based file architecture. Due to this, no concept of chain break arises, and the problems that arise as a result of chain break in the blockchain are avoided. 2022 Elsevier Inc. All rights reserved. -
A predictive model on post-earthquake infrastructure damage
Disaster management initiatives are employed to mitigate the effects of catastrophic events such as earthquakes. However, post-disaster expenses raise concern for both the government and the insurance companies. The paper provides insights about the key factors that add to the building damage such as the structural and building usage properties. It also sheds light on the best model that can be adopted in terms of both accuracy and ethical principles such as transparency and accountability. From the performance perspective, random forest model has been suggested. From the perspective of models with ethical principles, the decision tree model has been highlighted. Thus, the paper fulfills to propose the best predictive model to accurately predict the building damage caused by earthquake for incorporation by the insurance companies or government agency to minimize the post-disaster expenses involved in such catastrophic event. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
A Qualitative Enquiry of the Experience of Music Professionals during the COVID-19 Pandemic
Introduction: The COVID-19 pandemic became a new normal in todays world and has changed the consumption pattern and absorption of music and music apps in India. The music industry is relatively non-telecommutable, making working from home difficult during the imposed lockdown and social distancing norms. These conditions had adverse effects on the physical and mental health of music professionals. Therefore, it was crucial to understand the differential impact of COVID-19 on music professionals to find effective solutions and plan for future careers in a changed music industry. Method: The current paper qualitatively explored the experiences of the music professionals participating in this research during the COVID-19 pandemic in India. Twelve participants having 8 years of average professional experience (comprising singers, instrumentalists, music teachers, composers, YouTube content creators) were telephonically interviewed during the second wave of COVID-19 in India. The interviews were analysed using thematic content analysis. Results: The thematic content analysis resulted in the emergence of two major themes identified from the participants narratives were impact on participating music professionals and coping reactions. Conclusion: The themes emerged from analysis highlighted the impact of COVID-19 on these music professionals and the coping reactions utilized by them. 2025 selection and editorial matter, Dr Uzaina, Dr Rajesh Verma with Dr Ruchi Pandey; individual chapters, the contributors. -
A Real-Time Approach with Deep Learning for Pandemic Management
It has never been so critical to managing pandemic situations created by a virus like COVID-19, which has brought the world almost to a standstill, claiming millions of lives. Learning from all earlier viruses and building a quick tackling mechanism is a need of the hour. There is a greater need for technology to collaborate with healthcare and leverage each of the domains expertise. With less time in hand, this collaboration must happen in a short time. There is a need to study the exiting progression in technology and the healthcare landscape to bring them to a common path for practical solutions. In the chapter, an attempt was made to put together some thoughts in both fields to relate them to pandemic managements frequent subject. Caution is drawn towards some crucial aspects, such as security and transparency, that cannot be compromised in this journey. Artificial intelligence (AI), being at the forefront of the technology supporting lives, provides a greater hope in this direction. Some of the prominent approaches can be looked at from a pandemic management point of view, which can start a more in-depth discussion on AI and healthcare going hand in hand in managing this pandemic situation. Essential areas of pandemic management, such as building on the knowledge gathered over a period, plugging in the real-time data from the society, building efficient data management systems and building transparent and interpretable solutions are the focus areas of exploration in this chapter. 2022, Springer Nature Switzerland AG. -
A Reversible Hybrid Architecture forMultilayer Memory Cell inQuantum-Dot Cellular Automata withMinimized Area andLess Delay
CMOS innovation shows limited features when diminishing the size and region of a circuit. The burden of such a technology incorporates higher force utilization and also shows some temperature issues. Quantum-Dot Cell Automata is another innovation which is useful to defeat any of its weaknesses. The reversible rationale is innovation used to diminish the force misfortune in QCA. QCAs are utilized to plan recollections requiring a high working rate. In the following research, construction of reversible memory is proposed in QCA. It is designed by using a 3-layer innovation that altogether has an effect on the decreased size of the circuit. The reversible memory proposed here has 61% increase in cell number, with a 74% enhancement in the territory inhabitance, and 59% decrease in delay contrasted with any previous optimal designs. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Review of Deep Learning Methods in Automatic Facial Micro-expression Recognition
Facial expression analysis to understand human emotion is the base for affective computing. Until the last decade, researchers mainly used facial macro-expressions for classification and detection problems. Micro-expressions are the tiny muscle moments in the face that occur as responses to feelings and emotions. They often reveal true emotions that a person attempts to suppress, hide, mask, or conceal. These expressions reflect a persons real emotional state. They can be used to achieve a range of goals, including public protection, criminal interrogation, clinical assessment, and diagnosis. It is still relatively new to utilize computer vision to assess facial micro-expressions in video sequences. Accurate machine analysis of facial micro-expression is now conceivable due to rapid progress in computational methodologies and video acquisition methods, as opposed to a decade ago when this had been a realm of therapists and assessment seemed to be manual. Even though the research of facial micro-expressions has become a longstanding topic in psychology, this is still a comparatively recent computational science with substantial obstacles. This paper a provides a comprehensive review of current databases and various deep learning methodologies to analyze micro-expressions. The automation of these procedures is broken down into individual steps, which are documented and debated. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A review of reinforcement learning approaches for autonomous systems in industry 4.0
[No abstract available] -
A Review on Influence of Cutting Fluid on Improving the Machinability of Inconel 718
Nickel-based superalloys are widely used in the production and manufacturing sectors that require processes or applications that endure or operate at very high superheating temperatures. With the properties of high tensile strength, high melting point, and lightweight structural arrangement of molecules within the alloy material composition makes it more suitable for industrial utilization in aerospace industries and marine applications. This review paper discusses the use of various coolant lubricants that improves the machinability of Inconel 718 based on parameters such as surface roughness and tool wear under the influence of cutting speed, feed rate, and depth of cut. The machine used for analysis is CNC milling machine which will be used for experimentation using ceramic inserts as end milling tool. Various cooling techniques such as hybrid cooling, flood emulsion cooling, minimum quantity lubrication, and cryogenic cooling are being summarized in this paper from various experimentations and conclusions of other authors. On the basis of review, the hybrid cooling technique is found to be better than other cooling techniques because of its ability to obtain long tool life and smoother surface finish on the workpiece. With the use of these reviewed data, further research for finding a more compatible and effective cooling lubricant has to be done by experimentation in order to obtain an improved machining process for Inconel 718 material. 2020, Springer Nature Singapore Pte Ltd. -
A Review on Preprocessing Techniques for Noise Reduction in PET-CT Images for Lung Cancer
Cancer is one of the leading causes of death. According to World Health Organization, lung cancer is the most common cause of cancer deaths in 2020, with over 1.8 million deaths. Therefore, lung cancer mortality can be reduced with early detection and treatment. The components of early detection require screening and accurate detection of the tumor for staging and treatment planning. Due to the advances in medicine, nuclear medicine has become the forefront of precise lung cancer diagnosis. Currently, PET/CT is the most preferred diagnostic modality for lung cancer detection. However, variable results and noise in the imaging modalities and the lung's complexity as an organ have made it challenging to identify lung tumors from the clinical images. In addition, the factors such as respiration can cause blurry images and introduce other artifacts in the images. Although nuclear medicine is at the forefront of diagnosing, evaluating, and treating various diseases, it is highly dependent on image quality, which has led to many approaches, such as the fusion of modalities to evaluate the disease. In addition, the fusion of diagnostic modalities can be accurate when well-processed images are acquired, which is challenging due to different diagnostic machines and external and internal factors associated with lung cancer patients. The current works focus on single imaging modalities for lung cancer detection, and there are no specific techniques identified individually for PET and CT images, respectively, for attaining effective and noise-free hybrid imaging for lung cancer detection. Based on the survey, it has been identified that several image preprocessing filters are used for different noise types. However, for successful preprocessing, it is essential to identify the types of noise present in PET and CT images and the appropriate techniques that perform well for these modalities. Therefore, the primary aim of the review is to identify efficient preprocessing techniques for noise and artifact removal in the PET/CT images that can preserve the critical features of the tumor for accurate lung cancer diagnosis. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A secure bio-hash-based multiparty mutual authentication protocol for remote health monitoring applications
Remote health monitoring can benefit a large number of stake holders in healthcare industry, and it has the potential to make healthcare facilities available to a large number of masses at a reduced cost. Wireless Body Area networks (WBAN) comprising of sensors, capable of capturing and transferring physiological parameters of patients, provide an efficient and cost-effective solution for remote health monitoring. Data security is one among the major concerns preventing the widespread adoption of this technology by patients and healthcare sector. This chapter on remote health monitoring, presents a biometric-based authentication protocol. The work also proposes a multiparty mutual authentication protocol for authenticating the entities, such as users, sensors, personal devices, and medical gateway, participating in a WBAN. In the proposed protocol, a verifier table is not required to store the password of users. Formal security analysis and verification of the discussed protocols are performed using Scyther tool, and the results reveal that the protocols are resistant to privileged-administrator resilience attack, man-in-the-middle attack, replay attack, and impersonation attack. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. -
A Secure Resilient Scheme for Autonomous Vehicles against External Attacks
Autonomous vehicular ad hoc networks are networks created with autonomous vehicles and other entities in the vehicular environment. Like traditional vehicular ad hoc networks, autonomous ad hoc networks are also prone to internal and external attacks. Many authentication schemes are proposed to overcome internal attacks, whereas external attacks are not focused on. Though the impact of external attacks is less when compared to that of internal attacks, external attackers observe and analyze the network traffic information, which will be helpful for the internal attackers to affect the performance of the network. Hence, this chapter proposes a secure identity-based authentication scheme without pairings against external attacks. It uses an elliptic curve cryptography-based identity-based signature to authenticate vehicles. The proposed authentication scheme ensures secure vehicular communications, including inter-vehicular communication, without RSUs during emergencies. Simulation results demonstrate its superior performance. 2024 River Publishers. All rights reserved. -
A secured predictive analytics using genetic algorithm and evolution strategies
In the banking sector, the major challenge will be retaining customers. Different banks will be offering various schemes to attract new customers and retain existing customers. The details about the customers will be provided by various features like account number, credit score, balance, credit card usage, salary deposited, and so on. Thus, in this work an attempt is made to identify the churning rate of the possible customers leaving the organization by using genetic algorithm. The outcome of the work may be used by the banks to take measures to reduce churning rates of the possible customers in leaving the respective bank. Modern cyber security attacks have surely played with the effects of the users. Cryptography is one such technique to create certainty, authentication, integrity, availability, confidentiality, and identification of user data can be maintained and security and privacy of data can be provided to the user. The detailed study on identity-based encryption removes the need for certificates. 2020 by IGI Global. All rights reserved. -
A Smart Internet of Things (IoT) Enabled Agricultural Farming System
Industry 4.0 has brought about a profound revolution in recent times. This advancement profoundly impacted technology usage in every aspect and has significantly improved businesses. Agriculture is one of the evergreen economic contributors to Indias GDP. With improvements in adaptability in this sector, the time is ripe for instituting IoT (Internet of Things)-based smart agriculture. Water scarcity and drastic climate change are real issues affecting crop yields, leading to the failure in the timely fulfillment of market demand (Nawandar 2019). The authors have collaborated to address these concerns by creating a system comprising a functional hardware prototype and an android application for regulating irrigation and temperature. The introduction of IoT (Internet of Things) automates crop monitoring and reduces labor costs. By using IoT, (Internet of Things) an earmarked agricultural field is covered with sensors. The sensors are concealed so as not to be affected by the bleakness of the external environment. These sensors work in tandem with drip irrigation following the sensed climatic conditions. The water is pumped directly to the root zone in an optimally sensed manner. The authors developed and tested the system successfully in a greenhouse system. The process initially aims to extract the values of soil parameters by using IoT (Internet of Things) sensors and appropriately control the watering of crops, thus enabling the cultivation of crops even in a hot and dry climate. Crops can be irrigated from a remote location and their temperature can be meticulously regulated to ensure they remain within an optimal range. Water utilization for agricultural crops is optimized with the use of automated irrigation systems that use W.S.N (Wireless - Sensor-Networks) and G.P.R.S (General-Packet-Radio-Service) modules. The algorithm employed in the system to control water usage is based on the needs of the crop and the terrain. The entire system is powered by photovoltaic panels, which are useful in rural and isolated areas without electricity (Raut and Shere 2014). A cellular network is used for duplex communication. Continuous monitoring and irrigation schedule programming are used by web apps to manage irrigation. This is also possible using a browser and web pages. A system with three identical automatic irrigation systems can save water use by up to 90%. 2024 by Nova Science Publishers, Inc. All rights reserved. -
A Spatio-temporal Model for the Analysis and Classification of Soil Using the IoT
The Internet of Things (IoT) is an evolving trend in the field of computer applications where various hardware and software are connected together to address a specific problem. With the help of the IoT, the world has become smart and enabled itself to connect various objects (e.g., cars, computers, mobile phones, and smart appliances) with distinctive Internet protocol addresses, which allows them to interact with one another, thus accomplishing various procedures. Applications of the IoT include but are not restricted to smart cities, healthcare, industry, and robotics. Amongst a huge list of applications furnished by the IoT, agricultural IoT is the theme of this chapter. The IoT in agriculture transforms entities such as crops, soils, and livestock in a smart way by utilizing underlying technologies such as embedded systems, pervasive computing, sensor networks, ubiquitous computing, ad hoc networks, various wireless communication technologies, Internet protocols and other advanced technologies. The research here focuses on the most important agriculture entity soil. It is the soil that determines the yield of a crop. The more fertile the soil, more qualitative is the yield. The main idea behind the research is to identify the soil most suitable for agriculture. Using a spatio-temporal model, the soil samples collected from various parts of the country are classified into agricultural soil and non-agricultural soil. This classification is done by the aid of features such as the pH of the soil, and its humidity, moisture, and temperature collected from IoT sensors. The chapter begins with an introduction to the usage of IoT technology in different areas of agriculture followed by an account of the proposed state-of-the-art model, and its results, analysis, and a conclusion. 2022 selection and editorial matter, Vikram Bali, Vishal Bhatnagar, Deepti Aggarwal, Shivani Bali, and Mario JosDiv; individual chapters, the contributors. -
A study exploring the effect of subliminally priming known human faces vs. unknown human faces on product selections by consumers: Unseen motivators
Unconscious thoughts more than often are seen to precede conscious contemplations of the surroundings. The Present chapter attempts to explore how subliminal priming of known and unknown human faces could impact product selection and decision-making time of consumers. 2 (Known face X Unknown face) X 2 (Product selection X Decision-making time) within-subject design was used for the study. A stimulus-priming experiment designed in E-prime software was used to subliminally expose the participants to both known and unknown human faces They were then asked to select a product that they were willing to buy from an option of four products, of which one of the products was primed along with Human face (Known Vs Unknown). The product selection rates as well as the time taken to select the product were recorded. A total of 100 Participants falling in the age category of young adults (18-39) took part in the study. The chapter discusses the results and dives deeper into the implications that they hold in the world of marketing. 2024, IGI Global. All rights reserved. -
A study of mediating factors influencing student motivation and behaviour: A literature review
This study aims to examine the mediating factors of learning environment, technology, good behaviour, and effective communication, sports, media, problem-based case study, and psychological influence on student motivation and behaviour. The study employs secondary data based on peer-reviewed articles, conference papers, and books collected from a variety of sources such as Scopus, WoS, J-Store, EBSCO, ProQuest, etc. between 1982 and 2022. The 103 articles have been selected based on the keywords which related to study. The results evidenced that the mediating factors have a significant positive impact on student motivation and behaviour. This study contributes to the existing literature by providing a com- prehensive understanding of the relationship between the mediating factors and student motivation and behaviour. The findings of this study have implications for educational institutions, policy makers, and educators in creating effective learning environments that can enhance student motivation and behaviour. 2023, IGI Global. All rights reserved.