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Insights into Artificial Neural Network techniques, and its Application in Steganography
Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions. 2021 Institute of Physics Publishing. All rights reserved. -
Insight into the effects of waste vegetable oil on self-healing behavior of bitumen binder
The application of waste vegetable oil (WVO) in bitumen has been the subject of research for years, however, the self-healing behavior of WVO modified bitumen (WMB) has not been adequately reported. In this research, molecular dynamics (MD) simulations and laboratory experiments were performed to reveal the effects of WVO on the self-healing behavior of bitumen. Models of base bitumen and WMB were constructed. Further, dynamic calculations were carried out for the self-healing models of base bitumen and WMB both with 10 microcracks. The energy properties, conformation and density of bitumen during the self-healing process were analyzed. Meanwhile, the effects of WVO on the fractional free volume (FFV) of bitumen, the distribution of bitumen components and the mobility of bitumen molecules were investigated. Finally, the modified fatigue-healing-fatigue (FHF) test was conducted to verify the effects of WVO on the self-healing efficiency of bitumen. Results show that Van der Waals forces drive the mobility of bitumen molecules. Along with the disappearance of the central microcrack, the density of the self-healing system gradually increases and finally reaches that of the bulk bitumen. WVO with superior mobility capacity increases the FFV of bitumen and converts asphaltene large aggregated structure into small aggregated structure, which facilitates the mobility of the bitumen during the self-healing process. Thus, the addition of WVO contributes to the self-healing efficiency of the bitumen. The modified FHF test also verified that the self-healing efficiency of bitumen is improved with the presence of WVO. These findings provide further insight into the self-healing behaviors of WMB. 2022 -
Insider attack detection using deep belief neural network in cloud computing
Cloud computing is a high network infrastructure where users, owners, third users, authorized users, and customers can access and store their information quickly. The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently. This cloud is nowadays highly affected by internal threats of the user. Sensitive applications such as banking, hospital, and business are more likely affected by real user threats. An intruder is presented as a user and set as a member of the network. After becoming an insider in the network, they will try to attack or steal sensitive data during information sharing or conversation. The major issue in today's technological development is identifying the insider threat in the cloud network. When data are lost, compromising cloud users is difficult. Privacy and security are not ensured, and then, the usage of the cloud is not trusted. Several solutions are available for the external security of the cloud network. However, insider or internal threats need to be addressed. In this research work, we focus on a solution for identifying an insider attack using the artificial intelligence technique. An insider attack is possible by using nodes of weak users systems. They will log in using a weak user id, connect to a network, and pretend to be a trusted node. Then, they can easily attack and hack information as an insider, and identifying them is very difficult. These types of attacks need intelligent solutions. A machine learning approach is widely used for security issues. To date, the existing lags can classify the attackers accurately. This information hijacking process is very absurd, which motivates young researchers to provide a solution for internal threats. In our proposed work, we track the attackers using a user interaction behavior pattern and deep learning technique. The usage of mouse movements and clicks and keystrokes of the real user is stored in a database. The deep belief neural network is designed using a restricted Boltzmann machine (RBM) so that the layer of RBM communicates with the previous and subsequent layers. The result is evaluated using a Cooja simulator based on the cloud environment. The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine. 2022 CRL Publishing. All rights reserved. -
Inquiry into reverse logistics and a decision model /
International Journal Logistics Systems And Management, Vol.33, Issue 3, pp.353-382, ISSN No: 1742-7967. -
Inquiry into reverse logistics and a decision model
A process in which a product is moving in reverse along the supply chain network is called as reverse logistics. The objective of reverse logistics is to recapture the value of the final product. Reverse logistics is gaining ground because of its importance in managing a closed loop supply chain. Companies across the world are showing concern about environmental degradation and are realising the need for sustainable development. Many countries have already passed legal regulations. Good reverse logistics process indicates lot of reuse, recycling and reducing the material consumed, thereby ensuring sustainability. Improving reverse logistics will result in increase in sales up by 10%, a reduction in the supply chain costs by 25% to 40%. In retail sector the profit margins are less and strengthening reverse logistics can increase the profit margins. This paper attempts to inquire into the reverse logistics area and come out with the prioritised variables impacting the different reverse logistics disposition decision. The paper attempts to propose a conceptual model for reverse logistics disposition decision. Copyright 2019 Inderscience Enterprises Ltd. -
Inplane Lateral Load Behaviour of Masonry Walls
Masonry is one of the commonly used construction technology both in urban and rural areas. In this paper the in-plane behaviour of masonry walls is analytically studied considering existing closed form equations. Previous studies have proven that the lateral load behaviour mainly depends on the aspect ratios (h/L) as well as the axial loads. From this analysis the governing failure is determined and the lateral load versus lateral deflection curve is plotted for various percentages of axial loads. This graph gives the ductility of the wall. This concept is further applied to a simple masonry structure and the push over curve is plotted. 2020, Springer Nature Switzerland AG. -
Inphase and outphase concentration modulation on the onset of magneto-convection and mass transfer in weak electrically conducting micropolar fluids
The paper analyses the effect of concentration modulation at the onset of solute magneto-convection and heat transfer in a weak electrically conducting fluid by carrying out a linear and non-linear analysis. The Venezian approach is assented encompassing the correction Solute Rayleigh number and wave numbers for meagre amplitude concentration modulation. A multiscale method is applied to convert the analytically untraceable Lorenz model to an analytically traceable Ginzburg-Landau equation which is solved to quantify mass transfer through Sherwood number. It is observed that concentration modulation results in sub-critical motion however out-of-phase concentration modulation is more stable compare to others. 2019 Author(s). -
Inpatient complaining behaviour: A study on the overt and covert behaviour of inpatients in Indian hospitals
Consumer dissatisfaction and complaining behaviour have always been a topic of discussion in educational institutes and industries alike. Whereas dissatisfaction with product purchases and subsequent returns or associated consumer responses is very common, the same in the service sector has been quite different. In India, it is not only the patient who decides, which healthcare service to opt for, because Indians are culturally embedded in a system of collective consumption where other family members or relatives or friends also influence their decision-making. This paper is an exploratory study done to comprehend the chosen behavioural responses of dissatisfied inpatients in India through a questionnaire survey. The survey followed a retrospective recall technique in which the recall window was fixed at six months. The sampling technique followed was probability sampling. The data collection tool was structured and self-administered questionnaire administered in the sampled nine districts of Kerala. A good number of respondents attributed their overt complaining behaviour to lack of cordiality of doctors, nurses or the attending staff and lack of proper care and concern from doctors or nurses. Post complaining, service recovery was found to be satisfactory for most of the complainers. 2020, Kamala-Raj Enterprises. All rights reserved. -
Inorganic Nanoparticles in Cosmetics
Inorganic nanomaterials of different chemical compositions and morphologies have been applied in cosmetic products due to their size- and shape-dependent properties which can improve the performance of the products. This chapter discusses the application of inorganic nanoparticles in cosmetic products with an emphasis on the characteristic features of nanoparticles suitable for cosmetic applications. In particular, applications of inorganic nanoparticles as UV filters and antimicrobial materials are discussed in detail with a basic overview of the fundamental scientific basis related to these applications. Types of nanoparticles used in commercial cosmetic products are enlisted, reflecting the range of applications and property modifications. Applications of inorganic nanoparticles in cosmetic formulations as active components and nanocarriers are also discussed along with relevant examples. Springer Nature Switzerland AG 2019. -
Innovative strategies for urban construction optimization in the IoT era
This abstract explores the urgent need for creative techniques to improve urban building via the lens of the Internet of Things (IoT), which is becoming increasingly prevalent in the context of broad use of IoT technologies. The Internet of Things (IoT) solutions are becoming more widespread in urban areas, which has resulted in an increase in the demand for innovative tactics that may successfully exploit technology breakthroughs in urban development. In this article, both the opportunities and the difficulties that have arisen as a result of the Internet of Things age are discussed, with a special focus placed on the necessity of rethinking the conventional paradigms that have been used in urban planning. Through the examination of cutting-edge methodology and case studies, the purpose of this article is to shed light on how cities might utilize Internet of Things technology to improve the efficiency of their infrastructure, the distribution of resources, and the delivery of public services. The core of this study is a comprehensive investigation of the dynamics of urban population and public infrastructure, which provides urban planners and policymakers with insights that can be put into a practical application. When it comes to handling the challenges of modern urbanization, these results will show to be quite beneficial in this era of the Internet of Things. 2024, IGI Global. All rights reserved. -
Innovative recruitment channels: Leveraging social media and virtual job fairs for talent acquisition
Amidst the dynamic realm of talent acquisition, organizations are increasingly adopting inventive approaches propelled by technological progressions and shifts in candidate conduct. Employing social media platforms has evolved into a crucial strategy for engaging, retaining, and attracting top talent. Ethical considerations, data-driven insights, and compliance are critical factors that significantly influence recruitment practices. Emerging virtual job marketplaces provide employers with novel opportunities to network with prospective employees. By embracing digital transformation, leveraging emerging technologies, and prioritizing candidate experience, organizations can remain competitive in attracting and retaining talent in today's dynamic job market. 2024, IGI Global. -
Innovative Natural Disaster Precautionary Methods Through Virtual Space
Humancomputer interaction is the study of a human and computer interaction in which we analyze and create an interface between the humans and the computer to decide to which extent it is possible to interact with computers which change the way of the usual lifestyle that can evolve the future generations according to the humans convenience. Virtual reality environments in natural disasters are to train people to overcome or prevent their lives from risky situations. When it comes to natural disasters, people never know when such disasters strike in their daily lives, so it is necessary to be prepared to face such consequences. Though the rescuers are there to save the lives of the people, it is not possible to wait for the rescuers all the time, and the situations may also be even worse than the expected. It becomes highly impossible to take precautionary measures; therefore, after the warning of the disaster, people can prepare themselves to survive such situations without the help of rescuers. Different disasters happen in different landscapes; for example, Tsunami occurs in the sea, floods occur as a temporary disaster that covers the land with water, usually not covered by water, and many other disasters that cause life and damage property. Therefore, with the help of virtual reality simulation, people can be trained according to the scenarios or the natural disaster created by the computer-generated 3D environment where the trainee can interact and perform actions generated based on the scenarios. In the virtual world, provided in the head-mounted display, the user can be trained upon by first instructing what to be done and later, after understanding the situation, the trainee is put into a natural disaster scenario where he performs the precautionary measures that need to be done based on the scenario and prepare accordingly in such situations so that before the arrival of the rescuers, people would be more aware of what measures to be taken and react accordingly in such a way that it reduces the risk of life. The chapter further explains in detail about humancomputer interaction (HCI), virtual reality (VR), advantages and disadvantages of virtual reality, various natural disasters, and the role and impact of VR environment in creating awareness and providing precautionary measures for preventing natural disasters. When it comes to immersive technology and smart cities, it is equally important to make everything smart according to the changing generations and technologies in our day-to-day lives. On the other hand, when dealing with people to make them understand and educate things, we must also enhance teaching and make them feel interested in whatever we impose on them. So, when we give the people a 360-degree view or a three-dimensional view of the scenarios, it helps them experience like they are actually into the scenario to understand and make immediate decisions. The advantage of using such immersive technology is that when errors or misjudgments are made to learn from the mistakes and correct it, it helps them understand the scenario and take spot and efficient decision at the time of disasters which will have a significant impact on rescuing the lives of the people. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Innovative Method for Detecting Liver Cancer using Auto Encoder and Single Feed Forward Neural Network
Liver cancer ranks sixth among all cancers in frequency of incidence. A CT scan is the gold standard for diagnosis. These days, CT scan images of the liver and its tumor can be segmented using deep learning and Neural Network techniques. In this proposed approach to identifying cancer cells, it's focus on four important areas: To enhance a photo by taking out imperfections and unwanted details. An ostu method is used for this purpose. Specifically, this proposed approach to use the watershed segmentation technique for image segmentation, followed by feature extraction, in an effort to isolate the offending cancer cell. After finishing the model training with AE-ELM. To do this, Extreme Learning Machine incorporates an auto encoder. To achieve effective and supervised recognition, the network's strengths of Extreme Learning Machine (ELM) are thoroughly leveraged, including its few training parameters, quick learning speed, and robust generalization ability. The auto encoder-extreme learning machine (AE-ELM) network has been shown to have a respectable recognition impact when the sigmoid activation function is used and the number of hidden layer neurons is set to 1200. According to the results of this investigation, a method based on AE-ELM can be utilized to detect the liver tumor. As compared to the CNN and ELM models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE. -
Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN
Brain illnesses are notoriously challenging because of their fragility, surgical complexity, and high treatment costs. Contrarily, it is not obligatory to carry out the operation, as the outcomes of the procedure may fall short of expectations. Adult-onset Alzheimer's disease, which causes memory loss and losing information to varied degrees, is one of the most common brain diseases. This will vary from person to person based on their current health situation. This highlights the need of using CT brain scans to classify the extent of memory loss and determine the patient's risk for Alzheimer's disease. The four main goals of Alzheimer's disease detection are preprocessing the data, extracting features, selecting features, and training the model with GP-ELM-RNN. The Replicator Neural Network has been utilized earlier for AD detection, however this study offers an improved version of the network, modified with ELM learning and the Garson algorithm. From this study, it is deduced that the proposed method is not only efficient, but also quite precise. In this research, GP-ELM-RNN network is built to four groups of images representing different stages of Alzheimer's disease: very mildly demented, mildly demented, averagely demented, and non-demented. The class of very mildly demented patients was found to have the highest accuracy (99.1%) and specificity (0.984%). As compared to the ELM and RNN models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE. -
Innovative Leadership and Sustainable Development: Exploring Resource-Efficient Strategies Among Indian Managers
Global sustainability challenges demand innovative leadership approaches, particularly in resource-constrained environments where traditional models may fall short. The current study aimed to explore the need for adaptive, resource-efficient leadership by examining key traits such as empathy, inclusivity, adaptability, and intuitive decision-making that allows managers to balance immediate organizational demands with long-term sustainability goals. A Qualitative-Interpretative Phenomenological Approach was adopted. A total of 28 senior Indian managers were interviewed to understand traits aligned with stakeholder theory and the resource-based view, positioning them as strategic assets for achieving sustainable competitive advantage. Findings revealed that culturally resonant practices of frugality and resilience support social innovation and organizational resilience, contributing to broader sustainable development objectives. Further, it advances sustainable leadership literature by showcasing how resource efficiency and contextual adaptability can foster impactful, sustainable practices, particularly within emerging economies. 2024 ERP Environment and John Wiley & Sons Ltd. -
Innovative instructional strategies that motivate students to learn during the pandemic
Coronavirus disease (COVID-19) has spread all over the world affecting public health at the outset of 2020. This has slowed down almost every sector of human life including education. However, few of the private educational institutes have risen to the occasion immediately and have continued providing education online. Teachers were forced to adapt themselves to teach online. Students started to attend online classes from home. Inevitably, parents had to invest in purchasing computers or smartphones and internet connections to support their children's education. Though all these changes began with an overwhelming spirit from all stakeholders, in no time it has become a monotony. This has led to the present study to find innovative instructional strategies that can motivate learners to learn and sustain interest in learning during pandemics. The present study employs a qualitative research design to address this issue. 2022, IGI Global. -
Innovative constraints formulation in timetable planning for efficient resource allocation in academic institutions
Many developing nations still rely on manual timetable scheduling in academic institutions, leading to inefficiencies. However, advancements in technology have introduced software solutions such as FET (free evolutionary timetabling) to automate the process. In a study, the authors successfully implemented FET to automate timetable creation at a university, reducing the time required from days to seconds. Scheduling in universities with elective courses poses challenges, includingavailable rooms, faculty availability, and guest lecturers. The authors propose a unique timetable generation process that considers post-pandemic social distancing measures. This process addresses various complex constraints faced by academic institutions and holds potential for reopening institutions in a cautious manner following the pandemic. 2023, IGI Global. All rights reserved. -
Innovations in teaching-learning and evaluation: An overview of processes undertaken at CHRIST (Deemed to be University)
The term 'teaching-learning' intrinsically expresses the ongoing learning process that every educator constantly experiences; to teach is to learn and to engage in knowledge updation continually. Indeed, it may be argued that the very basis of being a teacher is the facilitation of one's own learning opportunities and skill sets. In investigating the evolution of teaching-learning processes at CHRIST, one may define the university's growth using the key concept of 'innovation '. Whether it be the humanities, social sciences, life sciences, or business studies, innovations in teaching-learning methods are imperative in any globally conscious education system today. Two of the key areas of focus in terms of innovations in the teaching learning process are the practical application of knowledge and learnt skills. 2021, IGI Global. -
Innovation journey: Unleashed business applications framework
This chapter gives a thorough framework for understanding the dynamics and applications of innovation in the business environment. In today's fast-paced and competitive world, innovation is essential for organizational growth, adaptation, and sustainability. This study takes a descriptive approach, addressing the complexities of creativity across multiple dimensions. This chapter offers a conceptual overview before examining the various aspects of innovation and its function as a driving force behind strategic initiatives and a means of fostering competitive advantage. It provides a thorough study that clarifies the various stages of the innovation process, from ideation to optimization, emphasizing key challenges and opportunities at each level. It provides a road map for businesses looking to foster an innovative culture and use it as an outlet for value creation and competitive advantage by adopting a comprehensive viewpoint. 2024, IGI Global.