Browse Items (14421 total)
Sort by:
-
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. -
Innovative fish peptide-loaded chitosomes: Advancing bioactive delivery through comprehensive in vitro and in vivo assessments
Considerable advancements have been achieved in controlled delivery systems, yet ensuring optimal stability, bioavailability, and precise targeting of bioactive compounds continues to present challenges. Addressing this gap, the present study explores chitosomeslipid vesicles stabilized by chitosanas a promising approach to enhance the delivery efficiency of bioactive molecules. This study investigate fish peptide-loaded chitosomes, leveraging chitosan's biocompatibility, biodegradability, and encapsulation capabilities. A comprehensive evaluation was conducted under both in vitro and in vivo conditions to assess their potential applications. In vitro studies using L929 cell lines demonstrated high biocompatibility, efficient cellular uptake, and sustained cell viability, with a dose-dependent cytotoxicity profile, leading to early and late apoptosis. The tolerance, favourable metabolic stability, and biomarker responses were validated by in vivo evaluations in Albino Wistar rats, demonstrating systemic efficacy and safety. Moreover, the peptide-chitosome formulation demonstrated a lipid-lowering impact, as evidenced by increases in high-density lipoprotein (HDL) and unsaturated fatty acids and decreases in triglycerides, saturated fatty acid content, and low-density lipoprotein (LDL). These findings highlight the potential of fish peptide-loaded chitosomes as an advanced bioactive delivery system, addressing existing limitations and expanding their applicability in nutraceutical and therapeutic formulations. 2025 The Author(s) -
Innovative Frameworks for LMS Integration in Public Education: Balancing Local Needs with Global Standards
India has made significant progress in using digital technology in education, with Learning Management Systemsplaying a key role. LMS offer scalable platforms for content delivery, assessment, and engagement, enhancing both teaching and learning. They enable personalized learning, simplify administrative tasks, and bridge the digital divide, improving accessibility across geographies. One such initiative, eVidya, provides digital content to school students. This study explores the role of LMS in public education and the challenges of integrating them into government schools, balancing local needs with global standards. The paper analyzes LMS evolution, key features, and benchmarks, with a detailed review of India's DIKSHA system and comparisons with international platforms like Google Classroom, Canvas, Moodle, and Schoology. It looks at country-specific LMS solutions from the US, Europe Australlia and Asia. The research proposes the Integrated eVidya-LMS Implementation Framework for Karnataka's schools, highlighting the potential benefits for India's digital education landscape. 2026, IGI Global Scientific Publishing. All rights reserved. -
Innovative Hybrid Models for Predicting Diabetes: CNN-LSTM Hybrid and Calibrated Soft Voting Model
This study assesses four ensemble techniques - stacking, soft voting, hard voting, and calibrated soft voting - for predicting diabetes onset using the Pima Indians Diabetes dataset. Traditional single-model methods are contrasted with these advanced ensemble approaches, which integrate multiple models to enhance predictive accuracy. The evaluation included metrics such as accuracy, precision, recall, F1 score, and AUC. The CNN-LSTM model was also examined, achieving an accuracy of 75%, precision of 70%, recall of 69%, and an F1 score of 72%. Among the suggested methods, the calibrated soft vote model was the most effective, with improved performance compared to the rest of the techniques. Upcoming studies will address the combination of these models with real-time monitoring systems and deploying their use across a broad range of datasets and medical conditions. 2025 IEEE. -
Innovative implementation, ethical challenges, and future prospects of artificial intelligence in pharmaceuticals
Incorporation of pharmaceutical industry and artificial intelligence (AI) is revolu-tionizing patient care, drug development, and discovery. Drug interactions have been predicted using machine learning techniques, optimal molecular structures have been designed, and clinical trial lengths have been reduced. Ethical problems including violations of data privacy, biassed algorithms, and unequal access to AI-informed treatments have been highlighted in the face of these technologies Reacting to these problems, regulatory models have been debated and passed. It is well known that artificial intelligence can drive therapeutic developments and customize medicine. One expects continuous innovations in artificial intelligence technologies to change the pharmaceutical industry. Ethical standards must be reinforced and openness kept if artificial intelligence is to be fully embraced. Innovation must guide newly developing projects. 2026 by IGI Global Scientific Publishing. All rights reserved. -
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 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 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 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 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 paths to energy efficiency and CO2 reduction in supply chains
The undercurrents of the model provide innovative pathways to energy efficiency and collaboration strategies to reduce CO2 emissions throughout the sustainable supply chain. It highlights the importance of multi- stakeholder engagement in catalyzing transformative change to global systems. By implementing new technologies, enhancing efficiencies, and leveraging renewables, organizations can improve their sustainability performance. Overcoming the barriers to collaboration in this chapter, the challenges and potential solutions of cooperation are discussed, including supportive policy frameworks and collaborative training programs. Ultimately, it sees a better future, one where the capacity for innovation and stakeholder engagement enables a resilient, low- carbon supply chain network that is made even more resilient as we work to align economic outcomes with environmental needs in the age of climate change. 2025, IGI Global Scientific Publishing. All rights reserved. -
Innovative Power Conversion Solutions for Renewable Energy and Electric Mobility
The global transition to renewable energy sources and electrification demands efficient power conversion systems for applications like hybrid electric vehicles (HEVs) and energy storage systems. This paper introduces a novel Multi-Port Bidirectional DC-DC/DC-AC Converter (MBPC) with high efficiency, compact design, and versatile functionality. The MBPC supports two input and two output ports, enabling energy flow between renewable energy sources, storage systems, and loads. Its efficiency exceeds 95%, with a power density of over 10W/cm2. The innovative design minimizes component count, reducing manufacturing costs by 30% compared to conventional converters. Extensive experimentation validates its ability to handle varying current-voltage profiles in multiple operational modes, including DC-DC and DC-AC conversions. With applications in grid-tied systems and electric vehicles, the MBPC addresses efficiency, cost, and flexibility challenges in modern power systems. This work contributes to advancing renewable energy integration and efficient electrification solutions. 2025 IEEE. -
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 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 Technology for Social Good: Real-Time Sign Language Generation Using TensorFlow
Real-time sign language is a basic means of communication for hearing-impaired people. There is a substantial communication barrier between sign language users and those who cannot comprehend sign language. Indian Sign Language (ISL) is built to ease the social challenge between hearing-impaired people and individuals who are unable to understand sign language using TensorFlow featuring Indian languages for smoother communication. The study aims to build a proficient real-time sign language translator using TensorFlow to detect hand signals in real-time video streams. Integration of TensorFlow enables real-time gesture detection, demonstrating how technology can bring about real progress when it comes to improving communication with persons who cannot hear. The application is trained on a specialized dataset comprising different Indian sign language signals, pre-processed to improve gesture recognition focusing on fast and accurate sign language recognition and translation. The objective is to develop a model that can recognize and translate hand gestures into text in Indian languages. This approach uses TensorFlow object detection API to recognize body gestures from real-time videos. The model is trained on a unique dataset of diverse Indian languages that is pre-processed for better recognition accuracy. Techniques like transfer learning are employed to fine-tune the model by integrating CNN for gesture recognition. The detected outputs are after-ward transformed into Indian languages. The systems accuracy may be restricted because of the quality and variety of different Indian languages across the country. The findings indicate that the model can accurately translate the collection of sign languages into text highlighting the potential of TensorFlow Object Detection for real-time sign language. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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. -
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. -
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). -
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. -
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.