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Analysis of a Fractional Stage-Structured Model With CrowleyMartin Type Functional Response by Lagrange Polynomial Based Method
The dynamics of a stage-structured predator-prey system that replicates interactions between two densities of prey and predator populations were investigated in this work. The adult predator population and the juvenile predator are the two compartments that make up the predator population in the model. The predator relies on both prey and juvenile predator, which is another element of the paradigm that can be termed cannibalism. CrowleyMartin type functional denotes the nature of the interaction between prey and adult predators, while Holling type-I functional response denotes the nature of contact between juvenile and adult predators. The concept of memory is introduced in the form of the Caputo fractional derivative to reflect the complicated dynamics of interaction among the species. As a result, the model is able to incorporate all relevant historical information about the occurrence, from its inception to the desired time, into its calculations. We have also investigated the boundedness and existence and uniqueness of solutions to the proposed model. The condition of existence and stability of various points of equilibrium are investigated. The numerical simulations are performed by using the Lagrange polynomial-based method which is novel in the field of mathematical biology. Simulations have been accomplished to examine the significance of parameters related to cannibalism, the conversion rate from prey to adult predator, harvesting of an adult predator, and growth rate of juvenile predators on the overall behavior of the system. The noteworthy performance of the fractional operator on the anticipated predator-prey models dynamical behavior is well demonstrated by numerical results. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Decrypting Free Expression: AMMA-WCC Conflict and Comment Culture Rattling the Malayalam Film Industry
The chapter examines the gender-power dynamics in the Malayalam film industry through an analysis of a skit, a YouTube video and trolls related to a recent controversy involving the Association of Malayalam Movies Artistes (AMMA) and the Women in Cinema Collective (WCC). This analysis is supported by an exploration of the historical roots of sexism in the industry and a discussion about how it continues to perpetuate sexism in the industry. The study also investigates the emergence of WCC as a response to the actresss molestation case and the subsequent division within the industry. The research focuses on the Sthree Shaktheekaranam skit performed at AMMAs cultural show, a YouTube video, Oru Feminichi Kadha and a sample of trolls which targeted the WCC and women who refuse to comply with AMMAs patriarchal bias. The chapter analyses the content of these representations, highlighting the power play structuring them. The study sheds light on the contradictions and hypocrisy within the industry and its portrayal of progressive values while perpetuating regressive gender norms. 2024 selection and editorial matter, Francis Philip Barclay and Kaifia Ancer Laskar; individual chapters, the contributors. -
Question-answering versus machine reading comprehension: Neural machine reading comprehension using transformer models
Teaching machines to read and learn natural language documents and seek answers to questions is an elusive task. Traditional question-answering systems were based on rule-based and keyword-searching algorithms without proper natural language understanding. Machine reading comprehension (MRC) belongs to reading comprehension models and facilitates the machines learning from context. MRC can infer the answer from the context through language understanding. Neural machine reading comprehension has built reading comprehension models by employing the advancements of deep neural networks that have shown unprecedented performance compared to other non-neural and feature-based models. The article comprises the MRC span extraction tasks using Transformer models and, in addition, the illustration of the MRC tasks, trends, modules, benchmarked datasets, implementation, and empirical results. 2024 selection and editorial matter, Muskan Garg, Sandeep Kumar and Abdul Khader Jilani Saudagar chapters. -
Software Quality Prediction by CatBoost: Feed-Forward Neural Network in Software Engineering
Software quality is the key aspect of every software organization. Multiple frameworks and algorithms are essential to ensure quality. However, multiple software failures occur uninvited. There are multiple aspects that skew a softwares efficiency. Now the software quality analysis framework mostly focuses on design flaws and test plans done during development. To overcome this problem of software failure, this research proposes a prediction for software efficiency analysis in software engineering using enhanced feed-forward neural network machine learning classification with CatBoost. This research also evaluates the parameters of efficiency of each software component before implementation. This proposed work also analyses the basic aspects that need to be ensured before the design phase of any software. 2024 Taylor & Francis Group, LLC. -
Digital twin technologies for automated vehicles in smart healthcare systems
The idea of being comfortable seems appealing to a vast majority of people, from the start humans were always dependent on something. First the tools were invented and with the help of the tools, amazing things were built. From the invention of the wheel to the steam-powered machines and now the introduction of electronic automation, digitization and making intelligent production processes is the need for todays industry. Industry 4.0 is now the standard by which businesses must measure their progress. It enables businesses to reinvent themselves. Manufacturing systems go beyond simple connections here, communicating, analyzing, and using data to drive more intelligent activities. It combines Internet of Things (IoT), analytics, additive manufacturing, robots, artificial intelligence, sophisticated materials, and augmented reality. The autonomous vehicle (AV) is one of the applications of Industry 4.0. AVs can make passenger transfers more efficient. Furthermore, smart sensors, when combined with cognitive computing and IoT, portray an AV as a cyber-physical system where data from all relevant viewpoints is closely monitored and synced between physical devices and the cyber computational realm. By utilizing sophisticated information analytics, AVs will be able to work more effectively, collaboratively, and resiliently. As a result, AVs might be able to work with Industry 4.0 systems. 2023 Elsevier Inc. All rights reserved. -
Application and challenges of optimization in Internet of Things (IoT)
[No abstract available] -
DELHI: A NOVEL by Khushwant Singh
[No abstract available] -
Economic Sustainability, Mindfulness, and Diversity in the Age of Artificial Intelligence and Machine Learning
The sustainability of artificial intelligence (Al) and machine learning (ML) requires human diversity and mindfulness. This chapter discusses the various ways in which AI and ML can interact with humans to improve society, e.g., in filing copyrights or design patents or increasing mindfulness. AI and ML could educate weavers and farmers about their legal rights, cultivation methods, banking processes, and the harmful effects of tobacco consumption and other health-related issues. AI and ML could help teach mindfulness. ML can measure additional biofeedback. Music, mathematics, and art may benefit from AI and machine learning. Human-technology relations and the blue-green deployment model can be used to maintain two independent infrastructures or duplicate feature stores. It is possible to cultivate mindfulness and an awareness of diversity and communal harmony through AI and machine learning, as AI and machine learning can infer the emotional and cognitive states of the people with whom they interact. By leveraging the entire process of visualization, reading, and listening with AI, machine learning, and beyond, the digital future has the potential to incorporate real-time emotions and feelings. This would entail emotional responses on both ends and a variety of other technologies and users. 2024 Taylor & Francis Group, LLC. -
Design of Machine Learning Model for Health Care Index during COVID- 19
Predicting stock prices and index movement in the field of finance is always challenging. The events in the macro-economic framework affect the trends of the market and the COVID-19 pandemic was a major reason for the slowdown of the global economies in the short run. It was assumed that the healthcare industry has completely been transformed due to changing behavioral habits of individuals. The study presents the time series approach with the help of historical prices on the Bombay Stock Exchanges (BSE) Health Care Index, both in the long and short run, using the ARIMA model. The period of the study is from February 1999 to August 2020. The ARIMA equations are used to forecast the future price movement of the Health Care Index till December 2020. The findings reveal that the market will continue with the same volatility, and investors should give due attention to analysis and logical reasoning rather than following their feeling of overconfidence. 2024 Taylor & Francis Group, LLC. -
Smart Antenna for Home Automation Systems
A smart antenna for home automation systems is suggested in this design. An antenna is a device that minimizes human movements while dealing with electronics systems and software. This smart antenna helps reduce physically challenged peoples movement and supports home automation systems. The presented antennas could be used for any small device, like a mobile, tablet, laptop, Wi-Fi, or WiMAX, which are essential in the current scenario. The presented smart antenna is capable of radiating a large frequency band from 3 to 13.8 GHz, which covers the .5-7 GHz (5G(I) Sub-6 GHz band), Wimax 3.5 and 5.5 GHz bands, WLAN 5.2 and 5.8 GHz bands, C 4-8 GHz Band, X 8-12 GHz Band, and other home automation applications with high efficiency. The impedance bandwidth of the smart antenna is 128%, with a size of 15x15x1.5 mm3. The suggested design includes a modified patch in the shape of a square patch attached with one circular element fed by a microstrip line. Circular pieces have been designed for better resonances at lower modes. The antenna is simulated with an FR4 substrate using a CST Simulator. The design is investigated by simulations and corresponding S-parameter results are presented. The robotics process automation is well described in Table 7.3. The proposed structure also demonstrates stable radiation patterns across the operating bandwidth. The proposed radiator has a high gain of 5.21 dBi and an efficiency of 80%. 2023 Scrivener Publishing LLC. -
Reduce Overfitting and Improve Deep Learning Models Performance in Medical Image Classification
A significant role in clinical treatment and educational tasks is played by clinical image classification. However, the traditional approach has reached its peak in terms of implementation. Additionally, using traditional approaches requires a lot of time and effort to remove and choose arrangement features. The deep learning (DL) model is a new machine learning (ML) technique that has proven effective for various classification problems. To alter image classification problems, the convolutional neural network performs well, with the best results. This chapter discusses the importance and challenges of deep learning models in medical image classification and explains some techniques for reducing overfitting and leveraging model performance during model training. 2024 Taylor & Francis Group, LLC. -
Edge/Fog Computing: An Overview and Insight into Research Directions
The rapid proliferation of data from applications including IoT, and on-demand access to data have increased dependency on cloud computing, which helps to minimize the overhead related to data storage and maintenance. Applications such as IoT, industrial control, etc. generate data which are highly time-critical in most scenarios. The cloud platform offers permanent storage of this massive amount of data but with comparatively less focus on time-sensitivity. Edge/fog computing are extensions of the cloud computing paradigm and require less response time for time-sensitive data. The edge/fog brings processing and storage closer to the edge of the network, thereby reducing network traffic, delay, and latency. It acts as an intermediate layer between the end devices and the cloud platform, for data collection, offloading, processing, and data management. This chapter addresses the need for fog computing, presents the design model for edge/fog computing, and discusses applications and open issues of implementation. The three-layered network model, the services provided by the edge/fog computing, and a few research challenges of implementation will also be discussed. 2024 Taylor & Francis Group, LLC. -
Unraveling the Interplay Between Indian Agricultural Sector, Food Security, and Farms Bill: Key to Sustainable Development Goals
Agriculture, along with its allied sectors, plays a significant role in the economic progress and expansion of any country; despite tremendous economic progress, Indias agriculture sector is in jeopardy for various reasons. Agriculture in rural areas has been the primary source of income for the poor. With the growing susceptibility, policymakers main problems are to design ways to promote sustain-able agriculture to achieve the Sustainable Development Goals (SDGs). The Sustain-able Development Goals emphasize the relevance of agriculture and the need to revi-talize agribusiness worldwide by aiding farmers, increasing investments in research, technology, and market infrastructure, and increasing knowledge sharing. It may lead to spur innovation and give farmers more power. One of the essential advantages of urban agriculture is its potential to boost social capital and civic participation in low-income neighborhoods. As a result, the most critical goal in agricultural develop-ment for food security should be to raise productivity and diversify food production. Diversification of crops should be encouraged among farmers. This would aid in the fight against starvation, but it would also assist in preventing biodiversity loss and strengthen farmer resilience. Hence, our Chapter attempts to analyze the poor food security and what strategies will contribute to the SDG goals to reduce hunger in India as well worldwide. It elucidates a variety of obstacles and opportunities for successful, sustainable, and resilient agriculture. It also covers topics such as the recent agricultural bill and its long-term implications for our growth and a few important takeaways that could help us get closer to our objectives, mainly through the application of technology. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Anti-caste Movement and Rise of Dalit Womens Voices from South Asia
There have been cohesive attempts at forging alliance through the sustained efforts of emergent Dalit Civil society network, Dalit academicians and the renaissance of Ambedkarite thought among the Dalit youth around the question of political representation and social justice. This has led to a renewed and greater visibility of caste-based social relations and interactions in the present millennium, which was otherwise, treated as a long-forgotten age-old tradition. The lived experiences of exclusion and atrocities faced by members of the Dalit community especially the violence against women and girls reflect the grim reality of the prevalent casteist and patriarchal society. In this background, the emergence of Dalit Womens collectives raising their voices not just on caste but also on the intersectionality of gender provides a new dimension of analysis based on the critical race theory. Thereby, the attempt has been on forging an alliance and building collective voices. The chapter seeks to highlight the numerous struggles and triumphs along the way made by Dalit Women (also with building alliances with Black Womanists and Feminists Movement) in challenging the way in which feminists discourses have been held leading to rethinking and reimagining womens collectives by way of building solidarities, recognizing the difference of experience and positioning in caste and gender ladder that have influenced access to resources, rights, political representation and decision-making power from the local governance to national level. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix
Early detection of any sort of cancer, particularly lung cancer, which is one of the worlds most lethal illnesses, can save many lives. Life expectancy can be improved and the degree of mortality reduced by adopting the early forecast. While there are different methods like X-ray and CT scans to detect lung cancer cells, CT images resulted as more favored. The 2D images are used for more accurate medical results, such as CT scans. The proposed approach here will address how to interpret the CT images for the Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix. This research will explore how the image conversion can be achieved through different methods of image processing to obtain better results from CT images. The Confusion Matrix helps to estimate inequality in a picture pattern. After the evaluation of the processed images by Confusion Matrix, a final accuracy with a result of 93% is obtained. 2023 Scrivener Publishing LLC. -
Diabetic Retinopathy Detection Using Various Machine Learning Algorithms
The advances in technologies have paved the way to generate huge amounts of data in a variety of forms. Machine learning techniques, accompanied by Artificial Intelligence with its challenging nature help in extracting meaningful information from such data. This will have a great impact on many sectors, such as social media analytics, construction and healthcare, etc. Computer-aided clinical decision-making plays a vital role in todays medical field. Hence, a high degree of accuracy with which machine learning algorithms can detect diabetic retinopathy is really in demand. Convolutional neural networks, a deep learning technique, have been used to recognize pathological lesions from images. Image processing and analytics methods are used and have been trained to recognize the significant complications of diabetes, which cause damage to the retina, diabetic retinopathy (DR). Though this condition does not show any symptoms in its early stages, it has to be screened, diagnosed and treated at the earliest or it may lead to blindness. Deep neural networks have proved successful in screening DR from retinal images and handling the risks that may arise due to the disease. This chapter focuses on detecting diabetic retinopathy in retinal images by using efficient image processing and deep learning techniques. It also attempts to investigate the requirements of image pre-processing techniques for diabetic retinopathy. Experiments are carried out by taking a set of retinal images and predicting the level of diabetic retinopathy on a scale of 0 to 4. Deep learning techniques like CNN and DenseNet are applied and tested. 2024 Taylor & Francis Group, LLC. -
Data: An Anchor for Decision- Making to Build the Future Workforce Management System
In this digital era, the change in business environments and the nature of work lead to skill gaps. Training the workforce on desired skill sets must fill these skill gaps. Data play a crucial role in identifying the skills needed and helping organizations to plan the future workforce. Data is essential for any organizations growth and success in the dynamic market. Knowing the skill set in advance allows organizations and individuals to plan the business and skill requirements well. The way work is done may be impacted by these structural changes as the world is changing swiftly. Building the abilities necessary for the uncertain environments of the present and future environments is also crucial for training the employees. However, such skills must first be acknowledged and appreciated before being developed. Empirical data must support the methodology for valuing such abilities and skills. This chapter outlines the significance of data in skill identification for individuals to be future-ready. Finding the most relevant abilities in a given environment is the first step toward their formalization and acceptance at the systems level. It also presents the importance of creating skill matrices for students and organizations. The skill matrix objectively quantifies skill value for specific occupations and the possible trajectories to acquire those skill sets. This metric will allow policymakers to navigate this fast-changing workforce landscape and focus resources to ensure that skills are needed as students transition into the workforce and have skills that enable them to transition. 2024 selection and editorial matter, Alex Khang, Sita Rani, Rashmi Gujrati, Hayri Uygun, and Shashi Kant Gupta; individual chapters, the contributors. -
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. -
Leveraging Employee Data to Optimize Overall Performance: Using Workforce Analytics
Consistent employee performance is necessary for timely achievement and business success. Many key performance indicators influence an employees organizational performance, such as employee satisfaction, employee work environment, relationship with managers and coworkers, work-life balance, and many more. It becomes critical to regularly understand how these factors are connected to employee performance. One such method that is commonly used in companies is workforce analytics. It is a process that uses data-based intelligence for improving and enhancing management decisions in hiring and constructing compensations in alignment with employee performance. This also helps the management make data-based decisions and predictions, which helps in cost reductions and increases the overall profit. This chapter aims to analyze and report the workforce-related data and visualize the performance of 1,470 employees using published IBM human resources (HR) data made available at https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003357070/bb25a486-c036-4524-ab00-446f8eda3fd1/content/www.Kaggle.com xmlns:xlink=https://www.w3.org/1999/xlink>Kaggle.com. The chapter considers the following factors - job involvement, job satisfaction, performance rating, relationship satisfaction, environmental satisfaction, employee tenure, work-life balance, and income level - for data analysis and visualization of employee performance. The chapter aims to adopt descriptive, diagnostic, and predictive analysis using various software like Python, the Konstanz Information Miner (KNIME), and Orange. The visualization will be made using Tableau, Power BI, and Google Data Studio. Thus, the chapter gives a comprehensive insight into the meaning and importance of workforce analytics, different technologies used in workforce analytics, workforce analytics trends and tools, challenges of workforce analytics, and the process of implementation of workforce analytics. 2024 selection and editorial matter, Alex Khang, Sita Rani, Rashmi Gujrati, Hayri Uygun, and Shashi Kant Gupta; individual chapters, the contributors. -
Special Military Application Antenna for Robotics Process Automation
A special military application antenna for robotics process automation is presented in the following chapter. An antenna is a device that uses wireless communication. Wireless communications main advantage is protecting our soldiers from undefined enemies. To keep this thing in mind, we have designed a special military application antenna. The presented antenna is useful for defense and satellite communication, including wi-fi and Wimax, which is useful for the robotics automation process. Most of the military robotics automation is based on wireless communication. Our proposed antenna is very useful and capable of receiving or transmitting high signals in terms of GHz. The presented geometry can radiate the large frequency band from 2.9 to 11.6 GHz, which covers the 5G-(I) Sub- 6GHz band and X-Band Communication, with high efficiency. The impedance bandwidth of the radiator is 120%, with an electrical size of .14?x.14?x0.014? in lambda. The antenna is simulated with an FR4 substrate using a CST Simulator. Simulations also investigate the 08-stages evolution process and corresponding S-parameter results are presented. The proposed structure also demonstrates stable radiation patterns across the operating bandwidth. The proposed radiator has a high gain of 6.78 dBi and an efficiency of 89%. Therefore, it is useful for 5G-(I) Sub-6GHz band and X-band military applications, including satellite mobile, Radar, and Satellite microwave communication. 2023 Scrivener Publishing LLC.