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An internet of health things-driven deep learning framework for detection and classification of skin cancer using transfer learning
As specified by World Health Organization, the occurrence of skin cancer has been growing over the past decades. At present, 2 to 3 million nonmelanoma skin cancers and 132 000 melanoma skin cancers arise worldwide annually. The detection and classification of skin cancer in early stage of development allow patients to have proper diagnosis and treatment. The goal of this article is to present a novel deep learning internet of health and things (IoHT) driven framework for skin lesion classification in skin images using the concept of transfer learning. In proposed framework, automatic features are extracted from images using different pretrained architectures like VGG19, Inception V3, ResNet50, and SqueezeNet, which are fed into fully connected layer of convolutional neural network for classification of skin benign and malignant cells using dense and max pooling operation. In addition, the proposed system is fully integrated with an IoHT framework and can be used remotely to assist medical specialists in the diagnosis and treatment of skin cancer. It has been observed that performance metric evaluation of proposed framework outperformed other pretrained architectures in term of precision, recall, and accuracy in detection and classification of skin cancer from skin lesion images. 2020 John Wiley & Sons, Ltd. -
An internet of things based on covid spread prevention system and its method thereof /
Patent Number: 2021104215, Applicant: J. Martin Sahayaraj. -
An Interrogation and Analysis of Postmodern 'Self' in Robert Lowell's Sonnet Reading Myself
The interrogation and analysis of Self in Robert Lowell's Sonnet Reading Myself is the research statement. Jean Francois Lyotard proposed the idea of 'Delegitimation' of Grand Narratives in Modern Times (1). This concept of Delegitimation gives power to an individual to narrate her or his Self and gives complete control to have his power. The introspection of self in Robert Lowell's Sonnet is analysed critically in this postmodern sense. It aims at the liberation from the fixed system of beliefs or stereotypical norms of the society in writing a literary piece by analysing the lines of the sonnet in a postmodernist way. Specifically, the Sustainable Development Goal [SDG] of reducing inequality is examined through the poet's self in the paradoxical situation in a postmodern sense. It also questions the paradoxical existence and experiences faced by the poet in his life. The realisation of the self is significant in the present world gives the individual the freedom to create equal space for himself and others in society. The Electrochemical Society -
An Interrogation of Android Application-Based Privilege Escalation Attacks
Android is among the most widely used operating systems among consumers. The standard security model must address several dangers while still being usable by non-security users due to the wide range of use cases, including access to cameras and microphones and use cases for sharing information, entertainment, business, and health. The Android operating system has taken smartphone technology to peoples front doors. Thanks to recent technological developments, people from all walks of life can now access it. However, the popularity of the Android platform has exacerbated the growth of cybercrime via mobile devices. The open-source nature of its operating system has made it a target for hackers. This research paper examines the comparative study of the Android Security domain in-depth, classifying the attacks on the Android device. The study covers various threats and security measures linked to these kinds and thoroughly examines the fundamental problems in the Android security field. This work compares and contrasts several malware detection techniques regarding their methods and constraints. Researchers will utilize the information to comprehensively understand Android security from various perspectives, enabling them to develop a more complete, trustworthy, and beneficial response to Androids vulnerabilities. 2023 American Institute of Physics Inc.. All rights reserved. -
An Introduction to ?Agile for HR Through? the Development of ?an Agile Operating ?Mindset
An understanding of Agile principles and a readiness mindset for human resources professionals play a crucial role in determining the application of Agile for HR in an organisational context. With the rise in extended and non-linear workforce configurations and geo-neutral team arrangements, Agile organisations necessitate that the nature of the HR function evolve from working through traditional architectural models and quickly adopting Agile models of functional excellence. The dearth of literature on understanding and implementing Agile practices in the HR function within enterprises requires a clear examination of the advantages of going Agile for HR. This essay explores the intuitive concept of Agile HR and operating schema, which can develop as a starting point in examining an understanding of how Agile practices in HR can evolve for sustainable enterprises and some challenges that are encountered. The Author(s) 2024. -
An Introduction to Business Intelligence
The quality of managerial decisions impacts the performance of any business, and this decision mainly depends on the reliability, inclusiveness, correctness, and trustworthiness of the data used for this purpose. Nowadays, business intelligence (BI) has become a key buzzword. BI supports better business decision-making by transforming data into actionable insights. The digitalization or digitization of business is accommodating and embracing the new BI to endure and stand for consistency and competitiveness for business development toward technological or digital transformation. In this digital or computer era, only those businesses will be profitable and successful that are well furnished to digitally (or binary) shift their practices in the technological or information age. In the new technological age, high powered by data analytics capabilities, meaningful and systematic data assimilation has become a new challenge for an organization to transfer data into BI. BI is a technology-driven process for analyzing data into information; information into knowledge; and knowledge into plans that manage and regulate the organization. BI presents actionable information to help corporate executives; business managers, and other end-users and makes more informed business decisions. BI software systems provide historical, current, and predictive views of business operations. Dashboards; Forecasting; Graphical Reporting; Graphical Online Analytical Processing (OLAP); and Key Performance Indicators (KPIs) are the modules of BI. BI helps in organizing teams, keeping them mindful and aware of KPIs. The awareness of KPIs through dashboards and reports keeps teams aligned and more focused on their goals. The optimal aim of BI is to enable a business to make informed decisions. BI helps business managers or leaders utilize data in a way that is coherent and dynamic. The key elements of BI involved are Advanced Analytics or Corporate Performance Management; BI; Data Sources; Data Warehousing and OLAP. With the latest technology and innovations, there are countless BI applications available for varied types of data analysis. BI software or technologies can deal with multiple structured and unstructured data to identify, develop, and create new strategies business opportunities. Its purpose is to enable clear and accessible interpretation of the huge data, to identify new opportunities and execute effective strategies. Strategic BI (SBI) is always associated with reporting from an analytical data source or data warehouse. Essentially, SBI improves the business process by analyzing a predetermined set of data pertinent to that process and provides the historical background of that data. SBI assembles on four crucial and necessary criteria or frameworks, namely collection and storage of data; Optimization of data for analysis; Identification of important business drivers through past data records; and seeking answers to key business questions. Hence, BI provides procedures and technologies, and tools for current business leaders to alter and modify dynamically and effectively lead their companies with correct data decisions. This research paper is qualitative and based on secondary data. This chapter aims to provide insights into BI and highlights the recent innovations and future of BI. 2023 selection and editorial matter, Deepmala Singh, Anurag Singh, Amizan Omar & S.B Goyal. -
An introductory illustration of medical image analysis
The medical imaging field has evolved into an enormous scientific discipline since the last decade of the 19th century. The analysis of medical data obtained by current image modalities such as positron emission tomography, magnetic resonance imaging, computed tomography, and ultrasound comes to the aid of the fruitful diagnosis, appropriate planning, and assessment of therapy for patients treatment and much more. Medical image analysis is crucial to grip this huge amount of data and to investigate and present the appropriate information for any particular medical task. In this chapter, different aspects with regard to medical image analysis are exhaustively explored. In particular, issues and challenges in connection with this task are investigated and described. In addition, a brief summary of the contributory chapters is presented to trace the challenges and findings of each. 2020 Elsevier Inc. All rights reserved. -
An Intrusion Detection Model Based on Hybridization of S-ROA in Deep Learning Model for MANET
A kind of wireless network called a mobile ad hoc network (MANET) can transfer data without the aid of any infrastructure. Due to its short battery life, limited bandwidth, reliance on intermediaries or other nodes, distributed architecture, and self-organisation, the MANET node is vulnerable to many security-related attacks. The Internet of Things (IoT), a more modern networking pattern that can be seen as a superset of the paradigms discussed above, has recently come into existence. It is extremely difficult to secure these networks due to their scattered design and the few resources they have. A key function of intrusion detection systems (IDS) is the identification of hostile actions that impair network performance. It is extremely important that an IDS be able to adapt to such difficulties. As a result, the research creates a deep learning-based feature extraction to increase the machine learning technique's classification accuracy. The suggested model uses outstanding network-constructed feature extraction (RNBFE), which pulls structures from a deep residual network's many convolutional layers. Additionally, RNBFE's numerous parameters cause a lot of configuration issues because they require manual parameter adjustment. Therefore, the integration of the Rider Optimization Algorithm (ROA) and the Spotted Hyena Optimizer (SHO) to frame the new algorithm, Spotted Hyena-based Rider Optimization (S-ROA), is used to adjust the RNBFEs settings. Attack classification is performed on the resulting feature vectors using fuzzy neural classifiers (FNC). The experimental analysis uses two datasets that are publicly accessible. The Author(s), under exclusive licence to Shiraz University 2024. -
An Inventory Model for Growing Items with Deterioration and Trade Credit
Growing items industry plays a vital role in the economy of most of the countries. Growing item industries consists of live stocks like sheep, fishes, pigs, chickens etc. In this paper, we developed a mathematical model for growing items by considering various operational constraints. The aim of the present model is to optimize the net profit by optimizing decision variables like time after growing period and shortages. Also, the delay in payment policy has been used to maximize the profit. A numerical example is provided in support of the solution procedure. Sensitivity analysis provides some important insights. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An investigation and analysis on automatic speech recognition systems
A crucial part of a Speech Recognition System (SRS) is working on its most fundamental modules with the latest technology. While the fundamentals provide basic insights into the system, the recent technologies used on it would provide more ways of exploring and exploiting the fundamentals to upgrade the system itself. These upgrades end up in finding more specific ways to enhance the scope of SRS. Algorithms like the Hidden Markov Model (HMM), Artificial Neural Network (ANN), the hybrid versions of HMM and ANN, Recurrent Neural Networks (RNN), and many similar are used in accomplishing high performance in SRS systems. Considering the domain of application of SRS, the algorithm selection criteria play a critical role in enhancing the performance of SRS. The algorithm chosen for SRS should finally work in hand with the language model conformed to the natural language constraints. Each language model follows a variety of methods according to the application domain. Hybrid constraints are considered in the case of geography-specific dialects. 2024 by author(s). -
An investigation into the causes of non compliance with labour laws by zimbabwean local authorities
The current research; which I carried out in Mashonaland East Province of Zimbabwe between 2012 and 2014 was prompted by the surge newlinein labour related disputes in sub-national governments in Zimbabwe as well as the evident poor levels of compliance to labour laws by local authorities which also happen to be a poorly rated sector of the economy in terms of service delivery. In carrying out the study, I was guided by the following newlineresearch objectives: to identify the challenges being faced by Zimbabwean newlinelocal authorities in complying with the labour laws, to establish the extent to which non compliance affects labour relations in local government in Zimbabwe, to ascertain the impact of non-compliance on service delivery and finally to assess the government monitoring aspect. The targeted population for the study comprised all the ten local authorities and ministry of local government employees from the province. The study mainly newlineemployed the exploratory research design and I found that non-compliance with labour laws by Zimbabwean local authorities was not only as a result of the quality of labour relations and management systems (internal controls and corruption) but also lack of governmental financial support, political newlineinterference, skills migration (brain drain), increased poverty (economic meltdown) and high unemployment rate. In light of the research findings, I recommend that the government formulates deliberate policies to re-engage the international community as this will help attract foreign direct investment; thereby reducing poverty, unemployment, skills migration and corruption. I also recommend an increase in the financial support by government to its sub national governments. There should also be total newlinedecentralization of all sub national governments to ensure efficiency and newlinenon-interference with local authorities operations. -
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An Investigation into the Role of AI-Based Innovation in Supporting the Next Generation of Startup Entrepreneurs
The advent of Artificial Intelligence (AI) has revolutionized various industries, offering unprecedented opportunities for innovation and entrepreneurship. This investigation delves into the pivotal role of AI-based innovation in nurturing and empowering the next generation of startup entrepreneurs.AI technologies, including machine learning, natural language processing, and computer vision, have significantly augmented the capabilities of startups across diverse sectors. This study aims to elucidate the multifaceted ways in which AI fosters entrepreneurial endeavors, from ideation to market penetration.AI algorithms enable startups to analyze vast datasets swiftly, extracting valuable insights that inform strategic decision-making and product development. Through predictive analytics and trend forecasting, entrepreneurs can anticipate market demands, optimize resource allocation, and mitigate risks, thereby enhancing the viability and competitiveness of their ventures.AI facilitates personalized customer experiences, driving customer engagement and retention for startups. By leveraging AI algorithms to analyze user behavior and preferences, entrepreneurs can deliver tailored products, services, and marketing campaigns, fostering brand loyalty and customer satisfaction.The integration of AI into startup ecosystems also presents various challenges, including ethical considerations, data privacy concerns, and regulatory complexities. Therefore, this investigation also explores the ethical implications and regulatory frameworks surrounding AI-based entrepreneurship, advocating for responsible innovation practices and stakeholder collaboration. 2024, Collegium Basilea. All rights reserved. -
An Investigation of Complex Interactions Between Genetically Determined Protein Expression and the Metabolic Phenotype of Human Islet Cells Using Deep Learning
The relationship between gene modules and several genome-scale metrics was examined, including heterozygosity that caused type 2 diabetes due to insulin deuteration, differential expression, genotyping association, methylation, and copy number changes. This work investigates the complex relationships between protein expression, genetic polymorphisms, and metabolic properties of human islet cells using expression quantitative trait loci (eQTL) detection. We looked at the genomic, transcriptomic, and proteomic information from islet cells in persons with type 2 diabetes. From the information from different levels, we noticed novel eQTLs that regulate crucial metabolic and signaling pathways in islet cells. Our study highlights the importance of a systems-level approach in understanding the complicated biological processes by highlighting the complexity of the link between genetic variants, protein expression, and metabolic abnormalities using the PIMA Indian dataset. Our findings provide novel insights into the molecular mechanisms behind islet cell failure in type 2 diabetes, potential targets for emerging treatment strategies, and the genomic implications of variations in gene expression, mutations, and other factors. To accomplish this purpose, we proposed a novel BLB model and obtained 99.89%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
An Investigation of Multifractality and Herd Behaviour in Indian Capital Market During Macro-Political Events : An Empirical Evidence Through Econophysics Approach
The financial markets worldwide exhibit several complex and dynamic features in them. Among them, Multifractality is one of the most significant features of complex systems, and it has been identified and examined in the financial markets in recent years. Besides, studies in the past confirm that there exists a linkage between multifractality and herding behaviour in financial markets during extreme events. The current study attempts to investigate the presence of Multifractality caused by herding behaviour in the segments of the Indian capital market during the macro-political events. For this, the macro- political events were classified into three broad categories pre-scheduled events, intensified geopolitical events and uncertain macro-political events. Further, two major segments of the Indian capital market, namely, the equity and the Forex segment, were examined. The study employed the Multifractal Detrended Fluctuation Analysis approach to examine the Multifractality caused by herding behaviour during macro-political events. In addition, the study also measured the volatility surface and quantified the information uncertainty present in the selected segments of the Indian capital market. The findings suggest that the macro-political events impact the multifractality and herding behaviour in the examined segments of the Indian capital market. However, the degree of the multifractality caused by the herding behaviour traced in the market segments is event-specific. It differs based on the type of macro-political event. The overall analysis suggests that the pre-scheduled macro-political event's impact was higher for both equity and forex segments of the Indian capital market. Further, a high degree of multifractality caused by herding behaviour was traced in the Nifty segments during the intensified geopolitical events. On the other hand, uncertain macro-political events had no impact on the multifractality caused by the herding behaviour in equity and forex segments. The study results provide some significant implications for various market participants for investment decision-making and portfolio risk diversification during the macro-political events in India. -
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An investigation of the business level strategies in Zimbabwe food manufacturing sector (2006 -2013) /
International Journal Of Science And Research, Vol.3, Issue 6, pp.1052-1063, ISSN No: 2319-7064. -
An Investigation of the Effects of Chronic Stress on Attention in Parents of Children with Neurodevelopmental Disorders
Prolonged exposure to stress can cause impairments in various brain functions including cognition. Attention is one such important cognitive function that is required for our daily life and work-related activities. Chronic stress can have an impact on attention networks such as alerting, executive control, and orienting. The effects of naturalistic, persistent psychosocial stress on several attention networks were explored in this study. Parents of children with neurodevelopmental disorders (NDD) and parents of children with typical development (TD) were given an attention network test (ANT). Overall the stressed group (M= 564.623, SD= 75.484) was found to have a quicker reaction time in all the target and cue conditions whencompared to the non-stressed group (M= 588.874, SD= 101.575). Both groups had similar accuracy in all the conditions. When comparing the three attention network scores, no significantdifference was found in either group. However, in the stressed group, there was a significant beneficial relationship between the alerting and orienting networks (p=.006) and a high negative correlation between the alerting and executive control networks (p=.028). No significant correlation was found between the attention networks in the non-stressed group. Copyright2024 by authors, all rights reserved. -
An investigation of various algorithms suitable for 3D compute on heterogeneous platform
This research explores the usage of heterogeneous computing, which is the current trend in the computing industry, for the 3D compute based on the collaborative algorithm. As a part of literature review the areas related to the heterogeneous computing is investigated, which includes OpenCL, General Purpose Graphical Processing Unit [ GPGPU ], collaborative algorithm. The novelty of this research work is the application of collaborative algorithms, which have been used in other areas to 3D compute. This exploration of 3D compute algorithm was part of the exercise in order to find the suitable 3D compute algorithm for the monocular image to binocular image conversation engine. Here the two algorithms, i.e., intelligent water drops and zombie crowd algorithm were implemented using OpenCL to run on heterogeneous platform. The performance of both the algorithm was measured on the experimental setup rig. Based on the above research exercise, it can be inferred that, the collaborative algorithm can indeed be suitable for 3D compute and it can be also noted that each algorithm is suitable for particular 3D compute. KEYWORDS: Heterogeneous Computing, GPGPU, OpenCL, Collaborative Algorithm -
An Investigation on Machine Learning Models in Classification and Identifications Cervical Cancer Using MRI Scan
This study analyzes the effectiveness of machine learning models in the classification of cervical cancer using a dataset of 900 cancer and 200 non-cancer images gathered from online resources and hospitals. The dataset, covering both CT and MRI images, undergoes rigorous preprocessing, including standardization, normalization, and noise reduction, to enhance its quality for model training. Four machine learning models, namely VGG16, CNN, KNN, and RNN, are recruited to predict cancer and non-cancer cases. During the testing phase, VGG16 emerges as the most accurate, achieving an impressive accuracy of 95.44%, followed by CNN at 92.3%, KNN at 89.99%, and RNN at 86.233%. Performance parameters, such as precision, recall, F1 score, and accuracy, are fully analyzed, providing insights into each model's strengths and capabilities. These discoveries not only contribute to the advancement of cervical cancer diagnostic techniques but also underscore the potential of machine learning in medical imaging. The study emphasizes the relevance of model selection and provides a framework for future research endeavors seeking to enhance the accuracy and performance of cervical cancer diagnosis through the merger of advanced computational techniques with standard diagnostic practices. 2024 IEEE.