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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. -
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 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 Intuitionistic Fuzzy-Rough Attribute Selection Using Representative Samples
Selecting relevant features is an important tool for extracting knowledge from datasets with many attributes and objects. The traditional theory of rough set is a fundamental and successful tool for dealing with vagueness and inconsistency. Combining the rough set with the fuzzy set handles the information loss problem arising from the discretisation process. Still, it fails to consider the hesitancy part of any information system. A generalisation of fuzzy set known as an intuitionistic fuzzy (IF) set has more real-world applications to confront uncertainty and ambiguity than the fuzzy set. So, the combination of rough set and IF set not only deals with vagueness but also able to consider the hesitancy available in any real-world data. In this work, we propose an IF rough set model based on representative samples and its application in the area of attribute reduction of high-dimensional datasets. First, we defined the representative sample-based intuitionistic fuzzy rough set and then presented an algorithm to calculate the reduction of a dataset using the degree of dependency method. Mathematical theorems are applied to validate the presented model theoretically. Experimental analysis is also discussed to validate the proposed technique. Finally, we applied our proposed method to improve the prediction of antifungal peptides. 2025 Old City Publishing, Inc. -
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 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 introduction to multimodal data representation
The contemporary digital epoch is characterized by a radical transformation of data representation methodologies that imply increased intricacy as well as an enlarged bulk of data. An unimodal approach focusing on judicious data types, considered in isolation, was the earlier norm. The emphasis was on structured data, which had the advantage of being arranged systematically within relational databases and entity-relationship frameworks. This facilitated efficient data management. With the introduction of the internet and digital communication, such unstructured data as textual content, images, and audio began to be placed up front. But unimodal techniques were not adequately equipped to manage the intricate and interconnected nature of real-world phenomena. The welcome result was the development of multimodal data representation methodologies, which constitute a sophisticated paradigm that integrates data from such varied sources as text, images, audio, video, and sensor data. This results in a more holistic comprehension of complex scenarios. Distinct attributes and inherent challenges characterize each modality. To exemplify, text data need advanced natural language processing strategies to comprehend context and semantics; Image data necessitate methodologies well versed in managing spatial features and elevated dimensionality; audio data requires concentration on temporal patterns and noise; video data, on the contrary, integrates these complexities, leading to efficient processing techniques to accommodate its substantial volume and dynamic characteristics. The unsynchronous and heterogeneous sensor data complicate the integration of diverse data streams. Sophisticated fusion techniques, that is, early fusion, late fusion, and hybrid fusion, capable of integrating features from various modalities, are employed to mitigate the challenges faced by multimodal data representation. It increases interpretative insights and precision. The deep learning technologies, such as convolutional neural networks for image analysis, recurrent neural networks for sequential data processing, and attention mechanisms, have led to advancements in this domain. These models have become competent in recognizing complex patterns across modalities. Naturally, they bring about significant progress in domains such as health care, autonomous systems, multimedia processing, and natural language comprehension. This chapter explores the historical background of data representation, right from the beginnings in unimodal to its advancement in multimodal. The unique characteristics and challenges associated with each modality are scrutinized; Fusion techniques alongside contemporary deep learning models are examined; and underscore real-world applications, which are effective examples of the transformative potential of multimodal data representation. The chapter also emphasizes the necessity of escalating these methodologies in an increasingly data-centric world. It lays the foundation for advancements in the future with the goal of overcoming existing limitations and enlarging the scope of multimodal applications. 2026 Elsevier Inc. All rights reserved. -
An introduction to design-based research
Design-Based Research (DBR) is an innovative methodology aimed at bridging the gap between theory and practice in educational settings. It emphasizes the iterative design and testing of interventions or educational innovations in real-world contexts, allowing researchers to collaboratively address complex problems in teaching and learning. Unlike traditional research methods that often focus solely on experimental or statistical analyses, DBR fosters a collaborative environment where educators, researchers, and stakeholders jointly engage in the design process. One of the core principles of DBR is its focus on understanding and enhancing learning processes, rather than merely measuring outcomes. By prioritizing collaboration and context, DBR offers valuable insights that can lead to scalable and impactful educational practices. Ultimately,Design-Based Research serves as a powerful tool for educators and researchers looking to innovate within the complex landscape of education, fostering a deeper understanding of how design can enhance learning experiences. 2025 by IGI Global Scientific Publishing. -
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 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 ?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 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 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 Interpretable Federated Multi-Task Learning Framework for Smart Traffic Management with Hessian-Driven Optimization Insights
Smart traffic management faces challenges in balancing privacy, interpretability, and optimization robustness, particularly when using deep learning for vehicle detection and traffic prediction. Existing methods struggle to provide transparent feature attribution while preserving data confidentiality in decentralized settings. This study proposes a federated multi-task learning (FMTL) framework based on YOLOv10, trained on an original traffic dataset, to address these limitations. The framework simultaneously performs vehicle detection, traffic density analysis, and no-entry sign identification, while employing Grad-CAM to enhance interpretability and Hessian-based eigenvalue analysis to evaluate optimization complexity. Results demonstrate an average mean accuracy of 89.7% across three real-world locations, with Grad-CAM revealing meaningful focus on vehicle density and intersection geometry. Hessian analysis confirms the presence of mixed-sign eigenvalues, proving the non-convexity of the optimization surface and highlighting convergence challenges. These outcomes establish a privacypreserving, interpretable, and optimization-aware framework for real-world smart traffic management. 2025 IEEE. -
An internet of things based on covid spread prevention system and its method thereof /
Patent Number: 2021104215, Applicant: J. Martin Sahayaraj. -
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 intelligent web caching system for improving the performance of a web-based information retrieval system
With an increasing number of web users, the data traffic generated by these users generates tremendous network traffic which takes a long time to connect with the web server. The main reason is, the distance between the client making requests and the servers responding to those requests. The use of the CDN (content delivery network) is one of the strategies for minimizing latency. But, it incurs additional cost. Alternatively, web caching and preloading are the most viable approaches to this issue. It is therefore decided to introduce a novel web caching strategy called optimized popularity-aware modified least frequently used (PMLFU) policy for information retrieval based on users' past access history and their trends analysis. It helps to enhance the proxy-driven web caching system by analyzing user access requests and caching the most popular web pages driven on their preferences. Experimental results show that the proposed systems can significantly reduce the user delay in accessing the web page. The performance of the proposed system is measured using IRCACHE data sets in real time. Copyright 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. -
An Intelligent System to Forecast COVID-19 Pandemic using Hybrid Neural Network
A current outbreak known as COVID-19 has been discovered from the coronavirus was informed by WHO. COVID-19 is a universal pandemic that has brought out the best and the worst of humanity. Due to an increase in the cases daily, COVID-19 is creating a menace to public health and establishes a disruption of the social and economic development of the countries. The problem is the hospitals are not able to provide proper facilities and treatments on time due to the lack of facilities in India. The purpose of this project to build an efficient hybrid deep learning model for forecasting the COVID-19 pandemic with multiple features that are responsible for the spread of COVID-19 in the top five states in India. In particular, a hybrid model that incorporates Auto-Regressive Integrated Moving Average and Long-term Short Memory is been used to forecast confirmed cases. The linear and non-linear dependencies in the dataset is been dealt with by an ARIMA-LSTM hybrid model. As a result, when compared to the outcomes of ARIMA, LSTM models independently, the hybrid model was giving better results and was performing well in forecasting COVID-19 cases. Through this, the policymakers will get prior information on COVID-19 cases in states which will help the government and healthcare departments to take prominent measures to prevent it. 2021 IEEE. -
An Intelligent Stock Market Automation with Conversational Web Based Build Operate Transfer (BOT)
Zerodha, Upstox, Angel Broking, Groww, etc. Such companies have the most significant users of traders/investors in the equity share market. Their trust is based on their ease of use, less time-consuming process, and accurate graphs and charts of real-Time data. But what if such companies had an algorithm that could predict the future prices of any share? Not just based on historical data but also on sentimental data? This project aims to build a speech recognition chatbot like Alexa Google, which will use Recurring Neural Network-Long Short-Term Memory (RNNLSTM) and Natural Language Processing (NLP) to predict future intra-day prices. 2022 IEEE. -
An intelligent secure and efficient workflow scheduling (SEWS) model for heterogeneous cloud computing environment
This study recognizes the critical role of the cloud computing platform in scientific workflow applications yet identifies vulnerabilities in existing cloud workflow systems, such as information leaks, unauthorized access, and compromised data integrity during task scheduling. Mainly, attackers exploit the lack of security for intermediate-level task information. To address these security threats, this work introduces the secure and efficient workflow scheduling (SEWS) model for heterogeneous cloud computing environments. The SEWS model identifies malicious attacks on all workflow tasks and focuses explicitly on safeguarding intermediary data. The SEWS model employs intelligent techniques to enhance security and introduces a comprehensive metric to measure the security of workflow tasks, considering factors like integrity, confidentiality, and availability. Beyond security improvements, the SEWS model aims to elevate the overall quality of service (QoS) in workflow scheduling applications. This includes reducing simulation time, enhancing overall power efficiency, and minimizing average energy consumption. Results: Results from the SEWS model demonstrate substantial improvements over the energy-minimized scheduling (EMS) model, with a reduction of 79.41% in average simulation time, 87.92% in average power sum, 41.35% in average power average, and 89.62% in average energy consumption. These findings underscore the SEWS models effectiveness in providing enhanced security and improved QoS in cloud workflow scheduling. The overarching goal of this work is to contribute to developing a more secure and efficient cloud workflow scheduling system, aligning with the increasing demands for robust security measures and optimized performance in heterogeneous cloud environments. Findings: Compared to the energy-minimized scheduling (EMS) model, the findings of this study demonstrate that the secure and efficient workflow scheduling (SEWS) model yields superior outcomes across key performance metrics. Specifically, the SEWS model excels in average simulation time, power sum, power average, and energy consumption. These results underscore the effectiveness of the SEWS model in enhancing the efficiency and resource utilization of cloud workflow scheduling. Importantly, the study identifies a notable gap in the existing work related to workflow task scheduling. Many prior studies still need to address the critical aspects of security and QoS in this context. While some jobs have attempted to enhance security, a significant limitation is the failure to extend these security measures to intermediary data. This gap in the literature highlights the unique contribution of the SEWS model, which addresses security concerns comprehensively and prioritizes QoS in the workflow task scheduling process. The observed superiority of the SEWS model in comparison with the EMS model serves as a testament to the models efficacy in concurrently addressing security and QoS challenges. By focusing on intermediary data, the SEWS model presents a holistic solution that aligns with the increasing demand for comprehensive security measures in cloud workflow environments. The findings emphasize the significance of integrating security and QoS considerations to establish a more robust and efficient workflow scheduling framework in heterogeneous cloud computing environments. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.