Ci-STEM Journal of
Digital Technologies and Expert Systems

By connecting things, the Internet of Things (IoT) is part of the next stage of the interconnected world, where different devices, platforms, and infrastructures can exchange data. With its diverse range of use cases, including but not limited to smart homes, industrial automation, healthcare, and environmental monitoring, IoT has emerged as a crucial element of contemporary technology landscapes. Thus, it first identifies the several significant aspects of IoT, the protocols of communication, and platforms, and finally assesses the applicability of IoT to numerous fields. But widespread adoption is facing several critical challenges, such as security issues, privacy concerns, interoperability issues, and scalability challenges. Advanced encryption, haltered frameworks and adaptive organization architecture can tackle these issues. In addition, emerging technologies like edge computing, AI-powered analytics and next-generation connectivity (e.g. 5G) are being utilized to reshape the capabilities of IoT. This paper summarizes the latest advancement achieved on IoT during the recent year and presents the empirical evidence on emerging trends, existing problems, and possible future work of IoT evolution.
Key Words: AI-driven analytics, Data exchange, Edge computing, Industrial automation, Interconnected world, Internet of Things (IoT), Smart homes, Security vulnerabilities.
Citation: Balaiah M, “A Brief Study on Evolution of IoT Protocols and Applications", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(2), pp. 57-65, 2024.

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The primary colour components (Green, Red, and Blue) are captured through single-sensor digital colour cameras mounted with a colour filter array (CFA) at all pixel locations through CFA. The limitations of the sensors used in the commercial digital cameras make the camera to capture only a sub-sampled image where each pixel contains only partial colour information. Consequently, the estimation of missing colour samples is Critical to Reconstruct the full colour picture. The process of Calculating the missing colour samples is known as demosaicking. The demosaicking is a critical step, in producing a high-quality colour image. Among Numerous demosaicking methods, directional filtering-based methods found to be most efficient. These techniques utilize the inherent directional colour gradients in the image to guide the interpolation process, thus preserving edges and reducing artifacts. In this research work, a simple yet effective approach is proposed to demosaicking that leverages the principles of directional filtering. By optimizing the balance between simplicity and efficiency, our proposed method provides high-quality colour image reconstruction making it appropriate for applications and devices with less power at their disposal for processing.
Key Words: Colour filter array, Colour gradients, Demosaicking, Directional filtering, Estimation, Interpolation, Primary Colour Components, Processing power, Single Sensor Digital Colour Cameras, Sub-sampled.
Citation: Roop K V “A novel Colour Gradients Based CFA Interpolation Algorithm", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(2), pp. 66-77, 2024.

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Accurate demand forecasting in the food industry is critical to optimizing supply chain operations, minimizing waste, and maximizing product availability. Traditional forecasting techniques are often incapable of capturing the complexities of consumer behaviour as well as external influences such as business tariffs, weather conditions or shifts in the economy. These hurdles lead to inefficiencies, excess inventory, or stockouts. This study focuses the application of Machine Learning (ML) techniques to improve the forecasting accuracy of food demand. Using more transformational algorithms obtained at a deeper level, we hope to make predictions more accurately and control the dynamic nature of food demand in a better way. These fluctuations may make traditional methods often inaccurate, based on seasonality, promotions, or shifting consumer preferences. However, machine learning can better handle these changing variables. In this paper, we introduce a comparative study of multiple ML methods, including time series models like ARIMA and Prophet, and regression models such as decision trees and neural networks. These models are used on past sales data coupled with explanatory variables such as the weather forecast and promotional information. Through the application of machine learning, our goal is to offer more accurate, adaptable forecasting solutions, thus enabling improved inventory control, minimized waste, and a streamlined supply chain. Using this method can greatly enhance the precision of demand prediction, which ultimately can be quite useful to the running of food- industry.
Key Words: ARIMA, Consumer Behaviour, Demand Forecasting, Food Industry, Inventory Management, Machine Learning, Neural Networks Seasonality, Supply Chain, Time Series Models.
Citation: Sri Nagesh A et. al., “Predictive Modelling of Food Demand: Harnessing Machine Learning for Analysis and Insights", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(2), pp. 78-85, 2024.

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ML stands for machine learning, a groundbreaking technological paradigm that has transformed the way we approach diverse areas of society from clinical diagnostics to algorithmic trading, from personalized media delivery to self-driving cars. In this article, we will look into the history of ML algorithms — in the order of their chronology. This can aid in providing an extensive perspective of how various ML techniques evolved, the theoretical underpinnings that strengthen them and the potential of forthcoming research in this branch of computational intelligence. Tracing this history helps us to appreciate how the interplay between theoretical innovations, the expanding availability of computational resources, and the demands of complex real-world problems have shaped the field of ML. From a few decades ago to today, this evolution tells the story of how ML has become such an influential driver of much of the technology that we see today. Lastly, the article discusses the interaction among improvements in algorithm design, computational infrastructure, and challenges brought by data tasks, paving the way for more advances in the years to come.
Key Words: Computational intelligence, Historical development, Machine Learning (ML) Algorithms, Real-world applications, Research directions, Theoretical foundations.
Citation: S. Ghosh., “Study of Machine Learning Algorithms: A Historical Perspective", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(2), pp. 86-97, 2024.

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Demosaicking is the procedure of re-constructing full colour information at each pixel from a CCD sensor, which captures only one colour component red or green or blue per pixel. Various demosaicking techniques are employed to perform this reconstruction. However, many studies overlook a detailed analysis of image quality, particularly with regard to artifacts that may appear in the edges and textures of the images. These drawbacks in the existing methods, the re-constructed image seems to be of poor in quality with less PSNR values. To address the limitations of existing methods, this paper proposes a new demosaicking technique. The technique proposed is designed with two major stages (i) CFA demosaicking (ii) Enhancement by Levenberg-Marquardt technique. The given input CFA image green, red, blue pixel values are interpolated to extract the full-colour image. This full-colour image seems to be less quality and has more distortion in the edge and texture of the image. So the output full colour image is enhanced by the Levenberg-Marquardt technique. Hence, the image can be demosaicked more effectively by reaching higher PSNR and MSE ratio compared to the conventional demosaicking algorithms. The comparison of results shows that the proposed technique extracts high-quality demosaicked images than the state-of-the-art methods, in terms of PSNR.
Key Words: Artifact, CCD Sensor, Demosaicking, edge, enhancement, texture, Interpolation, Levenberg Marquardt, Colour Filter Array (CFA), Peak signal-to-noise (PSNR), Pixel.
Citation: Rao.C.R. et.al., “Demosaicking of Colour Filter Array (CFA) Data via Levenberg-Marquardt Optimization Method", Ci-STEM Journal of Digital Technologies and Expert Systems., Vol. 1(2), pp. 98-110, 2024.

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