International Research and Academic scholar society

IRASS Journal of Multidisciplinary Studies

Issue-9(September), Volume-3 2026

1. Integrating Artificial Intelligence and IoT for Precision Farming: A R...
141

G. Swetha*1, Prof. T. Anuradha2
1*-2Department of Computer Science & Technology, Dravidan University, Kuppam
1-13
http://doi.org/10.67564/IRASSJMS.v3.i9.0186

Agriculture 4.0 marks a decisive shift from mechanized and chemically intensive farming toward a data-driven, automated, and connected model of food production. At the center of this shift lies the convergence of artificial intelligence (AI) and the Internet of Things (IoT), which together allow farms to sense, interpret, and act on field conditions with a precision that manual methods cannot match. This review synthesizes the current body of literature on AI-IoT integration in precision farming, covering the core enabling technologies — smart sensors, unmanned aerial vehicles (UAVs), geographic information systems (GIS), edge and cloud computing, and block chain-based traceability — and the layered architecture through which they combine into functioning smart-farming systems. Drawing on peer-reviewed studies published largely between 2019 and 2025, the review examines representative applications in smart irrigation, crop-disease detection, yield prediction, and supply-chain traceability, consolidates the recurring barriers reported across this literature, and provides a detailed, yearordered comparison of technique, dataset, performance metrics, and reported limitations across ten representative studies. The review concludes by outlining research gaps — particularly around affordable edge-AI models, interoperable data standards, and region-specific validation in smallholder contexts such as India — that merit attention in future work.

2. Bidirectional Film Title Translation through Prospective Compression a...
355

Yu Qi*
Institute of Foreign Languages, University of Shanghai for Science and Technology, Shanghai, China
14-18
http://doi.org/10.67564/IRASSJMS.v3.i9.0187

This study examines bidirectional film title translation through the perspectives of prospective compression and retrospective expansion, drawing on Wang Jianguo’s (2025) contrastive account of Chinese and English, particularly the differences between process orientation and result orientation and between weak and strong semantic boundaries. Based on a qualitative comparative analysis of 16 Chinese and English film titles, the study identifies four major application paths for each direction of translation. In Chinese-to-English translation, prospective compression is manifested through process-to-result, concrete-to-abstract, event-toobject, and positive-comparison-to-comparative transformations. These operations condense processual meaning, strengthen semantic boundaries, and produce concise and target-oriented English titles. In English-to-Chinese translation, retrospective expansion operates through resultto-process, abstract-to-concrete, object-to-event, and comparative-to-positive transformations. These operations recover implicit processes, concrete imagery, and narrative information, thereby increasing the accessibility and expressive appeal of Chinese titles. The analysis shows that the two strategies are particularly compatible with the functional demands of film titles, including brevity, thematic communication, genre identification, and cross-cultural accessibility. At the same time, the study argues that neither strategy should be applied mechanically: compression should not result in excessive semantic loss, while expansion should not introduce meanings unsupported by the source title. The findings provide a practical framework for bidirectional film title translation and offer a case-based application of contrastive ChineseEnglish analysis to audiovisual translation.

3. Artificial Neural Network Modelling for Production of Biodiesel from W...
189

P. Kanakasabai1, Saikat Banerjee2, D. Sridevi3, S. Sivamani*4
1-2-4*College of Engineering and Technology, Engineering Department, University of Technology and Applied Sciences, Salalah, Oman, 3IT Department, SRM Valliammai Engineering College Tamil Nadu, India
19-25
http://doi.org/10.67564/IRASSJMS.v3.i9.0188

Artificial neural networks (ANN) are bioinspired algorithms used in various engineering applications. The objective of this present study is to create a model algorithm for biodiesel synthesis from the collected waste cooking oil utilizing artificial neural networks (ANN). The factors consider to be influencing the biodiesel production are concentrations of solutions, time and temperature, pH of the solution, and agitation speed. In previous trials, methanol to oil ratio, sodium hydroxide to oil ratio, in addition to reaction temperature were taken as in terms of independent type of variables and % biodiesel yield inulin as a dependent variable. The results reveal that the 3-10-1 architecture of ANN provides goodness of fit to predict the percentage yield of biodiesel. The prediction ability of ANN is assessed by the coefficient of determination (R^2). The resultant R^2 value shows that the ANN predicted values fitted well to the percentage yield of biodiesel.





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