Volume 13 | Issue 4 |

 Abstract

The increasing reliance on solar energy necessitates accurate and efficient forecasting techniques to mitigate its inherent variability and improve integration into power systems. This review explores recent advancements in solar power forecasting using Machine Learning (ML) and Deep Learning (DL) approaches. It outlines the significance of forecasting, examines a variety of ML and DL models including Support Vector Machines, Random Forests, LSTM, CNN, and hybrid architectures, and discusses their effectiveness in handling meteorological and irradiance data. The report also highlights key metrics for model evaluation and provides comparative insights.

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Solar Power Forecasting Using Machine Learning And Deep Learning: A Review

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 Abstract

The Coimbatore consumer perception toward organic products with special reference to awareness, preference, and determinants form the ambience for the study. This research found that most of the respondents, especially youngsters and students, had knowledge of organic products and purchased organic products mainly because of health and environment conservation. Fruits and vegetables were the most popularly purchased items. However, hindrances in the form of a high price, scarcity of supply, and lack of confidence in certification anywhere cripple the acceptance of organic. Though a few constraints exist, consumers are generally favourable toward organic products. Development of affordability, improved distribution, and further public awareness will greatly enhance demand for organic products and support sustainable consumer behaviour.

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 Keywords

Organic product, Customer perception, Health benefits, Coimbatore city.

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 Abstract

Thermoplastic elastomers are materials showing the properties of thermoplastics at processing temperatures and those of elastomers at service temperatures. At low temperatures, the thermoplastic phase acts as crosslinks or reinforcing filler particles in between the soft rubbery phase. Polyethylene- natural rubber blends have promising properties like thermoplastic behavior. Latex product waste when replaces natural rubber in such blends, proves to be a potential substitute for producing several end products using thermoplastic elastomers. Also, recycling and reusing waste rubber is the need of the hour, which is being realized in this process.

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 Keywords

Thermoplastic elastomer, Polyethylene- natural rubber blends, Latex product waste, dynamic vulcanization, mechanical and rheological studies.

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 Abstract

Heart disease, also known as cardiovascular disease (CVD), encompasses a variety of conditions affecting the heart and blood vessels. According to the World Health Organization (WHO), CVDs are the leading cause of death globally, claiming approximately 17.9 million lives each year. Early detection and diagnosis are vital for preventing complications and improving patient outcomes. Traditional methods, including ECG interpretation, echocardiography, and angiography, often require specialized expertise and significant time. In recent years, Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has shown tremendous promise in aiding the detection and diagnosis of heart disease by automating and optimizing these processes.

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Heart Disease Detection Using Machine Learning & Deep Learning: A Review

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The study was conducted to evaluate passengers' awareness and satisfaction regarding the Chalo app in Coimbatore. As more people rely on digital tools for convenient public transport, the Chalo app is gaining popularity. It offers helpful feature such as live bus tracking, estimated arrival times and route planning, all of which contribute to a more efficient travel experience. In a city like Coimbatore, where public transportation plays a vital role in daily commuting, digital solution like Chalo prove to be highly beneficial. However, despite its advantage, many passengers are still unaware of the app. Additionally, some users have reported issues such as app crashes, inaccurate bus timing. The study suggests that increased awareness efforts and improvements in the app's accuracy and real-time functionality are needed. Overall, while the Chalo app is a valuable tool for enhancing public transport, it requires regular updates to meet the evolving needs of its users.

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Public Transport

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Celebrity endorsements and their counterparts have been examined in the study to highlight their possible impacts on consumers' buying behaviour relative to the fast-moving consumer goods (FMCG) sector. A sample of 120 respondents has been drawn from this exercise in testing the effectiveness of both forms of endorsements for building trust in the brand, recalling the brand, and, ultimately, deciding on purchases. What they found is that even though celebrity endorsement brings the attention and visibility of a brand, the endorser actually tends to be perceived as more genuine and, therefore, more trustworthy. Honest feedback and relatability of the non-celebrity endorser make this more credible in most cases. However, celebrity endorsements still have a better edge when intending to influence buying decisions-as with younger demographics. As such, the conclusions drawn from the study are that an ideal combination of both the endorsements will maximize the marketing impact and consumer engagement.

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 Keywords

Celebrity endorsement, FMCG Advertisement, impact of celebrity and non celebrity advertisement.

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Creative Commons Attribution 4.0 and The Open Definition

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The autonomous multipurpose robot system technology is transforming the restaurant industry by enhancing service efficiency and customer experience. This study presents a comprehensive approach that combines electronic menu (e-menu) systems with autonomous multipurpose robot to address challenges such as order inaccuracies, service delays, and labor shortages. The e-Menu which uses LCD display provides a real time update to customers with an interactive and visually appealing platform to browse and order dishes, including detailed information about ingredients and allergies. The multipurpose robot which uses line follower technique assists in taking orders and delivering food, allowing human staff to focus on other tasks such as cooking and customer service. The main aim is to automate and improve the ordering and billing processes in restaurants along with real time customer interaction and feedback.

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Autonomous Robot, Restaurant Service Robot, Multi-purpose Robot, Electronic Menu, Smart RestaurantsDelivery, Efficiency, Contactless Service

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Bharat mein balatkat kanoono ka vikas evam ehtihasik paripekshya

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Bharat mein balatkat kanoono ka vikas evam ehtihasik paripekshya

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Forecasting is crucial in supply chain management, influencing inventory control, production planning, and customer satisfaction. As supply chains grow more complex and volatile, traditional forecasting methods often fail to deliver accurate and timely insights. This paper examines the impact of artificial intelligence (AI)-based forecasting models on supply chain resilience and operational efficiency. Using a combination of literature review, industry case studies, and simulation modeling, we demonstrate that AI-driven forecasting improves demand prediction accuracy, reduces the bullwhip effect, and enhances overall performance. The results show that organizations adopting AI-powered forecasting can achieve significant gains in efficiency and adaptability, particularly in response to supply chain disruptions. These findings offer valuable guidance for businesses seeking to implement AI technologies to optimize supply chain operations.

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 Keywords

Supply chain Management, Forecasting, Artificial Intelliegence, Machine learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition