2024 [Vol 03 Issue 1]
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Rohith Kumar Ragala Dr T. M. Usha, Dedipya Edupallic USER-FRIENDLY STATIC CHATBOT IN IOT DEVICE FOR INTERACTION WITH FARMERS IN INTEGRATED RICE-FISH FARMING (Journal Article) In: International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS), vol. 03, iss. 01, pp. 119-131, 2024. @article{nokey,
title = {USER-FRIENDLY STATIC CHATBOT IN IOT DEVICE FOR INTERACTION WITH FARMERS IN INTEGRATED RICE-FISH FARMING },
author = {Dr T. M. Usha, Rohith Kumar Ragala, Dedipya Edupallic,
Hari Lakshmi A S, Sudhishna DVVK, SVDS Raghuram Akula
},
editor = {Dr. K. K. Patel},
url = {https://sciencetransactions.com/ijascis/uploads/2024/01/j24-119-131.pdf},
year = {2024},
date = {2024-02-01},
urldate = {2024-02-01},
journal = {International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS)},
volume = {03},
issue = {01},
pages = {119-131},
abstract = {This paper introduces an AI chatbot designed specifically for integrated rice-fish farming, which combines the cultivation of rice crops with fish rearing in the same ecosystem. The chatbot utilizes artificial intelligence techniques, including natural language processing and machine learning algorithms, to provide real-time information and support to farmers engaged in integrated rice-fish farming systems. It offers guidance on various aspects such as crop cultivation techniques, fish species selection, water management, pest and disease control, and market trends. By continuously learning from user interactions and analysing data, the chatbot tailors its responses and recommendations to meet the specific needs of individual farmers. The integration of AI technology in agriculture has the potential to revolution- ize traditional farming practices, optimize resource utilization, increase productivity, and enhance food security. The proposed AI chatbot for integrated rice-fish farming serves as a valuable tool for farmers, empowering them to leverage AI capabilities and make informed decisions, ultimately promoting efficient and sustainable farming practices within the context of integrated rice-fish ecosystems.},
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pubstate = {published},
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This paper introduces an AI chatbot designed specifically for integrated rice-fish farming, which combines the cultivation of rice crops with fish rearing in the same ecosystem. The chatbot utilizes artificial intelligence techniques, including natural language processing and machine learning algorithms, to provide real-time information and support to farmers engaged in integrated rice-fish farming systems. It offers guidance on various aspects such as crop cultivation techniques, fish species selection, water management, pest and disease control, and market trends. By continuously learning from user interactions and analysing data, the chatbot tailors its responses and recommendations to meet the specific needs of individual farmers. The integration of AI technology in agriculture has the potential to revolution- ize traditional farming practices, optimize resource utilization, increase productivity, and enhance food security. The proposed AI chatbot for integrated rice-fish farming serves as a valuable tool for farmers, empowering them to leverage AI capabilities and make informed decisions, ultimately promoting efficient and sustainable farming practices within the context of integrated rice-fish ecosystems. |
SVDS Raghuram Akula Dr T. M. Usha, Rohith Kumar Ragala A LITERATURE REVIEW ON INNOVATIVE APPROACHES TO INTEGRATED RICE-FISH FARMING FOR SUSTAINABLE AGRICULTURE AND AQUACULTURE (Journal Article) In: International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS), vol. 03, iss. 01, pp. 132-148, 2024. @article{nokey,
title = {A LITERATURE REVIEW ON INNOVATIVE APPROACHES TO INTEGRATED RICE-FISH FARMING FOR SUSTAINABLE AGRICULTURE AND AQUACULTURE},
author = {Dr T. M. Usha, SVDS Raghuram Akula, Rohith Kumar Ragala, Dedipya Edupallic, Sudhishna DVVK, Hari Lakshmi A S},
editor = {Dr. K. K. Patel},
url = {https://sciencetransactions.com/ijascis/uploads/2024/01/d23-132-148.pdf},
year = {2024},
date = {2024-02-01},
journal = {International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS)},
volume = {03},
issue = {01},
pages = {132-148},
abstract = {This paper introduces a new way of integrated rice-fish farming, which combines the cultivation of rice crops with fish rearing in the same ecosystem. Integrated rice-fish farming has emerged as a promising approach to address the challenges of sustainable agriculture and aquaculture. This literature review explores innovative strategies and practices that can be developed and implemented worldwide to promote the integration of rice cultivation and fish farming. There are many challenges faced by farmers, including water management, pH maintenance, temperature fluctuations, pesticide restrictions, and labor-intensive processes. To overcome these challenges various technologies can be used. IoT sensors and cloud are those type of technologies that helps in automation or real-time data monitoring that helps in efficient management of farm and increase in productivity.},
keywords = {},
pubstate = {published},
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This paper introduces a new way of integrated rice-fish farming, which combines the cultivation of rice crops with fish rearing in the same ecosystem. Integrated rice-fish farming has emerged as a promising approach to address the challenges of sustainable agriculture and aquaculture. This literature review explores innovative strategies and practices that can be developed and implemented worldwide to promote the integration of rice cultivation and fish farming. There are many challenges faced by farmers, including water management, pH maintenance, temperature fluctuations, pesticide restrictions, and labor-intensive processes. To overcome these challenges various technologies can be used. IoT sensors and cloud are those type of technologies that helps in automation or real-time data monitoring that helps in efficient management of farm and increase in productivity. |
Dedipya Edupalli Dr T. M. Usha, Hari Lakshmi A S SMART WEATHER PREDICTION FROM SENSOR DATA USING MACHINE LEARNING (Journal Article) In: International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS), vol. 03, iss. 01, pp. 149-163, 2024. @article{nokey,
title = {SMART WEATHER PREDICTION FROM SENSOR DATA USING MACHINE LEARNING },
author = {Dr T. M. Usha, Dedipya Edupalli, Hari Lakshmi A S, SVDS Raghuram Akula, Rohith Kumar Ragala, Sudhishna DVVK},
editor = {Dr. K. K. Patel},
url = {https://sciencetransactions.com/ijascis/uploads/2024/01/d23-149-163.pdf},
year = {2024},
date = {2024-02-01},
journal = {International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS)},
volume = {03},
issue = {01},
pages = {149-163},
abstract = {In this study, we implemented a weather prediction model using multiple classification algorithms like logistic regression, KNN, SVM, Decision Tree, Naive Bayes, and XGBoost. Notably, the Random Forest classification model emerged as the most effective for forecasting diverse weather parameters like drizzle, rain, sun, snow, and fog. Leveraging historical data, machine learning enhances weather forecasting accuracy by identifying patterns and handling complex relationships, integrating various data sources like satellite imagery. The model, utilizing a dataset with features such as temperature, humidity, wind speed, and atmospheric pressure, underwent preprocessing for missing values and feature normalization. The Random Forest algorithm demonstrated an 86% accuracy, validated by the confusion matrix analysis during training and evaluation. This study underscores the Random Forest algorithm's efficacy in multiclass weather prediction, emphasizing its potential to revolutionize forecasting accuracy and planning capabilities, outperforming existing strategies in precision and computational efficiency.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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In this study, we implemented a weather prediction model using multiple classification algorithms like logistic regression, KNN, SVM, Decision Tree, Naive Bayes, and XGBoost. Notably, the Random Forest classification model emerged as the most effective for forecasting diverse weather parameters like drizzle, rain, sun, snow, and fog. Leveraging historical data, machine learning enhances weather forecasting accuracy by identifying patterns and handling complex relationships, integrating various data sources like satellite imagery. The model, utilizing a dataset with features such as temperature, humidity, wind speed, and atmospheric pressure, underwent preprocessing for missing values and feature normalization. The Random Forest algorithm demonstrated an 86% accuracy, validated by the confusion matrix analysis during training and evaluation. This study underscores the Random Forest algorithm's efficacy in multiclass weather prediction, emphasizing its potential to revolutionize forecasting accuracy and planning capabilities, outperforming existing strategies in precision and computational efficiency. |
Nilesh N. Bokhani, Nilesh K. Modi QR Code Version Analysis for Improved Pharmaceutical Label Identification Using Open Source Libraries (Journal Article) In: International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS), vol. 03, iss. 01, pp. 164-172, 2024. @article{nokey,
title = {QR Code Version Analysis for Improved Pharmaceutical Label Identification Using Open Source Libraries},
author = {Nilesh N. Bokhani, Nilesh K. Modi
},
editor = {Dr. K. K. Patel},
url = {https://sciencetransactions.com/ijascis/uploads/2024/01/d23-164-172.pdf},
year = {2024},
date = {2024-02-01},
journal = {International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS)},
volume = {03},
issue = {01},
pages = {164-172},
abstract = {The pharmaceutical industry finds itself at an important juncture, driven by an increasing need to enhance the identification of drug labels. This imperative arises from a commitment to safeguard patient well-being and maintain compliance with stringent regulatory standards. Within this context, our research paper conducts a comprehensive exploration into the utilization of various QR code versions as a means to optimize pharmaceutical label identification. Leveraging open-source libraries, we delve into the multifaceted capabilities of QR codes, which are renowned for their capacity to store a wide array of data types. However, we emphasize that the selection of a specific QR code version can wield a substantial influence on the efficacy and reliability of label identification processes. Our study takes a deep dive into the diverse QR code versions available, with a particular focus on their data encoding capacities, error correction capabilities, and suitability for the intricate landscape of pharmaceutical labels. We rigorously evaluate the performance of these QR code versions across various conditions and constraints, including label dimensions, print quality, and decoding speed. Importantly, we employ open-source libraries to implement QR code generation and decoding, ensuring cost-effectiveness and accessibility for a broad spectrum of stakeholders within the pharmaceutical sector. The outcomes of our research shed light on the QR code version that best aligns with the specific requirements and constraints of the pharmaceutical industry. These findings offer invaluable insights for pharmaceutical manufacturers, healthcare providers, and regulatory bodies as they endeavour to refine and streamline drug label identification processes. By making informed decisions regarding the most suitable QR code version, stakeholders can elevate patient safety, optimize medication management, and ensure strict adherence to evolving regulatory frameworks. This research contributes significantly to the ongoing mission of harnessing technology to advance healthcare practices, emphasizing the pivotal role of informed decision-making in pharmaceutical labeling through the optimization of QR codes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The pharmaceutical industry finds itself at an important juncture, driven by an increasing need to enhance the identification of drug labels. This imperative arises from a commitment to safeguard patient well-being and maintain compliance with stringent regulatory standards. Within this context, our research paper conducts a comprehensive exploration into the utilization of various QR code versions as a means to optimize pharmaceutical label identification. Leveraging open-source libraries, we delve into the multifaceted capabilities of QR codes, which are renowned for their capacity to store a wide array of data types. However, we emphasize that the selection of a specific QR code version can wield a substantial influence on the efficacy and reliability of label identification processes. Our study takes a deep dive into the diverse QR code versions available, with a particular focus on their data encoding capacities, error correction capabilities, and suitability for the intricate landscape of pharmaceutical labels. We rigorously evaluate the performance of these QR code versions across various conditions and constraints, including label dimensions, print quality, and decoding speed. Importantly, we employ open-source libraries to implement QR code generation and decoding, ensuring cost-effectiveness and accessibility for a broad spectrum of stakeholders within the pharmaceutical sector. The outcomes of our research shed light on the QR code version that best aligns with the specific requirements and constraints of the pharmaceutical industry. These findings offer invaluable insights for pharmaceutical manufacturers, healthcare providers, and regulatory bodies as they endeavour to refine and streamline drug label identification processes. By making informed decisions regarding the most suitable QR code version, stakeholders can elevate patient safety, optimize medication management, and ensure strict adherence to evolving regulatory frameworks. This research contributes significantly to the ongoing mission of harnessing technology to advance healthcare practices, emphasizing the pivotal role of informed decision-making in pharmaceutical labeling through the optimization of QR codes. |
Aniket S. Inamdar, Sheetal Girase INTEROPERABILITY BETWEEN BLOCKCHAIN NETWORKS TO SUPPORT DECENTRALIZED APPLICATIONS (Journal Article) In: International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS), vol. 03, iss. 01, pp. 173-182, 2024. @article{nokey,
title = {INTEROPERABILITY BETWEEN BLOCKCHAIN NETWORKS TO SUPPORT DECENTRALIZED APPLICATIONS },
author = {Aniket S. Inamdar, Sheetal Girase},
editor = {Dr. K. K. Patel},
url = {https://sciencetransactions.com/ijascis/uploads/2024/01/d23-173-182-1.pdf},
journal = {International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS)},
volume = {03},
issue = {01},
pages = {173-182},
abstract = {Interest has been generated by the possibility of using decentralized, transparent, and secure systems enabled by blockchain technology to transform various industries. Blockchain networks have nonetheless become increasingly fragmented, which has made it more difficult to transmit and communicate data effectively. Interoperability between various networks is necessary if blockchain technology is to completely pay off. This paper discusses various issues related to the interoperability between blockchain networks along with a solution in the form of appXchain, an application layer-based strategy. DApps, or decentralized applications, are used to facilitate seamless integration and communication among various blockchain platforms. AppXchain intends to enable the exchange of assets, smart contracts, and data across dissimilar systems while assuring security, scalability, and reliability by bridging the gap between various blockchain networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Interest has been generated by the possibility of using decentralized, transparent, and secure systems enabled by blockchain technology to transform various industries. Blockchain networks have nonetheless become increasingly fragmented, which has made it more difficult to transmit and communicate data effectively. Interoperability between various networks is necessary if blockchain technology is to completely pay off. This paper discusses various issues related to the interoperability between blockchain networks along with a solution in the form of appXchain, an application layer-based strategy. DApps, or decentralized applications, are used to facilitate seamless integration and communication among various blockchain platforms. AppXchain intends to enable the exchange of assets, smart contracts, and data across dissimilar systems while assuring security, scalability, and reliability by bridging the gap between various blockchain networks. |
Arpita Patnaik, Merlin Nandy INFLUENCER MARKETING- A DATA DRIVEN APPROACH OF TRENDS AND STRUCTURES (Journal Article) In: International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS), vol. 03, iss. 01, pp. 183-197, 2024. @article{nokey,
title = {INFLUENCER MARKETING- A DATA DRIVEN APPROACH OF TRENDS AND STRUCTURES},
author = {Arpita Patnaik, Merlin Nandy},
editor = {Dr. K. K. Patel},
url = {https://sciencetransactions.com/ijascis/uploads/2024/01/d23-183-197.pdf},
year = {2024},
date = {2024-02-01},
journal = {International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS)},
volume = {03},
issue = {01},
pages = {183-197},
abstract = {The paper looks at a data driven approach to analyzing the trends wen influencer marketing. Who are the top authors, what are they talking about? What kind of approach is being taken by the papers wen terms of methodology and if the key tenets of answering how to recruit the right influencers or find the correct influencer – brand –fit can be achieved today!.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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The paper looks at a data driven approach to analyzing the trends wen influencer marketing. Who are the top authors, what are they talking about? What kind of approach is being taken by the papers wen terms of methodology and if the key tenets of answering how to recruit the right influencers or find the correct influencer – brand –fit can be achieved today!. |
Ayushmita Bhattacharjee Disha Banerjee Anal Rauth, Soumik Chakraborty Multi-Sensor Intelligent Robot designed for Mentally Challenged (Journal Article) In: International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS), vol. 03, iss. 01, pp. 198-210, 2024. @article{nokey,
title = {Multi-Sensor Intelligent Robot designed for Mentally Challenged},
author = {Anal Rauth, Ayushmita Bhattacharjee
Disha Banerjee, Soumik Chakraborty, Srijan Mondal,
Debmitra Ghosh
},
editor = {Dr. K. K. Patel},
url = {https://sciencetransactions.com/ijascis/uploads/2024/01/j23-198-210.pdf},
year = {2024},
date = {2024-02-01},
journal = {International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS)},
volume = {03},
issue = {01},
pages = {198-210},
abstract = {The proposed intelligent obstacle avoidance design of an autonomous mobile robot based on multi-sensor information fusion seems to address the limitations of existing ultrasonic-based obstacle avoidance systems by incorporating visual information from road signs and adding an infrared ranging sensor for avoiding pits. Overall, this design aims to enhance obstacle avoidance capabilities by integrating visual information from road signs and incorporating an infrared ranging sensor. By fusing data from multiple sensors and employing intelligent logic, the robot can autonomously navigate through a multi-obstruction environment, avoiding obstacles and pits effectively. This model involves Road Sign Detection, Multi-Sensor Information Fusion, Path Planning, Obstacle Avoidance Logic, Control System, and Feasibility Verification. The design's feasibility is verified through analysis and experimentation. The acquired distances from the ultrasonic sensor and infrared distance measuring sensors are analysed to ensure they provide accurate and reliable information. Additionally, the trained road sign detection model is evaluated to assess its performance. Finally, experiments are conducted in manually constructed complex environments to validate the effectiveness of the proposed design.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
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The proposed intelligent obstacle avoidance design of an autonomous mobile robot based on multi-sensor information fusion seems to address the limitations of existing ultrasonic-based obstacle avoidance systems by incorporating visual information from road signs and adding an infrared ranging sensor for avoiding pits. Overall, this design aims to enhance obstacle avoidance capabilities by integrating visual information from road signs and incorporating an infrared ranging sensor. By fusing data from multiple sensors and employing intelligent logic, the robot can autonomously navigate through a multi-obstruction environment, avoiding obstacles and pits effectively. This model involves Road Sign Detection, Multi-Sensor Information Fusion, Path Planning, Obstacle Avoidance Logic, Control System, and Feasibility Verification. The design's feasibility is verified through analysis and experimentation. The acquired distances from the ultrasonic sensor and infrared distance measuring sensors are analysed to ensure they provide accurate and reliable information. Additionally, the trained road sign detection model is evaluated to assess its performance. Finally, experiments are conducted in manually constructed complex environments to validate the effectiveness of the proposed design.
|
Ms. Bhumika Desai, Dr. Dharmendra Bhatti In-depth Scrutiny of Facial Expressions and Determination of the Happiness Index in University Students during Academic Lectures: A Thorough Review (Journal Article) In: International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS), vol. 03, iss. 01, pp. 211-215, 2024. @article{nokey,
title = {In-depth Scrutiny of Facial Expressions and Determination of the Happiness Index in University Students during Academic Lectures: A Thorough Review },
author = {Ms. Bhumika Desai, Dr. Dharmendra Bhatti
},
editor = {Dr. K. K. Patel},
url = {https://sciencetransactions.com/ijascis/uploads/2024/01/d23-211-215.pdf},
year = {2024},
date = {2024-02-01},
journal = {International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS)},
volume = {03},
issue = {01},
pages = {211-215},
abstract = {This review examines a variety of academic studies that focus on examining the face and well-being of college students in various fields. The main aim is to gain a deeper understanding of how students' emotions are expressed through facial expressions and to identify factors that influence their well-being. A comprehensive review of the existing literature over the last decade has been conducted and key findings have been summarized. Additionally, this paper discusses the implications of these findings on student learning and well-being while explaining the proposed course of action. },
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This review examines a variety of academic studies that focus on examining the face and well-being of college students in various fields. The main aim is to gain a deeper understanding of how students' emotions are expressed through facial expressions and to identify factors that influence their well-being. A comprehensive review of the existing literature over the last decade has been conducted and key findings have been summarized. Additionally, this paper discusses the implications of these findings on student learning and well-being while explaining the proposed course of action. |
Gautam Deshpande Vaibhav Gurap, Tanmay Devare Enhancing Customer Experience and Efficiency through an After Sales Service Portal (Journal Article) In: International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS), vol. 03, iss. 01, pp. 216-229, 2024. @article{nokey,
title = {Enhancing Customer Experience and Efficiency through an After Sales Service Portal},
author = {Vaibhav Gurap, Gautam Deshpande, Tanmay Devare, Rudra Chopde, Prof. Umesh Raut.},
editor = {Dr. K. K. Patel},
url = {https://sciencetransactions.com/ijascis/uploads/2024/01/j24-216-229.pdf},
year = {2024},
date = {2024-02-01},
journal = {International Journal of Advances in Soft Computing and Intelligent Systems (IJASCIS)},
volume = {03},
issue = {01},
pages = {216-229},
abstract = {This research paper presents an innovative After Sales Service Portal, designed to simplify the repair and replacement process for customers dealing with damaged products. Purchasing products is straightforward, but managing repairs and replacements can be cumbersome. Our portal allows customers to register and submit requests for repairing or replacing faulty items, aiming to enhance customer satisfaction and overall efficiency in after-sales services. It bridges the gap between the product, customer, service center, and service requests, involving technicians. Leveraging modern technology, this portal optimizes the after-sales service experience for all stakeholders. The paper explores the development and implementation of the portal, its impact on customer experience and service dealer operations, and discusses potential future enhancements. This research has the potential to revolutionize after-sales support across various industries.},
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pubstate = {published},
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This research paper presents an innovative After Sales Service Portal, designed to simplify the repair and replacement process for customers dealing with damaged products. Purchasing products is straightforward, but managing repairs and replacements can be cumbersome. Our portal allows customers to register and submit requests for repairing or replacing faulty items, aiming to enhance customer satisfaction and overall efficiency in after-sales services. It bridges the gap between the product, customer, service center, and service requests, involving technicians. Leveraging modern technology, this portal optimizes the after-sales service experience for all stakeholders. The paper explores the development and implementation of the portal, its impact on customer experience and service dealer operations, and discusses potential future enhancements. This research has the potential to revolutionize after-sales support across various industries. |