Pub Date : 2024-05-22DOI: 10.1007/s13205-024-04003-9
Asmaa I. El-Shazly, Marwa I. Wahba, Nayera A. M. Abdelwahed, Abeer N. Shehata
{"title":"Immobilization of alkaline protease produced by Streptomyces rochei strain NAM-19 in solid state fermentation based on medium optimization using central composite design","authors":"Asmaa I. El-Shazly, Marwa I. Wahba, Nayera A. M. Abdelwahed, Abeer N. Shehata","doi":"10.1007/s13205-024-04003-9","DOIUrl":"https://doi.org/10.1007/s13205-024-04003-9","url":null,"abstract":"","PeriodicalId":48765,"journal":{"name":"3 Biotech","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Som veer, M. Kumari, A. Pramanik, B. Lakshmaiah, B. Godara, PL Parameswari
Artificial Intelligence (AI) algorithms are increasingly being employed as substitutes for conventional methods or as components within integrated systems. They have demonstrated effectiveness in addressing complex applied problems across various domains, gaining popularity in the present context. AI approaches exhibit the ability to learn from patterns, tolerate faults by handling noisy data, and manage non-linear problems. Once trained, they excel in generalization and fast estimation. This survey presents a comprehensive review of AI algorithms developed for investigating nanofluid-related issues. In nanofluid research, the most commonly used neural network model is Multilayer perceptron neural network (MLP), while the Radial Basis Function Neural Network (RBF-ANN) is the preferred training method. the Generalized Regression Neural Networks (GRNNs) exhibit a simple structure that reduces learning time, making them particularly suitable for nanofluids modelling. Consequently, for nanofluids with a large number of samples, the use of RBF-ANN is recommended. The findings demonstrate the substantial potential of ANN methods as predictive and optimization tools for nanofluids. This paper highlights the recent researches done for evaluating thermo-physical properties of nanofluids using AI algorithms.
{"title":"A Predictive Approach for Evaluating Thermo-Physical Properties of Nano fluids Using Artificial Intelligence Algorithms","authors":"Som veer, M. Kumari, A. Pramanik, B. Lakshmaiah, B. Godara, PL Parameswari","doi":"10.46632/jdaai/2/3/10","DOIUrl":"https://doi.org/10.46632/jdaai/2/3/10","url":null,"abstract":"Artificial Intelligence (AI) algorithms are increasingly being employed as substitutes for conventional methods or as components within integrated systems. They have demonstrated effectiveness in addressing complex applied problems across various domains, gaining popularity in the present context. AI approaches exhibit the ability to learn from patterns, tolerate faults by handling noisy data, and manage non-linear problems. Once trained, they excel in generalization and fast estimation. This survey presents a comprehensive review of AI algorithms developed for investigating nanofluid-related issues. In nanofluid research, the most commonly used neural network model is Multilayer perceptron neural network (MLP), while the Radial Basis Function Neural Network (RBF-ANN) is the preferred training method. the Generalized Regression Neural Networks (GRNNs) exhibit a simple structure that reduces learning time, making them particularly suitable for nanofluids modelling. Consequently, for nanofluids with a large number of samples, the use of RBF-ANN is recommended. The findings demonstrate the substantial potential of ANN methods as predictive and optimization tools for nanofluids. This paper highlights the recent researches done for evaluating thermo-physical properties of nanofluids using AI algorithms.","PeriodicalId":48765,"journal":{"name":"3 Biotech","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77870398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Food is considered as a basic need of human being which can be satisfied through farming. Agriculture not only fulfils human’s basic needs, but also considered as source of employment worldwide. Agriculture is considered as a backbone of economy and source of employment in the developing countries like in India. Indian farmer still struggles when it comes to picking up the right crop for right biological and non-biological factors. Thus, to accelerate the yield of crops, different AI techniques been proposed worldwide. Advancement in area of machine learning has helped improving gains in agriculture. Machine learning is the current technology which is benefiting farmers to minimize the losses in the farming by providing rich recommendations and insights about the crops. This paper presents an extensive of latest machine learning techniques in agriculture. Techniques of machine learning in agriculture allows more efficient and precise farming with less human manpower with quality production.
{"title":"Machine Learning Techniques in Agriculture","authors":"M. Menaha, J. Lavanya","doi":"10.46632//jdaai/2/3/5","DOIUrl":"https://doi.org/10.46632//jdaai/2/3/5","url":null,"abstract":"Food is considered as a basic need of human being which can be satisfied through farming. Agriculture not only fulfils human’s basic needs, but also considered as source of employment worldwide. Agriculture is considered as a backbone of economy and source of employment in the developing countries like in India. Indian farmer still struggles when it comes to picking up the right crop for right biological and non-biological factors. Thus, to accelerate the yield of crops, different AI techniques been proposed worldwide. Advancement in area of machine learning has helped improving gains in agriculture. Machine learning is the current technology which is benefiting farmers to minimize the losses in the farming by providing rich recommendations and insights about the crops. This paper presents an extensive of latest machine learning techniques in agriculture. Techniques of machine learning in agriculture allows more efficient and precise farming with less human manpower with quality production.","PeriodicalId":48765,"journal":{"name":"3 Biotech","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89808763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meena S. Gomathi, S. Dharani, R. Manikandan, Jeshrak Sam. V.
Person re-identification (Re-ID) is an essential part of visual surveillance that aims to identify and locate persons from multiple network cameras without conflicting viewpoints. Although significant advances have been made in recent years with the use of deep learning, there are still many challenges in vision such as occlusion, exposure, background clutter, misalignment, scale, perspective, low resolution and illumination, and cross-camera methods. Dressing redefinition is a hot topic in education right now. Most existing methods assume that people's clothes do not change in a short time, but they do not apply when people change clothes. Accordingly, this article introduces a double-layer garment changer re-identification network that integrates the secondary care process through clustering and fine-grained knowledge in space and training the garment classification branch to increase the sensitivity of the network to garment characteristics. In this method, auxiliary equipment such as human bone is not used and the complexity of the model is greatly reduced compared to other methods. This article runs experiments on the famous redefined PRCC data and large-scale long-term dataset (LaST). Experimental results show that the method in this article is superior to existing methods.
{"title":"Identification of Changing Personnel with Double-Layer Network Fusion and Bi-Level Monitoring Mechanism","authors":"Meena S. Gomathi, S. Dharani, R. Manikandan, Jeshrak Sam. V.","doi":"10.46632/jdaai/2/3/3","DOIUrl":"https://doi.org/10.46632/jdaai/2/3/3","url":null,"abstract":"Person re-identification (Re-ID) is an essential part of visual surveillance that aims to identify and locate persons from multiple network cameras without conflicting viewpoints. Although significant advances have been made in recent years with the use of deep learning, there are still many challenges in vision such as occlusion, exposure, background clutter, misalignment, scale, perspective, low resolution and illumination, and cross-camera methods. Dressing redefinition is a hot topic in education right now. Most existing methods assume that people's clothes do not change in a short time, but they do not apply when people change clothes. Accordingly, this article introduces a double-layer garment changer re-identification network that integrates the secondary care process through clustering and fine-grained knowledge in space and training the garment classification branch to increase the sensitivity of the network to garment characteristics. In this method, auxiliary equipment such as human bone is not used and the complexity of the model is greatly reduced compared to other methods. This article runs experiments on the famous redefined PRCC data and large-scale long-term dataset (LaST). Experimental results show that the method in this article is superior to existing methods.","PeriodicalId":48765,"journal":{"name":"3 Biotech","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80327813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Assessment on The Manufacturing Environment Using the Grey Relational Analysis Method","authors":"","doi":"10.46632/jemm/9/3/1","DOIUrl":"https://doi.org/10.46632/jemm/9/3/1","url":null,"abstract":"","PeriodicalId":48765,"journal":{"name":"3 Biotech","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79929721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Kumari, Som veer, RR Deshmukh, RV Vinchurkar, PL Parameswari
Precision Dairy Farming (PDF)” or “The Per Animal Approach” can be enhanced through the implementation of three-dimensional computer vision, which offers improved cattle identification, disease monitoring, and growth assessment. The integration of 3D vision systems is particularly vital for advancing dairy farming practices in the next generation. These systems facilitate the automation of various animal husbandry tasks, including monitoring, herding, feeding, milking, and bedding of animals. The applications of 3D computer vision in PLF encompass diverse platforms, such as 3D camera installations for monitoring cow walking postures, and intelligent systems that interact safely with animals, capable of identifying dairy cattle and detecting health indicators like animal identification, recognition, body condition score, and lameness. To be effective, systems must be adaptable to unconstrained environments, varying herd characteristics, weather conditions, farmyard layouts, and animal-machine interaction scenarios. Considering these requirements, this paper proposes the application of emerging computer vision and artificial intelligence techniques in dairy farming. This review encourages future research in three-dimensional computer vision for cattle growth management and its potential extension to other livestock and wild animals
{"title":"Computer Vision Driven Precision Dairy Farming for Efficient Cattle Management","authors":"M. Kumari, Som veer, RR Deshmukh, RV Vinchurkar, PL Parameswari","doi":"10.46632/jdaai/2/3/9","DOIUrl":"https://doi.org/10.46632/jdaai/2/3/9","url":null,"abstract":"Precision Dairy Farming (PDF)” or “The Per Animal Approach” can be enhanced through the implementation of three-dimensional computer vision, which offers improved cattle identification, disease monitoring, and growth assessment. The integration of 3D vision systems is particularly vital for advancing dairy farming practices in the next generation. These systems facilitate the automation of various animal husbandry tasks, including monitoring, herding, feeding, milking, and bedding of animals. The applications of 3D computer vision in PLF encompass diverse platforms, such as 3D camera installations for monitoring cow walking postures, and intelligent systems that interact safely with animals, capable of identifying dairy cattle and detecting health indicators like animal identification, recognition, body condition score, and lameness. To be effective, systems must be adaptable to unconstrained environments, varying herd characteristics, weather conditions, farmyard layouts, and animal-machine interaction scenarios. Considering these requirements, this paper proposes the application of emerging computer vision and artificial intelligence techniques in dairy farming. This review encourages future research in three-dimensional computer vision for cattle growth management and its potential extension to other livestock and wild animals","PeriodicalId":48765,"journal":{"name":"3 Biotech","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87342615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Green supply chain management is highly significant to maintain environmental sustainability. The agglomeration of green components enhances and supports the business activities to practice green supply chain more effectively. Utilizing sustainable packaging materials in logistics is a step towards promoting business eco sustainability. This research work attempts to develop a hybrid decision making model by integrating techniques of fuzzy multi criteria decision making (MCDM) and Reinforcement Learning (RL). This research work proposes a decision-making method of IDOCRIW (Integrated Determination of Objective Criteria Weights) under fuzzy environment with linguistic representations to determine the criterion weights of material selection and applies the RL method of Q learning in ranking the packaging materials for promoting green sustainability. The proposed fuzzy based MCDM method resolves the problems of conflict of uncertainty. The ranking results obtained using this method are compared with the non-integrated MCDM method. The proposed combined model shall be discussed under various other extended fuzzy representations. The decision-making problem on optimal selection of packaging materials addressed in this research work benefits the business decision makers to make right choices. This hybrid model will certainly make the logistic environment more robust and also it will upscale the smart framework of supply chain management.
绿色供应链管理对保持环境的可持续性具有重要意义。绿色组件的集聚促进和支持企业活动更有效地践行绿色供应链。在物流中使用可持续包装材料是促进商业生态可持续发展的一步。本研究尝试将模糊多准则决策(MCDM)技术与强化学习(RL)技术相结合，建立一种混合决策模型。本研究提出了一种具有语言表征的模糊环境下IDOCRIW (Integrated Determination of Objective Criteria Weights)决策方法来确定材料选择的标准权重，并将Q学习的RL方法应用于促进绿色可持续发展的包装材料排序。提出的基于模糊的MCDM方法解决了不确定性冲突问题。将该方法得到的排序结果与非综合MCDM方法进行了比较。所提出的组合模型将在各种其他扩展模糊表示下进行讨论。本研究所解决的包装材料最优选择的决策问题，有利于企业决策者做出正确的选择。这种混合模式将使物流环境更加稳健，也将提升供应链管理的智能框架。
{"title":"Promoting Green Supply Chain Management With Optimal Selection Of Packaging Materials Using Integrated Fuzzy MCDM and Rl Model","authors":"","doi":"10.46632/ese/2/3/1","DOIUrl":"https://doi.org/10.46632/ese/2/3/1","url":null,"abstract":"Green supply chain management is highly significant to maintain environmental sustainability. The agglomeration of green components enhances and supports the business activities to practice green supply chain more effectively. Utilizing sustainable packaging materials in logistics is a step towards promoting business eco sustainability. This research work attempts to develop a hybrid decision making model by integrating techniques of fuzzy multi criteria decision making (MCDM) and Reinforcement Learning (RL). This research work proposes a decision-making method of IDOCRIW (Integrated Determination of Objective Criteria Weights) under fuzzy environment with linguistic representations to determine the criterion weights of material selection and applies the RL method of Q learning in ranking the packaging materials for promoting green sustainability. The proposed fuzzy based MCDM method resolves the problems of conflict of uncertainty. The ranking results obtained using this method are compared with the non-integrated MCDM method. The proposed combined model shall be discussed under various other extended fuzzy representations. The decision-making problem on optimal selection of packaging materials addressed in this research work benefits the business decision makers to make right choices. This hybrid model will certainly make the logistic environment more robust and also it will upscale the smart framework of supply chain management.","PeriodicalId":48765,"journal":{"name":"3 Biotech","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89621586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cardiovascular disease leads to heart attack disease even child children developing Asian countries. This Cardiovascular disease may affect the heart for various reasons for the child. The main objective of this research work is used to track and monitor the child from heart attack and also to protect the child from theft using GPS location with a wearable sensor. The Sensor embedded in the chain will monitor and track the child’s neuron activities based on the heartbeat, temperature, and GPS location of the Child. This Research work is classified into two sections. The first Section is used to track the neuro-centric activities of the child in terms of temperature, and heartbeat. If the Heartbeat is low or high and similarly if the child accidentally or incidentally body temperature is high. The information will be passed to their respective parents. In the Second Section, the child can be protected from theft using GPS Location. Initially, the parents had to set their border location, if the child cross the border, the alert information will be passed to the parents. This research work will be effective and efficient with Sensors using IoT to protect children physically and location-based.
{"title":"A Smart Neuro-Centric Approach to Predict Heart Attacks for Child Using IOT","authors":"K. Sai","doi":"10.46632/jdaai/2/3/4","DOIUrl":"https://doi.org/10.46632/jdaai/2/3/4","url":null,"abstract":"Cardiovascular disease leads to heart attack disease even child children developing Asian countries. This Cardiovascular disease may affect the heart for various reasons for the child. The main objective of this research work is used to track and monitor the child from heart attack and also to protect the child from theft using GPS location with a wearable sensor. The Sensor embedded in the chain will monitor and track the child’s neuron activities based on the heartbeat, temperature, and GPS location of the Child. This Research work is classified into two sections. The first Section is used to track the neuro-centric activities of the child in terms of temperature, and heartbeat. If the Heartbeat is low or high and similarly if the child accidentally or incidentally body temperature is high. The information will be passed to their respective parents. In the Second Section, the child can be protected from theft using GPS Location. Initially, the parents had to set their border location, if the child cross the border, the alert information will be passed to the parents. This research work will be effective and efficient with Sensors using IoT to protect children physically and location-based.","PeriodicalId":48765,"journal":{"name":"3 Biotech","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85827823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data mapping is one of the simplest terms is to map source data fields and their related target data fields. Mapping can have a varying degree of complexity, depending on the number, data types, schema, primary keys, and foreign keys of the data sources. Nowadays, Archaeological research is based on an interdisciplinary approach which makes use of a wide range of technologies allowing for the collection of data and information about sites and archaeological findings. The purpose of archaeology is to learn more about past societies and the development of the human race. An essential part of the archaeological data is related to spatial information that links historical contents to the metric reconstruction of monuments and artifacts, and show their mutual relations in a map. A critical a part of the archaeological records is associated with spatial data that links ancient contents to the metric reconstruction. By processing a steady stream of all real-time data, organizations can make time-sensitive decisions faster than ever before, monitor emerging trends, course-correct rapidly and jump on new business opportunities. To design a data mapping framework process, the data from various sources uses a new proposed technique. To secure the high profile raw and analyzed data using the combination of hardware and software any key generation for data extraction and mapping. The information can be accessed only through the authenticated source of the framework and hence duplication and data theft is extremely difficult. This paper follows the various data mapping techniques handled in previous work and also shows the limitations of existing techniques.
{"title":"A Survey of Bigdata Analysis, Extracting Data and Mapping the Data","authors":"P. Hemalatha, J. Lavanya","doi":"10.46632/jdaai/2/3/6","DOIUrl":"https://doi.org/10.46632/jdaai/2/3/6","url":null,"abstract":"Data mapping is one of the simplest terms is to map source data fields and their related target data fields. Mapping can have a varying degree of complexity, depending on the number, data types, schema, primary keys, and foreign keys of the data sources. Nowadays, Archaeological research is based on an interdisciplinary approach which makes use of a wide range of technologies allowing for the collection of data and information about sites and archaeological findings. The purpose of archaeology is to learn more about past societies and the development of the human race. An essential part of the archaeological data is related to spatial information that links historical contents to the metric reconstruction of monuments and artifacts, and show their mutual relations in a map. A critical a part of the archaeological records is associated with spatial data that links ancient contents to the metric reconstruction. By processing a steady stream of all real-time data, organizations can make time-sensitive decisions faster than ever before, monitor emerging trends, course-correct rapidly and jump on new business opportunities. To design a data mapping framework process, the data from various sources uses a new proposed technique. To secure the high profile raw and analyzed data using the combination of hardware and software any key generation for data extraction and mapping. The information can be accessed only through the authenticated source of the framework and hence duplication and data theft is extremely difficult. This paper follows the various data mapping techniques handled in previous work and also shows the limitations of existing techniques.","PeriodicalId":48765,"journal":{"name":"3 Biotech","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88440791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Review on Material Selection for Small Wind Turbine Blades Using the WASPAS Method","authors":"","doi":"10.46632/jame/2/3/1","DOIUrl":"https://doi.org/10.46632/jame/2/3/1","url":null,"abstract":"","PeriodicalId":48765,"journal":{"name":"3 Biotech","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87125553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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