Pub Date : 2023-09-11DOI: 10.52783/tjjpt.v44.i3.237
Nand Kumar Et al.
Cybersecurity threats are malicious activities or events that pose risks to the confidentiality, integrity, and availability of digital information systems, networks, and data. These threats can encompass a wide range of actions conducted by cybercriminals, hackers, or even insiders with malicious intent. Understanding these threats is crucial in safeguarding digital assets and maintaining the trust and reliability of modern information technology. In the rapidly evolving landscape of cybersecurity, the relentless growth of cyber threats poses a formidable challenge to organizations worldwide. To combat these threats effectively, there is an increasing reliance on Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper explores the integration of AI and ML into cybersecurity for threat detection and response, shedding light on the transformative impact of these technologies. AI (Artificial Intelligence) and ML (Machine Learning) have the potential to both bolster cybersecurity defences and, paradoxically, facilitate cyberattacks. On the defensive side, AI and ML technologies enhance threat detection and response, allowing organizations to identify and mitigate threats more efficiently. They can analyse vast amounts of data in real-time, spot anomalies, and recognize patterns indicative of potential cyberattacks. However, cybercriminals are also harnessing the power of AI and ML to perpetrate more sophisticated and targeted attacks. Ethical considerations surrounding AI in cybersecurity, including privacy concerns and responsible AI implementation, are also discussed to ensure a balanced and secure approach. The paper underscores the transformative impact of AI and ML in bolstering cybersecurity practices. It advocates for the integration of AI as an indispensable tool to fortify organizations against the ever-evolving landscape of cyber threats, ultimately enhancing their ability to detect, respond to, and mitigate potential breaches.
{"title":"AI in Cybersecurity: Threat Detection and Response with Machine Learning","authors":"Nand Kumar Et al.","doi":"10.52783/tjjpt.v44.i3.237","DOIUrl":"https://doi.org/10.52783/tjjpt.v44.i3.237","url":null,"abstract":"Cybersecurity threats are malicious activities or events that pose risks to the confidentiality, integrity, and availability of digital information systems, networks, and data. These threats can encompass a wide range of actions conducted by cybercriminals, hackers, or even insiders with malicious intent. Understanding these threats is crucial in safeguarding digital assets and maintaining the trust and reliability of modern information technology. In the rapidly evolving landscape of cybersecurity, the relentless growth of cyber threats poses a formidable challenge to organizations worldwide. To combat these threats effectively, there is an increasing reliance on Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper explores the integration of AI and ML into cybersecurity for threat detection and response, shedding light on the transformative impact of these technologies. AI (Artificial Intelligence) and ML (Machine Learning) have the potential to both bolster cybersecurity defences and, paradoxically, facilitate cyberattacks. On the defensive side, AI and ML technologies enhance threat detection and response, allowing organizations to identify and mitigate threats more efficiently. They can analyse vast amounts of data in real-time, spot anomalies, and recognize patterns indicative of potential cyberattacks. However, cybercriminals are also harnessing the power of AI and ML to perpetrate more sophisticated and targeted attacks. Ethical considerations surrounding AI in cybersecurity, including privacy concerns and responsible AI implementation, are also discussed to ensure a balanced and secure approach. The paper underscores the transformative impact of AI and ML in bolstering cybersecurity practices. It advocates for the integration of AI as an indispensable tool to fortify organizations against the ever-evolving landscape of cyber threats, ultimately enhancing their ability to detect, respond to, and mitigate potential breaches.","PeriodicalId":39883,"journal":{"name":"Tuijin Jishu/Journal of Propulsion Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136025167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.52783/tjjpt.v44.i3.474
Swati Sharma Et al.
In the age of artificial intelligence (AI), marketing has evolved into a data-driven, personalized, and highly efficient discipline. AI-based marketing tools and algorithms offer businesses unparalleled opportunities to understand and engage with their target audiences. However, this technological advancement raises profound ethical questions regarding the intersection of profit-seeking and consumer trust. This paper explores the intricate relationship between ethical considerations in AI-based marketing and the delicate equilibrium between profitability and the preservation of consumer trust.The paper begins by delving into the ethical challenges that emerge as AI is integrated into marketing strategies. It emphasizes the importance of transparency and accountability in AI-based marketing practices. Highlighting the need for clear communication regarding data collection, AI utilization, and decision-making processes, the paper argues that transparency can serve as the cornerstone for fostering trust among consumers.Data privacy and consent form another critical aspect of ethical AI-based marketing. [1] It also stresses the need for robust data protection measures to safeguard customer information, thereby mitigating the risk of breaches and misuse.Balancing personalization with intrusion is a central theme, as AI enables hyper-targeted marketing campaigns. The paper underscores the importance of respecting user preferences and avoiding overly invasive tactics that may erode trust.AI-generated content is examined within the context of marketing ethics.Data security, customer profiling, accessibility, and ethical AI development are also discussed in detail as integral aspects of ethical considerations in AI-based marketing. It demonstrates that striking a harmonious balance between profit and consumer trust in AI-based marketing requires a proactive commitment to ethical principles. It advocates for responsible AI development, ongoing monitoring, and adaptability to evolving ethical standards. By adhering to these principles, businesses can maximize the potential of AI in marketing while ensuring that consumer trust remains a cornerstone of their success. Ultimately, the paper underscores the imperative for businesses to navigate the AI-based marketing landscape with a steadfast commitment to ethical considerations, thereby fostering enduring consumer trust and sustainable profitability.
{"title":"Ethical Considerations in AI-Based Marketing: Balancing Profit and Consumer Trust.","authors":"Swati Sharma Et al.","doi":"10.52783/tjjpt.v44.i3.474","DOIUrl":"https://doi.org/10.52783/tjjpt.v44.i3.474","url":null,"abstract":"In the age of artificial intelligence (AI), marketing has evolved into a data-driven, personalized, and highly efficient discipline. AI-based marketing tools and algorithms offer businesses unparalleled opportunities to understand and engage with their target audiences. However, this technological advancement raises profound ethical questions regarding the intersection of profit-seeking and consumer trust. This paper explores the intricate relationship between ethical considerations in AI-based marketing and the delicate equilibrium between profitability and the preservation of consumer trust.The paper begins by delving into the ethical challenges that emerge as AI is integrated into marketing strategies. It emphasizes the importance of transparency and accountability in AI-based marketing practices. Highlighting the need for clear communication regarding data collection, AI utilization, and decision-making processes, the paper argues that transparency can serve as the cornerstone for fostering trust among consumers.Data privacy and consent form another critical aspect of ethical AI-based marketing. [1] It also stresses the need for robust data protection measures to safeguard customer information, thereby mitigating the risk of breaches and misuse.Balancing personalization with intrusion is a central theme, as AI enables hyper-targeted marketing campaigns. The paper underscores the importance of respecting user preferences and avoiding overly invasive tactics that may erode trust.AI-generated content is examined within the context of marketing ethics.Data security, customer profiling, accessibility, and ethical AI development are also discussed in detail as integral aspects of ethical considerations in AI-based marketing. It demonstrates that striking a harmonious balance between profit and consumer trust in AI-based marketing requires a proactive commitment to ethical principles. It advocates for responsible AI development, ongoing monitoring, and adaptability to evolving ethical standards. By adhering to these principles, businesses can maximize the potential of AI in marketing while ensuring that consumer trust remains a cornerstone of their success. Ultimately, the paper underscores the imperative for businesses to navigate the AI-based marketing landscape with a steadfast commitment to ethical considerations, thereby fostering enduring consumer trust and sustainable profitability.","PeriodicalId":39883,"journal":{"name":"Tuijin Jishu/Journal of Propulsion Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136026026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.52783/tjjpt.v44.i3.606
Pradip Kumar Et al.
Tea Tourism is emerging as a new form of niche tourism in India, especially in the northeastern part of the country. A serene landscape in tea gardens is perhaps the most exotic and innovative way to enjoy nature. Tea tourism is emerging as a new type of sustainable cultural tourism where less research has been done. This study attempts to evaluate the prospects of tea gardens and their related products to attract inbound and domestic tourists in Assam, the largest tea-producing state of India. Problems of tea tourism in the study area are also discussed here and necessary suggestions have been given for maintaining its sustainability. Various activities associated with tea tourism destinations and their importance as tour components are also highlighted here. The findings of this study revealed that demographic factors, cultural backgrounds, amenities and activities available in the destinations, eco-friendly practices, etc. are important to understanding the prospects and problems of tea tourism in the form of SWOT analysis in study area.
{"title":"Tea Tourism: Evaluating Prospects and Problems of Tea Tourism in Assam, North East India","authors":"Pradip Kumar Et al.","doi":"10.52783/tjjpt.v44.i3.606","DOIUrl":"https://doi.org/10.52783/tjjpt.v44.i3.606","url":null,"abstract":"Tea Tourism is emerging as a new form of niche tourism in India, especially in the northeastern part of the country. A serene landscape in tea gardens is perhaps the most exotic and innovative way to enjoy nature. Tea tourism is emerging as a new type of sustainable cultural tourism where less research has been done. This study attempts to evaluate the prospects of tea gardens and their related products to attract inbound and domestic tourists in Assam, the largest tea-producing state of India. Problems of tea tourism in the study area are also discussed here and necessary suggestions have been given for maintaining its sustainability. Various activities associated with tea tourism destinations and their importance as tour components are also highlighted here. The findings of this study revealed that demographic factors, cultural backgrounds, amenities and activities available in the destinations, eco-friendly practices, etc. are important to understanding the prospects and problems of tea tourism in the form of SWOT analysis in study area.","PeriodicalId":39883,"journal":{"name":"Tuijin Jishu/Journal of Propulsion Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136026474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.52783/tjjpt.v44.i3.320
Santosh Yerasuri Et al.
The integration of Artificial Intelligence (AI) into the field of mechanical engineering has heralded a new era of innovation and efficiency, particularly in the realm of next-generation rocket propulsion systems. This abstract explores the transformative impact of AI in the development and optimization of rocket propulsion technologies, highlighting its potential to revolutionize the aerospace industry. AI-powered mechanical engineering solutions have emerged as a game-changer in the design and manufacturing of rocket propulsion systems. Through advanced machine learning algorithms and predictive analytics, AI can significantly enhance the efficiency of the development process. By analyzing vast datasets of historical performance data, AI can identify patterns and correlations that human engineers might overlook. This allows for the creation of propulsion systems that are not only more powerful but also safer and more reliable. AI plays a pivotal role in the optimization of rocket engines. Traditional optimization methods often require extensive computational resources and time-consuming simulations. AI, on the other hand, leverages neural networks and genetic algorithms to rapidly iterate through design possibilities, resulting in propulsion systems that are finely tuned for maximum performance and fuel efficiency. This not only reduces development costs but also accelerates the time-to-market for next-gen rocket propulsion systems. Safety is paramount in rocket propulsion systems, and AI offers innovative solutions in this regard as well. AI algorithms can continuously monitor and analyze sensor data during rocket launches, quickly identifying anomalies and potential issues.[1] This real-time monitoring allows for immediate corrective actions, reducing the risk of catastrophic failures and ensuring the safety of crewed and uncrewed missions. AI-integrated mechanical engineering solutions enable autonomous maintenance and diagnostics of propulsion systems. Through predictive maintenance models, AI can predict when components are likely to fail and schedule maintenance activities accordingly. This proactive approach not only extends the lifespan of propulsion systems but also minimizes downtime and operational disruptions.
{"title":"AI-Integrated Mechanical Engineering Solutions for Next-Gen Rocket Propulsion Systems","authors":"Santosh Yerasuri Et al.","doi":"10.52783/tjjpt.v44.i3.320","DOIUrl":"https://doi.org/10.52783/tjjpt.v44.i3.320","url":null,"abstract":"The integration of Artificial Intelligence (AI) into the field of mechanical engineering has heralded a new era of innovation and efficiency, particularly in the realm of next-generation rocket propulsion systems. This abstract explores the transformative impact of AI in the development and optimization of rocket propulsion technologies, highlighting its potential to revolutionize the aerospace industry. AI-powered mechanical engineering solutions have emerged as a game-changer in the design and manufacturing of rocket propulsion systems. Through advanced machine learning algorithms and predictive analytics, AI can significantly enhance the efficiency of the development process. By analyzing vast datasets of historical performance data, AI can identify patterns and correlations that human engineers might overlook. This allows for the creation of propulsion systems that are not only more powerful but also safer and more reliable. AI plays a pivotal role in the optimization of rocket engines. Traditional optimization methods often require extensive computational resources and time-consuming simulations. AI, on the other hand, leverages neural networks and genetic algorithms to rapidly iterate through design possibilities, resulting in propulsion systems that are finely tuned for maximum performance and fuel efficiency. This not only reduces development costs but also accelerates the time-to-market for next-gen rocket propulsion systems. Safety is paramount in rocket propulsion systems, and AI offers innovative solutions in this regard as well. AI algorithms can continuously monitor and analyze sensor data during rocket launches, quickly identifying anomalies and potential issues.[1] This real-time monitoring allows for immediate corrective actions, reducing the risk of catastrophic failures and ensuring the safety of crewed and uncrewed missions. AI-integrated mechanical engineering solutions enable autonomous maintenance and diagnostics of propulsion systems. Through predictive maintenance models, AI can predict when components are likely to fail and schedule maintenance activities accordingly. This proactive approach not only extends the lifespan of propulsion systems but also minimizes downtime and operational disruptions.","PeriodicalId":39883,"journal":{"name":"Tuijin Jishu/Journal of Propulsion Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136025468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.52783/tjjpt.v44.i3.672
Mohammad Namazi, Seyyed Hamidreza Rakhsha
This research aims to formulate a suitable model of tax avoidance by using the effects of quality components of management accounting information (environmental uncertainty, financial reporting, corporate governance, and profit management).The research method is quantitative and descriptive-analytical.This study's statistical population includes companies admitted to the Tehran Bahadur Stock Exchange from 2011 to 2020. The statistical sample was selected using the systematic elimination method of 161 companies and 1610 company years. The results using panel data show that the quality components of management accounting information significantly impact tax avoidance. Profit management, financial reporting, and corporate governance are effective in reducing tax avoidance, and the component of environmental uncertainty is effective in increasing tax avoidance.
{"title":"Compiling the Influence Model of Management Accounting Information Quality Components on Tax Avoidance of Tehran Stock Exchange Companies","authors":"Mohammad Namazi, Seyyed Hamidreza Rakhsha","doi":"10.52783/tjjpt.v44.i3.672","DOIUrl":"https://doi.org/10.52783/tjjpt.v44.i3.672","url":null,"abstract":"This research aims to formulate a suitable model of tax avoidance by using the effects of quality components of management accounting information (environmental uncertainty, financial reporting, corporate governance, and profit management).The research method is quantitative and descriptive-analytical.This study's statistical population includes companies admitted to the Tehran Bahadur Stock Exchange from 2011 to 2020. The statistical sample was selected using the systematic elimination method of 161 companies and 1610 company years. The results using panel data show that the quality components of management accounting information significantly impact tax avoidance. Profit management, financial reporting, and corporate governance are effective in reducing tax avoidance, and the component of environmental uncertainty is effective in increasing tax avoidance.","PeriodicalId":39883,"journal":{"name":"Tuijin Jishu/Journal of Propulsion Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136027343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.52783/tjjpt.v44.i3.488
Gonesh Chandra Saha Et al.
The advent of high-throughput technologies has ushered in an era of unprecedented data generation in the field of bioinformatics. Omics data, including genomics, transcriptomics, proteomics, and metabolomics, provide comprehensive insights into biological systems, but their integration poses significant challenges. Integrative analysis of multi-omics data holds the promise of unraveling complex biological phenomena and enabling personalized medicine. [1] Deep learning, a subset of machine learning, has gained prominence in bioinformatics due to its ability to automatically extract intricate patterns from large-scale multi-omics datasets. This paper presents an overview of the challenges and opportunities associated with the integrative analysis of multi-omics data using deep learning techniques in bioinformatics.The challenges in multi-omics integration primarily stem from data heterogeneity, dimensionality, and noise. One of the key opportunities presented by deep learning is its ability to capture complex, non-linear relationships in multi-omics data. The paper emphasizes the importance of interpretability and explainability in deep learning models applied to bioinformatics, as they play a crucial role in gaining biological insights and facilitating clinical decision-making. The integration of domain knowledge and biological context is highlighted as a critical aspect of model development. The paper showcases real-world applications of deep learning in multi-omics data integration, such as disease subtype classification, biomarker discovery, and drug response prediction. As the field continues to evolve, addressing these challenges and harnessing the potential of deep learning approaches will pave the way for transformative advancements in our understanding of complex biological systems and the development of precision medicine strategies.
{"title":"Integrative Analysis of Multi-Omics Data with Deep Learning: Challenges and Opportunities in Bioinformatics.","authors":"Gonesh Chandra Saha Et al.","doi":"10.52783/tjjpt.v44.i3.488","DOIUrl":"https://doi.org/10.52783/tjjpt.v44.i3.488","url":null,"abstract":"The advent of high-throughput technologies has ushered in an era of unprecedented data generation in the field of bioinformatics. Omics data, including genomics, transcriptomics, proteomics, and metabolomics, provide comprehensive insights into biological systems, but their integration poses significant challenges. Integrative analysis of multi-omics data holds the promise of unraveling complex biological phenomena and enabling personalized medicine. [1] Deep learning, a subset of machine learning, has gained prominence in bioinformatics due to its ability to automatically extract intricate patterns from large-scale multi-omics datasets. This paper presents an overview of the challenges and opportunities associated with the integrative analysis of multi-omics data using deep learning techniques in bioinformatics.The challenges in multi-omics integration primarily stem from data heterogeneity, dimensionality, and noise. One of the key opportunities presented by deep learning is its ability to capture complex, non-linear relationships in multi-omics data. The paper emphasizes the importance of interpretability and explainability in deep learning models applied to bioinformatics, as they play a crucial role in gaining biological insights and facilitating clinical decision-making. The integration of domain knowledge and biological context is highlighted as a critical aspect of model development. The paper showcases real-world applications of deep learning in multi-omics data integration, such as disease subtype classification, biomarker discovery, and drug response prediction. As the field continues to evolve, addressing these challenges and harnessing the potential of deep learning approaches will pave the way for transformative advancements in our understanding of complex biological systems and the development of precision medicine strategies.","PeriodicalId":39883,"journal":{"name":"Tuijin Jishu/Journal of Propulsion Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136026025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.52783/tjjpt.v44.i3.484
Rupali Mahajan, Rashmi Priyadarshini
Every year millions of hectares of forest are devastated by fire. Forest fires may occur due to Natural causes or Man-made causes. Natural causes include lightning which set trees on fire, High atmospheric temperatures and dryness (low humidity). Man-made causes include naked flame, cigarette or bidi, electric spark or any source of ignition that comes into contact with inflammable material. Reason could be any, but important is forest fire causes huge damage to forest and nature. Infact, fires on large scales cause air pollution, mar quality of stream water, threaten biodiversity and spoil the aesthetics of an area. Forest fire causes imbalances in nature and endangers biodiversity by reducing faunal and floral wealth. Therefore, there is a need to develop methods for timely detection of forest fire so that damage is minimum. Existed methods for forest monitoring and fire detection are traditional and based on human observation, satellite imaging, use of digital cameras. There are several drawbacks associated with them such as inefficiency, power consumption, latency, accuracy and implementation costs. Therefore, to address these problems the deployment of automatic fire detection system is necessary to allow early and fast fire extinction. In this paper we present an early fire detection system based on Wireless Sensor Network.
{"title":"Wireless Sensor Network Based Early Fire Detection System","authors":"Rupali Mahajan, Rashmi Priyadarshini","doi":"10.52783/tjjpt.v44.i3.484","DOIUrl":"https://doi.org/10.52783/tjjpt.v44.i3.484","url":null,"abstract":"Every year millions of hectares of forest are devastated by fire. Forest fires may occur due to Natural causes or Man-made causes. Natural causes include lightning which set trees on fire, High atmospheric temperatures and dryness (low humidity). Man-made causes include naked flame, cigarette or bidi, electric spark or any source of ignition that comes into contact with inflammable material. Reason could be any, but important is forest fire causes huge damage to forest and nature. Infact, fires on large scales cause air pollution, mar quality of stream water, threaten biodiversity and spoil the aesthetics of an area. Forest fire causes imbalances in nature and endangers biodiversity by reducing faunal and floral wealth. Therefore, there is a need to develop methods for timely detection of forest fire so that damage is minimum. Existed methods for forest monitoring and fire detection are traditional and based on human observation, satellite imaging, use of digital cameras. There are several drawbacks associated with them such as inefficiency, power consumption, latency, accuracy and implementation costs. Therefore, to address these problems the deployment of automatic fire detection system is necessary to allow early and fast fire extinction. In this paper we present an early fire detection system based on Wireless Sensor Network.","PeriodicalId":39883,"journal":{"name":"Tuijin Jishu/Journal of Propulsion Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136026235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.52783/tjjpt.v44.i3.467
Moeljadi Et al.
Modern villages are innovative sources of economic growth, harnessing the untapped potential of local resources to drive sustainable development. This study explores the dynamic interplay between local resources and the establishment of modern businesses in Pandansari Lor Village. By adopting a qualitative approach and using interviews and field observations, the research uncovers the distinct local resources that play a pivotal role in the community's economic transformation. The findings demonstrate the significance of natural resources, cultural heritage, and traditional knowledge as drivers of entrepreneurial initiatives, paving the way for the modernization of village businesses. The study not only contributes to the academic literature on rural development, but also provides valuable insights for policymakers and local communities seeking to leverage their inherent resources for sustainable economic growth and community well-being.The development of local resource management today shows that the community already has a tradition to do business, in the form of opening cafes around Coban Jahe waterfall and the cassava processing industry that already has a market outside the village. Industrialization must be based on the local potential of the village, namely processed cassava, and coconut with various variants of product diversification. Village industrialization is carried out in two ways, first the development of various appropriate technologies in the form of production machines to increase the processing capacity of cassava, coconut, and herbal plants, and second, digital technology and automationfor water resources management, namely Hippam management and fish farming. This industrialization process is very important for increasing village production capacity, village product quality and tourism village branding in the future. The development of village industry will be able to accelerate the realization of a modern tourism village.
{"title":"Exploring the Untapped Potential: The Role of Local Resources in Fostering Modern Village Businesses","authors":"Moeljadi Et al.","doi":"10.52783/tjjpt.v44.i3.467","DOIUrl":"https://doi.org/10.52783/tjjpt.v44.i3.467","url":null,"abstract":"Modern villages are innovative sources of economic growth, harnessing the untapped potential of local resources to drive sustainable development. This study explores the dynamic interplay between local resources and the establishment of modern businesses in Pandansari Lor Village. By adopting a qualitative approach and using interviews and field observations, the research uncovers the distinct local resources that play a pivotal role in the community's economic transformation. The findings demonstrate the significance of natural resources, cultural heritage, and traditional knowledge as drivers of entrepreneurial initiatives, paving the way for the modernization of village businesses. The study not only contributes to the academic literature on rural development, but also provides valuable insights for policymakers and local communities seeking to leverage their inherent resources for sustainable economic growth and community well-being.The development of local resource management today shows that the community already has a tradition to do business, in the form of opening cafes around Coban Jahe waterfall and the cassava processing industry that already has a market outside the village. Industrialization must be based on the local potential of the village, namely processed cassava, and coconut with various variants of product diversification. Village industrialization is carried out in two ways, first the development of various appropriate technologies in the form of production machines to increase the processing capacity of cassava, coconut, and herbal plants, and second, digital technology and automationfor water resources management, namely Hippam management and fish farming. This industrialization process is very important for increasing village production capacity, village product quality and tourism village branding in the future. The development of village industry will be able to accelerate the realization of a modern tourism village.","PeriodicalId":39883,"journal":{"name":"Tuijin Jishu/Journal of Propulsion Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136026331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.52783/tjjpt.v44.i3.257
Joti Devi Et al.
- In recent years, the pharmaceutical industry has witnessed a transformative shift in the way drugs are discovered and developed, thanks to the advent of deep learning. This paper explores the profound impact of deep learning techniques on various stages of drug discovery and development, from target identification and lead optimization to clinical trials and personalized medicine. Deep learning, a subset of artificial intelligence, has demonstrated exceptional capabilities in handling complex biological data, including genomics, proteomics, and chemical informatics. It enables the integration of vast and diverse datasets, facilitating the identification of potential drug targets with unprecedented accuracy. Moreover, deep learning models can predict the binding affinity of drug candidates to specific target proteins, expediting the lead optimization process and reducing the need for costly experimental iterations. Deep learning algorithms enhance patient stratification and biomarker discovery, ultimately leading to more successful trials with higher patient response rates. Additionally, the ability to analyze real-world patient data aids in the identification of adverse events and the development of safer drugs.
Perrsonalized medicine is another area greatly influenced by deep learning, as it allows for tailoring treatments to individual patients based on their unique genetic and clinical profiles. This promises to revolutionize patient care, optimizing therapeutic outcomes while minimizing adverse effects. Despite the remarkable advancements facilitated by deep learning, there are challenges to address, such as data privacy, interpretability of models, and regulatory considerations. This paper discusses these challenges and potential solutions. Deep learning has emerged as a powerful tool in the pharmaceutical industry, driving innovation, efficiency, and precision in drug discovery and development. Its integration into the drug development pipeline holds the promise of accelerating the delivery of safer and more effective therapies to patients worldwide, marking a significant milestone in the evolution of pharmaceutical science.
{"title":"The Role of Deep Learning in Pharma: Revolutionizing Drug Discovery and Development.","authors":"Joti Devi Et al.","doi":"10.52783/tjjpt.v44.i3.257","DOIUrl":"https://doi.org/10.52783/tjjpt.v44.i3.257","url":null,"abstract":"- In recent years, the pharmaceutical industry has witnessed a transformative shift in the way drugs are discovered and developed, thanks to the advent of deep learning. This paper explores the profound impact of deep learning techniques on various stages of drug discovery and development, from target identification and lead optimization to clinical trials and personalized medicine. Deep learning, a subset of artificial intelligence, has demonstrated exceptional capabilities in handling complex biological data, including genomics, proteomics, and chemical informatics. It enables the integration of vast and diverse datasets, facilitating the identification of potential drug targets with unprecedented accuracy. Moreover, deep learning models can predict the binding affinity of drug candidates to specific target proteins, expediting the lead optimization process and reducing the need for costly experimental iterations. Deep learning algorithms enhance patient stratification and biomarker discovery, ultimately leading to more successful trials with higher patient response rates. Additionally, the ability to analyze real-world patient data aids in the identification of adverse events and the development of safer drugs.
 Perrsonalized medicine is another area greatly influenced by deep learning, as it allows for tailoring treatments to individual patients based on their unique genetic and clinical profiles. This promises to revolutionize patient care, optimizing therapeutic outcomes while minimizing adverse effects. Despite the remarkable advancements facilitated by deep learning, there are challenges to address, such as data privacy, interpretability of models, and regulatory considerations. This paper discusses these challenges and potential solutions. Deep learning has emerged as a powerful tool in the pharmaceutical industry, driving innovation, efficiency, and precision in drug discovery and development. Its integration into the drug development pipeline holds the promise of accelerating the delivery of safer and more effective therapies to patients worldwide, marking a significant milestone in the evolution of pharmaceutical science.","PeriodicalId":39883,"journal":{"name":"Tuijin Jishu/Journal of Propulsion Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136026518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study's goal is to provide an early analysis of deep learning (DL) for signal identification in wireless systems that use non-orthogonal multiple access (NOMA). The successive interference cancellation (SIC) approach is frequently used at the receiver in NOMA systems when several users are decoded successively. Without explicitly calculating channels, a DL-based NOMA receiver can decode messages for several users at once. To estimate the multiuser uplink channel (CE) and recognize the initial broadcast signal in this study, it is recommended that a deep neural network with bi-directional long short-term memory (Bi-LSTM) be utilized. The suggested Bi-LSTM model, in contrast to conventional CE techniques, may immediately retrieve transmission signals impacted by channel distortion. During the offline training phase, the Bi-LTSM model is trained using simulation data based on channel statistics. The trained model is then applied to retrieve the transmitted symbols in the stage of online deployment. According to the findings, the DL method could outperform a maximum probability detector that considers interference effects when inter-symbol interference is substantial.
{"title":"Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network","authors":"Neeraj Dwivedi, Sachin Kumar, Sudeep Tanwar, Sudhanshu Tyagi","doi":"10.52783/tjjpt.v44.i3.697","DOIUrl":"https://doi.org/10.52783/tjjpt.v44.i3.697","url":null,"abstract":"This study's goal is to provide an early analysis of deep learning (DL) for signal identification in wireless systems that use non-orthogonal multiple access (NOMA). The successive interference cancellation (SIC) approach is frequently used at the receiver in NOMA systems when several users are decoded successively. Without explicitly calculating channels, a DL-based NOMA receiver can decode messages for several users at once. To estimate the multiuser uplink channel (CE) and recognize the initial broadcast signal in this study, it is recommended that a deep neural network with bi-directional long short-term memory (Bi-LSTM) be utilized. The suggested Bi-LSTM model, in contrast to conventional CE techniques, may immediately retrieve transmission signals impacted by channel distortion. During the offline training phase, the Bi-LTSM model is trained using simulation data based on channel statistics. The trained model is then applied to retrieve the transmitted symbols in the stage of online deployment. According to the findings, the DL method could outperform a maximum probability detector that considers interference effects when inter-symbol interference is substantial.","PeriodicalId":39883,"journal":{"name":"Tuijin Jishu/Journal of Propulsion Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136070655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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