Pub Date : 2024-02-01DOI: 10.55529/jaimlnn.42.14.21
Dr. Shweta Kulshrestha
In the rapidly evolving landscape of Industry 4.0, the integration of Artificial Intelligence (AI) into Human Resources (HR) practices has emerged as a pivotal factor in enhancing organizational efficiency. This research study delves into the multifaceted implications of AI adoption within HR departments and its overarching impact on the operational efficiency of organizations. In the era of Industry 4.0, characterized by advanced automation, connectivity, and data-driven decision-making, AI technologies are playing an increasingly significant role in reshaping traditional HR functions. This research aims to quantitatively assess the extent to which AI-driven HR practices influence employee recruitment, retention, development, and overall human capital management. By analyzing data from a diverse set of organizations across different industries, this study seeks to identify patterns, trends, and best practices related to AI integration in HR. The research methodology involves a combination of surveys, data analysis, and case studies to collect and analyze quantitative data on AI adoption in HR practices and the subsequent impact on organizational efficiency. Key performance indicators (KPIs) such as employee productivity, cost effectiveness, and strategic alignment are scrutinized in order to ascertain the correlation between AI in HR and organizational success. Preliminary findings indicate that AI-driven HR practices are facilitating more streamlined and data-informed decision-making processes, allowing organizations to make better-informed talent-related choices. The insights gained from this study will be instrumental in guiding organizations in optimizing their HR functions through AI integration, enabling them to adapt and thrive in the Industry 4.0 landscape. Additionally, this research contributes to a deeper understanding of the evolving dynamics between AI, HR practices, and organizational efficiency, with implications for strategic decision-making and policy development in the context of Industry 4.0.
{"title":"Quantitative Assessment on Investigation on the Impact of Artificial Intelligence on HR Practices and Organizational Efficiency for Industry 4.0","authors":"Dr. Shweta Kulshrestha","doi":"10.55529/jaimlnn.42.14.21","DOIUrl":"https://doi.org/10.55529/jaimlnn.42.14.21","url":null,"abstract":"In the rapidly evolving landscape of Industry 4.0, the integration of Artificial Intelligence (AI) into Human Resources (HR) practices has emerged as a pivotal factor in enhancing organizational efficiency. This research study delves into the multifaceted implications of AI adoption within HR departments and its overarching impact on the operational efficiency of organizations. In the era of Industry 4.0, characterized by advanced automation, connectivity, and data-driven decision-making, AI technologies are playing an increasingly significant role in reshaping traditional HR functions. This research aims to quantitatively assess the extent to which AI-driven HR practices influence employee recruitment, retention, development, and overall human capital management. By analyzing data from a diverse set of organizations across different industries, this study seeks to identify patterns, trends, and best practices related to AI integration in HR. The research methodology involves a combination of surveys, data analysis, and case studies to collect and analyze quantitative data on AI adoption in HR practices and the subsequent impact on organizational efficiency. Key performance indicators (KPIs) such as employee productivity, cost effectiveness, and strategic alignment are scrutinized in order to ascertain the correlation between AI in HR and organizational success. Preliminary findings indicate that AI-driven HR practices are facilitating more streamlined and data-informed decision-making processes, allowing organizations to make better-informed talent-related choices. The insights gained from this study will be instrumental in guiding organizations in optimizing their HR functions through AI integration, enabling them to adapt and thrive in the Industry 4.0 landscape. Additionally, this research contributes to a deeper understanding of the evolving dynamics between AI, HR practices, and organizational efficiency, with implications for strategic decision-making and policy development in the context of Industry 4.0.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"63 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897600","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 : 2024-02-01DOI: 10.55529/jaimlnn.42.1.13
Deepak Pantha
Due to the traditional agricultural system, losses of millions of money have been loses every year. Farmers were always ready in agricultural work without risking their lives. If smart methods can be adopted in the agricultural system, the farmers will not have to suffer much damage. Using machine learning and testing with Convolutional Neural Network algorithm (mobileNet method), in this research to find out the actual accuracy, 3642 photos of apple leaves of Kaggle dataset and CSV files are used. In this paper, using Python language with the help of Jupyter notebook, Eposes has been tested 15 times to create confusion metrics. In this paper, precision, recall, f1_ score and average accuracy have been found and studied. An average accuracy of 95 percent has been obtained from the study. 95% accuracy is considered as a good result of the test using machine learning. By adopting this method, we can also give more motivation to the agricultural sector.
{"title":"Fruits Leaf Disease Detection Using Convolutional Neural Network","authors":"Deepak Pantha","doi":"10.55529/jaimlnn.42.1.13","DOIUrl":"https://doi.org/10.55529/jaimlnn.42.1.13","url":null,"abstract":"Due to the traditional agricultural system, losses of millions of money have been loses every year. Farmers were always ready in agricultural work without risking their lives. If smart methods can be adopted in the agricultural system, the farmers will not have to suffer much damage. Using machine learning and testing with Convolutional Neural Network algorithm (mobileNet method), in this research to find out the actual accuracy, 3642 photos of apple leaves of Kaggle dataset and CSV files are used. In this paper, using Python language with the help of Jupyter notebook, Eposes has been tested 15 times to create confusion metrics. In this paper, precision, recall, f1_ score and average accuracy have been found and studied. An average accuracy of 95 percent has been obtained from the study. 95% accuracy is considered as a good result of the test using machine learning. By adopting this method, we can also give more motivation to the agricultural sector.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"11 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896808","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}
Rice is the name of the type of plant that is needed by humans in the world. The plant is used as the main source of energy by Most people in the world, especially on the Asian continent. The importance of rice plants makes rice widely planted in various regions. Most humans use rice as a staple crop. Therefore, rice production needs to be considered to meet the need for enough food for most people in the world. The main thing that needs to be considered in maximizing rice production is that when guarding rice plants, many factors that inhibit rice plants can be the cause of food crises in various regions. Therefore, the care of rice production needs to be considered. In addition to the lack of nutrients in water and soil in decreasing rice production, plant diseases also need to be considered. Some types of diseases that often attack rice plants include bacterial leaf blight, brown spots, and left smut. Therefore, there is knowledge of prevention efforts or early treatment before the disease attacks rice plants more widely. The efficacy of technology can be used in solving this problem, we can take advantage of artificial intelligence in it. Artificial intelligence is implemented for the detection of types of diseases in rice plants using image images on the leaves of rice plants. If the disease in rice plants can be detected, it will make it easier for rice plant farmers to overcome the disease. The ANN (Artificial neural network) algorithm can be used in this problem from the results of research on identifying the type of rice disease using the algorithm obtained an accuracy of 83%. This shows the ability of artificial intelligence in disease identification can help farmers detect types of diseases.
{"title":"Utilization of Artificial Neural Network in Rice Plant Disease Classification Using Leaf Image","authors":"Nandi Sunandar, Joko Sutopo","doi":"10.55529/ijrise.42.1.10","DOIUrl":"https://doi.org/10.55529/ijrise.42.1.10","url":null,"abstract":"Rice is the name of the type of plant that is needed by humans in the world. The plant is used as the main source of energy by Most people in the world, especially on the Asian continent. The importance of rice plants makes rice widely planted in various regions. Most humans use rice as a staple crop. Therefore, rice production needs to be considered to meet the need for enough food for most people in the world. The main thing that needs to be considered in maximizing rice production is that when guarding rice plants, many factors that inhibit rice plants can be the cause of food crises in various regions. Therefore, the care of rice production needs to be considered. In addition to the lack of nutrients in water and soil in decreasing rice production, plant diseases also need to be considered. Some types of diseases that often attack rice plants include bacterial leaf blight, brown spots, and left smut. Therefore, there is knowledge of prevention efforts or early treatment before the disease attacks rice plants more widely. The efficacy of technology can be used in solving this problem, we can take advantage of artificial intelligence in it. Artificial intelligence is implemented for the detection of types of diseases in rice plants using image images on the leaves of rice plants. If the disease in rice plants can be detected, it will make it easier for rice plant farmers to overcome the disease. The ANN (Artificial neural network) algorithm can be used in this problem from the results of research on identifying the type of rice disease using the algorithm obtained an accuracy of 83%. This shows the ability of artificial intelligence in disease identification can help farmers detect types of diseases.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"123 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893804","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 : 2024-02-01DOI: 10.55529/jaimlnn.42.48.54
Ayush Kumar Ojha
This research explores the psychological impact of artificial intelligence (AI) on individuals, aiming to understand and analyze human responses and adaptations in the context of advancing AI technologies. Examining the intersection of psychology and AI, our study delves into the cognitive, emotional, and behavioral implications that arise as AI systems become integrated into various aspects of daily life. Through empirical investigations and comprehensive literature reviews, we aim to elucidate the evolving dynamics of human-AI interaction, shedding light on both positive and potentially challenging psychological outcomes. The findings contribute to a deeper understanding of the intricate relationship between humans and AI, providing valuable insights for developers, policymakers, and mental health professionals as society navigates the transformative landscape of technological integration.
{"title":"Psychological Impact of AI: Understanding Human Responses and Adaptations","authors":"Ayush Kumar Ojha","doi":"10.55529/jaimlnn.42.48.54","DOIUrl":"https://doi.org/10.55529/jaimlnn.42.48.54","url":null,"abstract":"This research explores the psychological impact of artificial intelligence (AI) on individuals, aiming to understand and analyze human responses and adaptations in the context of advancing AI technologies. Examining the intersection of psychology and AI, our study delves into the cognitive, emotional, and behavioral implications that arise as AI systems become integrated into various aspects of daily life. Through empirical investigations and comprehensive literature reviews, we aim to elucidate the evolving dynamics of human-AI interaction, shedding light on both positive and potentially challenging psychological outcomes. The findings contribute to a deeper understanding of the intricate relationship between humans and AI, providing valuable insights for developers, policymakers, and mental health professionals as society navigates the transformative landscape of technological integration.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"9 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896904","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}
The historical narrative of the fish trade is well-document in various sources. However, the concerning prevalence of fish traders vending spoiled fish poses a significant threat to human health, prompting specific research inquiries. The study aimed to address key questions: What quality of healthy fish do traders sell? How effective are their fish storage methods? What's the duration between fish purchase and consumer access? The study objectives were devised to uncover a actual condition of the fish on sale, assess storage practices, and determine the selling timeline. To achieve these aims, the study employed the EfficientNetB1 machine learning model, chosen for its simplicity and high accuracy. Five fish shops and traders from wards 1,2,3 and 4 in Damauli, the primary city of Vyas Municipality in Nepal, were selected for investigation. Results from five main city shops in Damauli revealed that only 26% of the fish were deemed healthy, while a concerning 74% were identified as rotten. Similarly, within the sample, 44% of the fish were healthy, while 56% were spoiled. This study unveiled that fish were being sold even up to 15 days post-purchase, employing ice packs, refrigeration, and potentially chemicals for storage. These findings highlight the urgent need for ongoing monitoring by relevant stakeholders and local government entities to address this issue effectively.
{"title":"Detection of Freshness of Fish using Machine Learning Techniques on Vyas Municipality, Nepal","authors":"","doi":"10.55529/ijitc.42.18.34","DOIUrl":"https://doi.org/10.55529/ijitc.42.18.34","url":null,"abstract":"The historical narrative of the fish trade is well-document in various sources. However, the concerning prevalence of fish traders vending spoiled fish poses a significant threat to human health, prompting specific research inquiries. The study aimed to address key questions: What quality of healthy fish do traders sell? How effective are their fish storage methods? What's the duration between fish purchase and consumer access? The study objectives were devised to uncover a actual condition of the fish on sale, assess storage practices, and determine the selling timeline. To achieve these aims, the study employed the EfficientNetB1 machine learning model, chosen for its simplicity and high accuracy. Five fish shops and traders from wards 1,2,3 and 4 in Damauli, the primary city of Vyas Municipality in Nepal, were selected for investigation. Results from five main city shops in Damauli revealed that only 26% of the fish were deemed healthy, while a concerning 74% were identified as rotten. Similarly, within the sample, 44% of the fish were healthy, while 56% were spoiled. This study unveiled that fish were being sold even up to 15 days post-purchase, employing ice packs, refrigeration, and potentially chemicals for storage. These findings highlight the urgent need for ongoing monitoring by relevant stakeholders and local government entities to address this issue effectively.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"117 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896949","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}
Deepshikha Aggarwal, Deepti Sharma, Archana B. Saxena
Artificial intelligence (AI) has revolutionized the online shopping experience for consumers. With AI-powered technologies, businesses can offer personalized recommendations based on consumers' browsing and purchase history. This creates a more tailored and convenient shopping experience, saving consumers time and effort. Additionally, AI can assist in fraud detection and prevention, ensuring secure transactions and building trust with customers. Moreover, AI chatbots are increasingly being used to provide instant and accurate customer support, answering queries and resolving issues promptly. As technology continues to advance, AI will play an even more significant role in enhancing the online shopping experience.AI can analyze vast amounts of data and identify patterns, enabling businesses to optimize their inventory management and supply chain processes. By predicting demand and optimizing product availability, AI helps reduce stock outs and overstocks, leading to increased customer satisfaction. AI-powered virtual try-on technology is also gaining popularity, allowing consumers to virtually try on clothing, accessories, and even makeup before making a purchase. This helps them make more informed buying decisions and reduces the likelihood of returns. Overall, AI is transforming the online shopping landscape by improving personalization, security, customer support, and product discovery, making the experience more enjoyable and efficient for consumers.
{"title":"Enhancing the Online Shopping Experience of Consumers through Artificial Intelligence","authors":"Deepshikha Aggarwal, Deepti Sharma, Archana B. Saxena","doi":"10.55529/ijitc.42.1.5","DOIUrl":"https://doi.org/10.55529/ijitc.42.1.5","url":null,"abstract":"Artificial intelligence (AI) has revolutionized the online shopping experience for consumers. With AI-powered technologies, businesses can offer personalized recommendations based on consumers' browsing and purchase history. This creates a more tailored and convenient shopping experience, saving consumers time and effort. Additionally, AI can assist in fraud detection and prevention, ensuring secure transactions and building trust with customers. Moreover, AI chatbots are increasingly being used to provide instant and accurate customer support, answering queries and resolving issues promptly. As technology continues to advance, AI will play an even more significant role in enhancing the online shopping experience.AI can analyze vast amounts of data and identify patterns, enabling businesses to optimize their inventory management and supply chain processes. By predicting demand and optimizing product availability, AI helps reduce stock outs and overstocks, leading to increased customer satisfaction. AI-powered virtual try-on technology is also gaining popularity, allowing consumers to virtually try on clothing, accessories, and even makeup before making a purchase. This helps them make more informed buying decisions and reduces the likelihood of returns. Overall, AI is transforming the online shopping landscape by improving personalization, security, customer support, and product discovery, making the experience more enjoyable and efficient for consumers.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"85 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897092","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}
Rice is the name of the type of plant that is needed by humans in the world. The plant is used as the main source of energy by Most people in the world, especially on the Asian continent. The importance of rice plants makes rice widely planted in various regions. Most humans use rice as a staple crop. Therefore, rice production needs to be considered to meet the need for enough food for most people in the world. The main thing that needs to be considered in maximizing rice production is that when guarding rice plants, many factors that inhibit rice plants can be the cause of food crises in various regions. Therefore, the care of rice production needs to be considered. In addition to the lack of nutrients in water and soil in decreasing rice production, plant diseases also need to be considered. Some types of diseases that often attack rice plants include bacterial leaf blight, brown spots, and left smut. Therefore, there is knowledge of prevention efforts or early treatment before the disease attacks rice plants more widely. The efficacy of technology can be used in solving this problem, we can take advantage of artificial intelligence in it. Artificial intelligence is implemented for the detection of types of diseases in rice plants using image images on the leaves of rice plants. If the disease in rice plants can be detected, it will make it easier for rice plant farmers to overcome the disease. The ANN (Artificial neural network) algorithm can be used in this problem from the results of research on identifying the type of rice disease using the algorithm obtained an accuracy of 83%. This shows the ability of artificial intelligence in disease identification can help farmers detect types of diseases.
{"title":"Utilization of Artificial Neural Network in Rice Plant Disease Classification Using Leaf Image","authors":"Nandi Sunandar, Joko Sutopo","doi":"10.55529/ijrise.42.1.10","DOIUrl":"https://doi.org/10.55529/ijrise.42.1.10","url":null,"abstract":"Rice is the name of the type of plant that is needed by humans in the world. The plant is used as the main source of energy by Most people in the world, especially on the Asian continent. The importance of rice plants makes rice widely planted in various regions. Most humans use rice as a staple crop. Therefore, rice production needs to be considered to meet the need for enough food for most people in the world. The main thing that needs to be considered in maximizing rice production is that when guarding rice plants, many factors that inhibit rice plants can be the cause of food crises in various regions. Therefore, the care of rice production needs to be considered. In addition to the lack of nutrients in water and soil in decreasing rice production, plant diseases also need to be considered. Some types of diseases that often attack rice plants include bacterial leaf blight, brown spots, and left smut. Therefore, there is knowledge of prevention efforts or early treatment before the disease attacks rice plants more widely. The efficacy of technology can be used in solving this problem, we can take advantage of artificial intelligence in it. Artificial intelligence is implemented for the detection of types of diseases in rice plants using image images on the leaves of rice plants. If the disease in rice plants can be detected, it will make it easier for rice plant farmers to overcome the disease. The ANN (Artificial neural network) algorithm can be used in this problem from the results of research on identifying the type of rice disease using the algorithm obtained an accuracy of 83%. This shows the ability of artificial intelligence in disease identification can help farmers detect types of diseases.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"32 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897259","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 : 2024-02-01DOI: 10.55529/ijrise.42.11.18
Aseel Nadhum Kadhum
With the rapid expansion of wireless networks and mobile computing applications, the quality of service (QoS) of mobile ad hoc networks (MANETs) has garnered growing attention. Ensuring QoS in a MANET system requires careful consideration of security issues. Attacks on a QoS distortion system without the protection of a security mechanism might result in subpar QoS performance, interference with resource use, or even failure of QoS provisioning. Traditional security measures cannot be applied because to the characteristics of MANET, which include limited processing and communication power and diversity of static topology. As a result, new security technologies are unavoidable. Nevertheless, not much research has been done on this subject. QoS and MANET system security are covered in this article. Consequently, the goal of this research is to create techniques for routinely evaluating security design reviews in order to make sure that all vulnerabilities, including security vulnerabilities, have been found, fixed, and their cause explained. Determine the system's fundamental security and protection needs by analyzing and determining its requirements. We create a network model using GloMoSim, specify node locations, communication features, and technology, and see if there are any vulnerabilities that could pose a security risk.
{"title":"Improved Digital Security Applications for Smart Card","authors":"Aseel Nadhum Kadhum","doi":"10.55529/ijrise.42.11.18","DOIUrl":"https://doi.org/10.55529/ijrise.42.11.18","url":null,"abstract":"With the rapid expansion of wireless networks and mobile computing applications, the quality of service (QoS) of mobile ad hoc networks (MANETs) has garnered growing attention. Ensuring QoS in a MANET system requires careful consideration of security issues. Attacks on a QoS distortion system without the protection of a security mechanism might result in subpar QoS performance, interference with resource use, or even failure of QoS provisioning. Traditional security measures cannot be applied because to the characteristics of MANET, which include limited processing and communication power and diversity of static topology. As a result, new security technologies are unavoidable. Nevertheless, not much research has been done on this subject. QoS and MANET system security are covered in this article. Consequently, the goal of this research is to create techniques for routinely evaluating security design reviews in order to make sure that all vulnerabilities, including security vulnerabilities, have been found, fixed, and their cause explained. Determine the system's fundamental security and protection needs by analyzing and determining its requirements. We create a network model using GloMoSim, specify node locations, communication features, and technology, and see if there are any vulnerabilities that could pose a security risk.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"45 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897649","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 : 2024-02-01DOI: 10.55529/jaimlnn.42.48.54
Ayush Kumar Ojha
This research explores the psychological impact of artificial intelligence (AI) on individuals, aiming to understand and analyze human responses and adaptations in the context of advancing AI technologies. Examining the intersection of psychology and AI, our study delves into the cognitive, emotional, and behavioral implications that arise as AI systems become integrated into various aspects of daily life. Through empirical investigations and comprehensive literature reviews, we aim to elucidate the evolving dynamics of human-AI interaction, shedding light on both positive and potentially challenging psychological outcomes. The findings contribute to a deeper understanding of the intricate relationship between humans and AI, providing valuable insights for developers, policymakers, and mental health professionals as society navigates the transformative landscape of technological integration.
{"title":"Psychological Impact of AI: Understanding Human Responses and Adaptations","authors":"Ayush Kumar Ojha","doi":"10.55529/jaimlnn.42.48.54","DOIUrl":"https://doi.org/10.55529/jaimlnn.42.48.54","url":null,"abstract":"This research explores the psychological impact of artificial intelligence (AI) on individuals, aiming to understand and analyze human responses and adaptations in the context of advancing AI technologies. Examining the intersection of psychology and AI, our study delves into the cognitive, emotional, and behavioral implications that arise as AI systems become integrated into various aspects of daily life. Through empirical investigations and comprehensive literature reviews, we aim to elucidate the evolving dynamics of human-AI interaction, shedding light on both positive and potentially challenging psychological outcomes. The findings contribute to a deeper understanding of the intricate relationship between humans and AI, providing valuable insights for developers, policymakers, and mental health professionals as society navigates the transformative landscape of technological integration.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893732","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}
The increasing occurrence of Android devices, coupled with their get entry to to touchy and personal information, has made them a high goal for malware developers. The open-supply nature of the Android platform has contributed to the developing vulnerability of malware assaults. presently, Android malware (AM) analysis strategies may be labeled into foremost categories: static evaluation and dynamic evaluation. These techniques are employed to analyze and understand the behavior of AM to mitigate its impact. This research explores the performance of DL model architectures, such as CNN-GRU, as well as traditional ML algorithms including SVM, Random Forest (RF), and decision tree (DT). The DT model achieves the highest accuracy (ACC) of 0.93, followed by RF (0.89), CNN-GRU (0.91), and SVM (0.90). These findings contribute valuable insights for the development of effective malware detection systems, emphasizing the suitability and effectiveness of the examined models in identifying AM.
安卓设备的使用率越来越高，再加上它们可以获取敏感信息和个人信息，使其成为恶意软件开发者的目标。目前，安卓恶意软件（AM）分析策略可分为几大类：静态评估和动态评估。这些技术用于分析和了解 AM 的行为，以减轻其影响。本研究探讨了 DL 模型架构（如 CNN-GRU）以及传统 ML 算法（包括 SVM、随机森林 (RF) 和决策树 (DT)）的性能。DT 模型的准确率（ACC）最高，达到 0.93，其次是 RF（0.89）、CNN-GRU（0.91）和 SVM（0.90）。这些发现为开发有效的恶意软件检测系统提供了宝贵的见解，强调了所研究模型在识别 AM 方面的适用性和有效性。
{"title":"Exploring the Effectiveness of Machine and Deep Learning Techniques for Android Malware Detection","authors":"Khalid Murad Abdullah, Ahmed Adnan Hadi","doi":"10.55529/jipirs.42.1.10","DOIUrl":"https://doi.org/10.55529/jipirs.42.1.10","url":null,"abstract":"The increasing occurrence of Android devices, coupled with their get entry to to touchy and personal information, has made them a high goal for malware developers. The open-supply nature of the Android platform has contributed to the developing vulnerability of malware assaults. presently, Android malware (AM) analysis strategies may be labeled into foremost categories: static evaluation and dynamic evaluation. These techniques are employed to analyze and understand the behavior of AM to mitigate its impact. This research explores the performance of DL model architectures, such as CNN-GRU, as well as traditional ML algorithms including SVM, Random Forest (RF), and decision tree (DT). The DT model achieves the highest accuracy (ACC) of 0.93, followed by RF (0.89), CNN-GRU (0.91), and SVM (0.90). These findings contribute valuable insights for the development of effective malware detection systems, emphasizing the suitability and effectiveness of the examined models in identifying AM.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"21 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893958","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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