Pub Date : 2024-02-01DOI: 10.55529/jaimlnn.42.22.33
Ebisa Kejela Melka
This project focuses on the analyzing different materials for torsion bar suspension system for Tank T-55 for optimizing its performance for cross country mobility and ride comfort. This suspension system is aimed to improve wheel travel and angle of twist on all terrain conditions from rough to flat surfaces. The different materials studied are carbon steel and alloy steel for their suitability as torsion bar and proposed de-sign is accomplished through the material selection and analytical calculation with analysis for shear stress, total deformation and strain. alloy steel is considered as alternative material for torsion bar based on the result of its good strength in shear stress and store maximum energy in the case of strain energy.
{"title":"Material Selection and Optimization of Torsion Bar Suspension for Military Vehicle in Case of Tank T-55","authors":"Ebisa Kejela Melka","doi":"10.55529/jaimlnn.42.22.33","DOIUrl":"https://doi.org/10.55529/jaimlnn.42.22.33","url":null,"abstract":"This project focuses on the analyzing different materials for torsion bar suspension system for Tank T-55 for optimizing its performance for cross country mobility and ride comfort. This suspension system is aimed to improve wheel travel and angle of twist on all terrain conditions from rough to flat surfaces. The different materials studied are carbon steel and alloy steel for their suitability as torsion bar and proposed de-sign is accomplished through the material selection and analytical calculation with analysis for shear stress, total deformation and strain. alloy steel is considered as alternative material for torsion bar based on the result of its good strength in shear stress and store maximum energy in the case of strain energy.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"90 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893966","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.42.34.47
Nimay Seth
People's health and well-being are not given priority in the technological and Internet-savvy world we live in. People are becoming worse because they don't regularly attend the hospital for checkups due to job and unanticipated events. Most people nowadays suffer from one or more chronic illnesses, such as diabetes, hypothyroidism, heart disease, breast cancer, and dermatology. According to the World Health Organization (WHO), these chronic illnesses account for half of all fatalities in most nations and are the main cause of premature mortality. Patients who are identified early on potentially have their condition stop progressing. Many dispersed studies clearly demonstrated that conventional approaches to diagnosing chronic illnesses are prone to prejudice and heterogeneity among physicians, making it difficult to promptly and precisely diagnose problems. Still, Despite the availability of up-to-date information and a variety of machine learning-based methods, there have been enormous published efforts demonstrating that machine learning (ML)/deep learning (DL) based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. There are many machine learning-based techniques and current knowledge available, however despite this, a great deal of published research has shown that machine learning/deep learning based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. In order to tackle this problem, this work uses the UCI/KAGGLE ML/DL disease dataset to evaluate various ML/DL procedures and explores how different machine learning algorithms forecast chronic diseases. Accuracy and confusion matrix are used to verify the results. In order to help inexperienced researchers comprehend the disease prediction function of ML/DL-based techniques and determine the direction of Upcoming research, this study also discusses the advantages and disadvantages of accessible disease prediction schemes.
{"title":"A Predictive Study of Machine Learning and Deep Learning Procedures Over Chronic Disease Datasets","authors":"Nimay Seth","doi":"10.55529/jaimlnn.42.34.47","DOIUrl":"https://doi.org/10.55529/jaimlnn.42.34.47","url":null,"abstract":"People's health and well-being are not given priority in the technological and Internet-savvy world we live in. People are becoming worse because they don't regularly attend the hospital for checkups due to job and unanticipated events. Most people nowadays suffer from one or more chronic illnesses, such as diabetes, hypothyroidism, heart disease, breast cancer, and dermatology. According to the World Health Organization (WHO), these chronic illnesses account for half of all fatalities in most nations and are the main cause of premature mortality. Patients who are identified early on potentially have their condition stop progressing. Many dispersed studies clearly demonstrated that conventional approaches to diagnosing chronic illnesses are prone to prejudice and heterogeneity among physicians, making it difficult to promptly and precisely diagnose problems. Still, Despite the availability of up-to-date information and a variety of machine learning-based methods, there have been enormous published efforts demonstrating that machine learning (ML)/deep learning (DL) based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. There are many machine learning-based techniques and current knowledge available, however despite this, a great deal of published research has shown that machine learning/deep learning based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. In order to tackle this problem, this work uses the UCI/KAGGLE ML/DL disease dataset to evaluate various ML/DL procedures and explores how different machine learning algorithms forecast chronic diseases. Accuracy and confusion matrix are used to verify the results. In order to help inexperienced researchers comprehend the disease prediction function of ML/DL-based techniques and determine the direction of Upcoming research, this study also discusses the advantages and disadvantages of accessible disease prediction schemes.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"132 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894168","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.42.22.33
Ebisa Kejela Melka
This project focuses on the analyzing different materials for torsion bar suspension system for Tank T-55 for optimizing its performance for cross country mobility and ride comfort. This suspension system is aimed to improve wheel travel and angle of twist on all terrain conditions from rough to flat surfaces. The different materials studied are carbon steel and alloy steel for their suitability as torsion bar and proposed de-sign is accomplished through the material selection and analytical calculation with analysis for shear stress, total deformation and strain. alloy steel is considered as alternative material for torsion bar based on the result of its good strength in shear stress and store maximum energy in the case of strain energy.
{"title":"Material Selection and Optimization of Torsion Bar Suspension for Military Vehicle in Case of Tank T-55","authors":"Ebisa Kejela Melka","doi":"10.55529/jaimlnn.42.22.33","DOIUrl":"https://doi.org/10.55529/jaimlnn.42.22.33","url":null,"abstract":"This project focuses on the analyzing different materials for torsion bar suspension system for Tank T-55 for optimizing its performance for cross country mobility and ride comfort. This suspension system is aimed to improve wheel travel and angle of twist on all terrain conditions from rough to flat surfaces. The different materials studied are carbon steel and alloy steel for their suitability as torsion bar and proposed de-sign is accomplished through the material selection and analytical calculation with analysis for shear stress, total deformation and strain. alloy steel is considered as alternative material for torsion bar based on the result of its good strength in shear stress and store maximum energy in the case of strain energy.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"57 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897280","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.42.19.26
Deepti Sharma, Deepshikha Aggarwal, D. A. B. Saxena
After pandemic, OTT platforms are the most common platform to provide entertainment to users. Among all platforms, Netflix has become most the popular one. Data visualization of Netflix data can provide valuable insights and benefits in many ways like understanding viewer preferences, content optimization, personalized recommendation, quality and content performance evaluation, fraud detection to name a few. This research provides exploratory data visualization and provide a content based recommendation system on Netflix data as in real world applications, company uses these recommendation system algorithms to determine which system are better to improve users’ engagement of the platform.
{"title":"Content Based Recommendation System on Netflix Data","authors":"Deepti Sharma, Deepshikha Aggarwal, D. A. B. Saxena","doi":"10.55529/ijrise.42.19.26","DOIUrl":"https://doi.org/10.55529/ijrise.42.19.26","url":null,"abstract":"After pandemic, OTT platforms are the most common platform to provide entertainment to users. Among all platforms, Netflix has become most the popular one. Data visualization of Netflix data can provide valuable insights and benefits in many ways like understanding viewer preferences, content optimization, personalized recommendation, quality and content performance evaluation, fraud detection to name a few. This research provides exploratory data visualization and provide a content based recommendation system on Netflix data as in real world applications, company uses these recommendation system algorithms to determine which system are better to improve users’ engagement of the platform.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897145","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}
Artificial intelligence (AI) and machine learning (ML) are essential for processing vast datasets and forecasting unknown events, offering innovative solutions to IoT security challenges. Recurrent neural networks (RNNs) have extended the predictive capacity of traditional neural networks, particularly in forecasting sequential events. With the increasing frequency of system attacks, the integration of machine learning into intrusion detection systems (IDS) is vital to identify and report potential threats, thereby safeguarding IoT infrastructure against destructive attacks
{"title":"Intrusion Detection in IOT Networks using Machine Learning Techniques","authors":"","doi":"10.55529/jecnam.42.1.18","DOIUrl":"https://doi.org/10.55529/jecnam.42.1.18","url":null,"abstract":"Artificial intelligence (AI) and machine learning (ML) are essential for processing vast datasets and forecasting unknown events, offering innovative solutions to IoT security challenges. Recurrent neural networks (RNNs) have extended the predictive capacity of traditional neural networks, particularly in forecasting sequential events. With the increasing frequency of system attacks, the integration of machine learning into intrusion detection systems (IDS) is vital to identify and report potential threats, thereby safeguarding IoT infrastructure against destructive attacks","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897436","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":"17 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893912","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}
India is known for its farming, which has been crucial for its economy. But recently, farming hasn't been doing as well because of unpredictable rain. When the monsoon doesn't behave as expected, it can seriously damage crops, causing farmers to lose a lot of their produce. Unseasonal rains can have significant effects on Post-Harvesting Activities and the quality of harvested agricultural products such as fruit and vegetable crops during different stages of growth and harvesting. Post-harvesting steps are crucial to preserving the quality and ensuring the safety of the harvested products before they reach consumers. After harvesting, drying the crops is essential for storing grains. However, around 70% of farmers use the old method of sun-drying, which becomes a problem when unexpected rain falls. Sometimes, farmers even get hurt or lose their crops due to thunderstorms and lightning. To mitigate these challenges and losses, we have proposed a project aimed at providing an innovative, cost-effective solution: a proper Automated Rain Shed System, which opens and closes to protect crops or plants from unwanted rains and enables remote monitoring of Automated Rain Shed Operations.
{"title":"Crop Canopy: Empowering Crop Resilience with IoT-Driven Rain Shed Solution","authors":"Kokkula Yashmi, Alugani Harshitha, Dondeti Sumagna, Eppala Rukshitha","doi":"10.55529/ijrise.42.27.39","DOIUrl":"https://doi.org/10.55529/ijrise.42.27.39","url":null,"abstract":"India is known for its farming, which has been crucial for its economy. But recently, farming hasn't been doing as well because of unpredictable rain. When the monsoon doesn't behave as expected, it can seriously damage crops, causing farmers to lose a lot of their produce. Unseasonal rains can have significant effects on Post-Harvesting Activities and the quality of harvested agricultural products such as fruit and vegetable crops during different stages of growth and harvesting. Post-harvesting steps are crucial to preserving the quality and ensuring the safety of the harvested products before they reach consumers. After harvesting, drying the crops is essential for storing grains. However, around 70% of farmers use the old method of sun-drying, which becomes a problem when unexpected rain falls. Sometimes, farmers even get hurt or lose their crops due to thunderstorms and lightning. To mitigate these challenges and losses, we have proposed a project aimed at providing an innovative, cost-effective solution: a proper Automated Rain Shed System, which opens and closes to protect crops or plants from unwanted rains and enables remote monitoring of Automated Rain Shed Operations.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"17 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139896900","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.42.34.47
Nimay Seth
People's health and well-being are not given priority in the technological and Internet-savvy world we live in. People are becoming worse because they don't regularly attend the hospital for checkups due to job and unanticipated events. Most people nowadays suffer from one or more chronic illnesses, such as diabetes, hypothyroidism, heart disease, breast cancer, and dermatology. According to the World Health Organization (WHO), these chronic illnesses account for half of all fatalities in most nations and are the main cause of premature mortality. Patients who are identified early on potentially have their condition stop progressing. Many dispersed studies clearly demonstrated that conventional approaches to diagnosing chronic illnesses are prone to prejudice and heterogeneity among physicians, making it difficult to promptly and precisely diagnose problems. Still, Despite the availability of up-to-date information and a variety of machine learning-based methods, there have been enormous published efforts demonstrating that machine learning (ML)/deep learning (DL) based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. There are many machine learning-based techniques and current knowledge available, however despite this, a great deal of published research has shown that machine learning/deep learning based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. In order to tackle this problem, this work uses the UCI/KAGGLE ML/DL disease dataset to evaluate various ML/DL procedures and explores how different machine learning algorithms forecast chronic diseases. Accuracy and confusion matrix are used to verify the results. In order to help inexperienced researchers comprehend the disease prediction function of ML/DL-based techniques and determine the direction of Upcoming research, this study also discusses the advantages and disadvantages of accessible disease prediction schemes.
{"title":"A Predictive Study of Machine Learning and Deep Learning Procedures Over Chronic Disease Datasets","authors":"Nimay Seth","doi":"10.55529/jaimlnn.42.34.47","DOIUrl":"https://doi.org/10.55529/jaimlnn.42.34.47","url":null,"abstract":"People's health and well-being are not given priority in the technological and Internet-savvy world we live in. People are becoming worse because they don't regularly attend the hospital for checkups due to job and unanticipated events. Most people nowadays suffer from one or more chronic illnesses, such as diabetes, hypothyroidism, heart disease, breast cancer, and dermatology. According to the World Health Organization (WHO), these chronic illnesses account for half of all fatalities in most nations and are the main cause of premature mortality. Patients who are identified early on potentially have their condition stop progressing. Many dispersed studies clearly demonstrated that conventional approaches to diagnosing chronic illnesses are prone to prejudice and heterogeneity among physicians, making it difficult to promptly and precisely diagnose problems. Still, Despite the availability of up-to-date information and a variety of machine learning-based methods, there have been enormous published efforts demonstrating that machine learning (ML)/deep learning (DL) based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. There are many machine learning-based techniques and current knowledge available, however despite this, a great deal of published research has shown that machine learning/deep learning based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. In order to tackle this problem, this work uses the UCI/KAGGLE ML/DL disease dataset to evaluate various ML/DL procedures and explores how different machine learning algorithms forecast chronic diseases. Accuracy and confusion matrix are used to verify the results. In order to help inexperienced researchers comprehend the disease prediction function of ML/DL-based techniques and determine the direction of Upcoming research, this study also discusses the advantages and disadvantages of accessible disease prediction schemes.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"2014 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139897135","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.42.14.21
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":"66 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893827","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":"31 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894008","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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