{"title":"Enhancing Privacy in Social Media Image Sharing Using Advanced Encryption Technique","authors":"","doi":"10.46632/daai/4/2/1","DOIUrl":"https://doi.org/10.46632/daai/4/2/1","url":null,"abstract":"","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"9 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230659","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}
: In order to automatically create evocative descriptions for photos, the Image Caption Generator Project introduces a novel blend of computer vision and natural language processing approaches. Convolutional Neural Networks (CNNs) are used by the system to process raw photos while utilizing cutting-edge deep learning models to recognize complicated patterns and objects. This visual comprehension is seamlessly combined with cutting-edge Natural Language Processing (NLP) algorithms, using attention processes and Sequence-to-Sequence models to produce captions that are both linguistically and contextually coherent. The project places a strong emphasis on the user experience by giving users a simple interface via which they can upload photographs and instantly receive pertinent captions. The reliability and correctness of generated captions are guaranteed by stringent evaluation measures like BLEU and METEOR. The system must be trained on a variety of datasets to ensure ethical considerations, minimize biases, and promote inclusive outcomes. Potential applications of the project include search engine content metadata enrichment, accessibility tools for the blind, and boosting user engagement on social media platforms.
{"title":"Image Caption Generator Using Deep Learning","authors":"Prof.S. Sankareswari, Miss.Bibi, Zainab Dongarkar, Miss.Heena Dongarkar, Miss.Simran Sarang, Miss.Madhura Valke, Student","doi":"10.46632/daai/4/2/5","DOIUrl":"https://doi.org/10.46632/daai/4/2/5","url":null,"abstract":": In order to automatically create evocative descriptions for photos, the Image Caption Generator Project introduces a novel blend of computer vision and natural language processing approaches. Convolutional Neural Networks (CNNs) are used by the system to process raw photos while utilizing cutting-edge deep learning models to recognize complicated patterns and objects. This visual comprehension is seamlessly combined with cutting-edge Natural Language Processing (NLP) algorithms, using attention processes and Sequence-to-Sequence models to produce captions that are both linguistically and contextually coherent. The project places a strong emphasis on the user experience by giving users a simple interface via which they can upload photographs and instantly receive pertinent captions. The reliability and correctness of generated captions are guaranteed by stringent evaluation measures like BLEU and METEOR. The system must be trained on a variety of datasets to ensure ethical considerations, minimize biases, and promote inclusive outcomes. Potential applications of the project include search engine content metadata enrichment, accessibility tools for the blind, and boosting user engagement on social media platforms.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"6 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141281090","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}
Research projects are necessary for conducting research or generating work products. Most of the work however, focuses more on the aspects of research within a domain instead of moving towards interdisciplinary work. In this chapter, the author proposes to develop research projects in perspective of SE and NLP. The future scope is also presented herein.
研究项目是开展研究或产生工作成果的必要条件。然而，大多数研究工作更侧重于某一领域内的研究方面，而不是转向跨学科工作。在本章中，作者建议从 SE 和 NLP 的角度开发研究项目。本章还介绍了未来的研究范围。
{"title":"Developing Research Projects in SE and NLP","authors":"","doi":"10.46632/daai/4/1/2","DOIUrl":"https://doi.org/10.46632/daai/4/1/2","url":null,"abstract":"Research projects are necessary for conducting research or generating work products. Most of the work however, focuses more on the aspects of research within a domain instead of moving towards interdisciplinary work. In this chapter, the author proposes to develop research projects in perspective of SE and NLP. The future scope is also presented herein.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"216 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848819","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}
Research projects are necessary for conducting research or generating work products. Most of the work however, focuses more on the aspects of research within a domain instead of moving towards interdisciplinary work. In this chapter, the author proposes to develop research projects in perspective of SE and NLP. The future scope is also presented herein.
研究项目是开展研究或产生工作成果的必要条件。然而，大多数研究工作更侧重于某一领域内的研究方面，而不是转向跨学科工作。在本章中，作者建议从 SE 和 NLP 的角度开发研究项目。本章还介绍了未来的研究范围。
{"title":"Developing Research Projects in SE and NLP","authors":"","doi":"10.46632/daai/4/1/2","DOIUrl":"https://doi.org/10.46632/daai/4/1/2","url":null,"abstract":"Research projects are necessary for conducting research or generating work products. Most of the work however, focuses more on the aspects of research within a domain instead of moving towards interdisciplinary work. In this chapter, the author proposes to develop research projects in perspective of SE and NLP. The future scope is also presented herein.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":" 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788844","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 pharmacy industry, a crucial pillar of the healthcare sector, encompasses the discovery, development, production, distribution, and sale of pharmaceutical drugs and medications. With an intricate interplay of scientific innovation, medical expertise, and commercial activities, this industry plays an indispensable role in safeguarding and improving human health. From the inception of groundbreaking drugs to their widespread distribution, the pharmacy industry integrates various stakeholders, including pharmaceutical companies, researchers, healthcare professionals, regulators, and consumers. It strives to address a wide spectrum of health conditions, from acute ailments to chronic diseases, by developing innovative treatments, generic medicines, and over-the-counter drugs. The pharmacy industry's evolution has been marked by technological advancements, research breakthroughs, and regulatory frameworks to ensure drug safety and efficacy. As the global population continues to grow and age, the industry faces the challenges of maintaining affordability, accessibility, and quality of medications. Furthermore, the pharmacy industry is a catalyst for economic growth, creating jobs, fostering research collaborations, and contributing to national and international healthcare systems. Its multifaceted nature, ranging from drug research to patient care, underscores its significance in the broader landscape of healthcare and public well-being. Research within the pharmacy industry holds immense significance due to its pivotal role in advancing medical knowledge and improving patient outcomes. Pharmaceutical research drives the development of new medications, innovative therapies, and treatment protocols, enhancing the efficacy and safety of drugs. It also uncovers insights into disease mechanisms, fostering a deeper understanding of health conditions. Furthermore, research guides regulatory decisions, ensuring drugs' quality, and promotes evidence-based medical practices. Through ongoing investigation, the pharmacy industry continually evolves, addressing emerging health challenges, optimizing drug utilization, and ultimately contributing to the overall enhancement of global healthcare standards. MOORA (Multi-Objective Optimization by Ratio Analysis) is a decision-making method used to evaluate and prioritize alternatives based on multiple conflicting criteria. It involves comparing alternatives' performance ratios against reference alternatives, considering both benefits and drawbacks. By assigning weights to criteria, MOORA quantifies their importance and ranks alternatives accordingly. This technique assists in complex decision scenarios where various factors must be balanced. MOORA's systematic approach aids in reaching well-informed decisions by quantifying trade-offs and providing a structured framework for considering multiple objectives simultaneously. Product Innovation, Market Share (%), Research Investment ($ billion), Patient Satisfaction, Dr
制药业是医疗保健行业的重要支柱，包括药品和药物的发现、开发、生产、分销和销售。科学创新、医学专业知识和商业活动错综复杂地交织在一起，该行业在保障和改善人类健康方面发挥着不可或缺的作用。从开创性药物的诞生到其广泛销售，制药业整合了各利益相关方，包括制药公司、研究人员、医疗保健专业人员、监管机构和消费者。它通过开发创新疗法、非专利药品和非处方药，努力解决从急性病到慢性病的各种健康问题。药剂行业的发展以技术进步、研究突破和监管框架为标志，以确保药物的安全性和有效性。随着全球人口的不断增长和老龄化，该行业面临着保持药品的可负担性、可获得性和质量的挑战。此外，药学行业还是经济增长的催化剂，它能创造就业机会，促进研究合作，并为国家和国际医疗保健系统做出贡献。从药物研究到病人护理，药剂学的多面性凸显了它在更广泛的医疗保健和公众福祉中的重要性。药学行业内的研究工作在推动医学知识发展和改善患者治疗效果方面发挥着举足轻重的作用，因此意义重大。药学研究推动了新药、创新疗法和治疗方案的开发，提高了药物的疗效和安全性。它还能揭示疾病机理，加深对健康状况的了解。此外，研究还能指导监管决策，确保药品质量，促进循证医疗实践。通过持续的研究，药学行业不断发展，应对新出现的健康挑战，优化药物使用，最终为全面提高全球医疗保健标准做出贡献。MOORA（比率分析法多目标优化）是一种决策方法，用于根据多个相互冲突的标准对备选方案进行评估和优先排序。它包括将替代品的性能比与参考替代品进行比较，同时考虑其优点和缺点。通过给标准分配权重，MOORA 可以量化这些标准的重要性，并据此对备选方案进行排序。这种技术有助于在必须平衡各种因素的复杂决策场景中使用。MOORA 的系统方法通过量化权衡，为同时考虑多个目标提供了一个结构化框架，有助于在充分知情的情况下做出决策。产品创新、市场份额（%）、研究投资（亿美元）、患者满意度、药物疗效（%）、全球影响力（国家）。辉瑞、强生、罗氏、诺华、葛兰素史克、CVS Health、沃尔格林博姿联盟、Rite Aid。从结果可以看出，诺华排名第一，而 Rite Aid 排名最低。
{"title":"A Multi-Objective Approach Optimizing Pharmacy Industry Decisions through MOORA Method","authors":"","doi":"10.46632/daai/4/1/1","DOIUrl":"https://doi.org/10.46632/daai/4/1/1","url":null,"abstract":"The pharmacy industry, a crucial pillar of the healthcare sector, encompasses the discovery, development, production, distribution, and sale of pharmaceutical drugs and medications. With an intricate interplay of scientific innovation, medical expertise, and commercial activities, this industry plays an indispensable role in safeguarding and improving human health. From the inception of groundbreaking drugs to their widespread distribution, the pharmacy industry integrates various stakeholders, including pharmaceutical companies, researchers, healthcare professionals, regulators, and consumers. It strives to address a wide spectrum of health conditions, from acute ailments to chronic diseases, by developing innovative treatments, generic medicines, and over-the-counter drugs. The pharmacy industry's evolution has been marked by technological advancements, research breakthroughs, and regulatory frameworks to ensure drug safety and efficacy. As the global population continues to grow and age, the industry faces the challenges of maintaining affordability, accessibility, and quality of medications. Furthermore, the pharmacy industry is a catalyst for economic growth, creating jobs, fostering research collaborations, and contributing to national and international healthcare systems. Its multifaceted nature, ranging from drug research to patient care, underscores its significance in the broader landscape of healthcare and public well-being. Research within the pharmacy industry holds immense significance due to its pivotal role in advancing medical knowledge and improving patient outcomes. Pharmaceutical research drives the development of new medications, innovative therapies, and treatment protocols, enhancing the efficacy and safety of drugs. It also uncovers insights into disease mechanisms, fostering a deeper understanding of health conditions. Furthermore, research guides regulatory decisions, ensuring drugs' quality, and promotes evidence-based medical practices. Through ongoing investigation, the pharmacy industry continually evolves, addressing emerging health challenges, optimizing drug utilization, and ultimately contributing to the overall enhancement of global healthcare standards. MOORA (Multi-Objective Optimization by Ratio Analysis) is a decision-making method used to evaluate and prioritize alternatives based on multiple conflicting criteria. It involves comparing alternatives' performance ratios against reference alternatives, considering both benefits and drawbacks. By assigning weights to criteria, MOORA quantifies their importance and ranks alternatives accordingly. This technique assists in complex decision scenarios where various factors must be balanced. MOORA's systematic approach aids in reaching well-informed decisions by quantifying trade-offs and providing a structured framework for considering multiple objectives simultaneously. Product Innovation, Market Share (%), Research Investment ($ billion), Patient Satisfaction, Dr","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"48 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139599775","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}
Information and Communication Technology (ICT) plays a vital role in bolstering endeavors aimed at safeguarding the environment, and the MOORA method offers a structured approach. ICT involves the use of digital tools and technologies to manage and transmit information, enabling real-time data collection, analysis, and communication. In environmental protection, ICT aids in various ways, such as monitoring air and water quality, tracking wildlife patterns, and managing waste disposal. The MOORA method is a decision-making technique that helps prioritize alternatives based on multiple conflicting criteria. In the context of environmental protection, the MOORA method assists in selecting the most effective ICT solutions. It evaluates various ICT options by considering multiple objectives, such as efficiency, cost-effectiveness, ecological impact, and scalability. MOORA computes ratios to compare alternatives against each criterion, enabling a comprehensive assessment. By assigning weights to the criteria, stakeholders can emphasize specific factors according to their importance. For instance, when choosing between different ICT systems for waste management, the MOORA method can quantify the ecological benefits of reduced emissions, energy savings, and waste reduction against factors like implementation costs and technological feasibility. This systematic evaluation ensures that the chosen ICT solution aligns with the overall goal of environmental protection while considering practical constraints. ICT leverages advanced technologies to bolster environmental protection, and the MOORA method provides a structured approach to assess and prioritize ICT solutions. This combined approach facilitates informed decision-making, leading to the adoption of efficient and sustainable technologies that contribute to a healthier planet. The Smart Grid System (A1), E-waste Recycling Program (A2), Air Quality Monitoring Network (A3), Water Pollution Detection Sensors (A4), Green Supply Chain Management Software (A5), and Virtual Environmental Education Platform (A6) are employed as alternative solutions. These alternatives are assessed based on their ability to achieve Reduction in Environmental Impact (C1), Enhancement of Efficiency (C2), Cost Efficiency (C3), and User-Friendliness (C4).The environmental production of E-waste Recycling Program is got first rank and Smart Grid System is got lowest rank.
{"title":"Assessing the Role of Information and Communication Technology (ICT) in Safeguarding the Environment through the Application of the MOORA Method","authors":"","doi":"10.46632/daai/3/5/6","DOIUrl":"https://doi.org/10.46632/daai/3/5/6","url":null,"abstract":"Information and Communication Technology (ICT) plays a vital role in bolstering endeavors aimed at safeguarding the environment, and the MOORA method offers a structured approach. ICT involves the use of digital tools and technologies to manage and transmit information, enabling real-time data collection, analysis, and communication. In environmental protection, ICT aids in various ways, such as monitoring air and water quality, tracking wildlife patterns, and managing waste disposal. The MOORA method is a decision-making technique that helps prioritize alternatives based on multiple conflicting criteria. In the context of environmental protection, the MOORA method assists in selecting the most effective ICT solutions. It evaluates various ICT options by considering multiple objectives, such as efficiency, cost-effectiveness, ecological impact, and scalability. MOORA computes ratios to compare alternatives against each criterion, enabling a comprehensive assessment. By assigning weights to the criteria, stakeholders can emphasize specific factors according to their importance. For instance, when choosing between different ICT systems for waste management, the MOORA method can quantify the ecological benefits of reduced emissions, energy savings, and waste reduction against factors like implementation costs and technological feasibility. This systematic evaluation ensures that the chosen ICT solution aligns with the overall goal of environmental protection while considering practical constraints. ICT leverages advanced technologies to bolster environmental protection, and the MOORA method provides a structured approach to assess and prioritize ICT solutions. This combined approach facilitates informed decision-making, leading to the adoption of efficient and sustainable technologies that contribute to a healthier planet. The Smart Grid System (A1), E-waste Recycling Program (A2), Air Quality Monitoring Network (A3), Water Pollution Detection Sensors (A4), Green Supply Chain Management Software (A5), and Virtual Environmental Education Platform (A6) are employed as alternative solutions. These alternatives are assessed based on their ability to achieve Reduction in Environmental Impact (C1), Enhancement of Efficiency (C2), Cost Efficiency (C3), and User-Friendliness (C4).The environmental production of E-waste Recycling Program is got first rank and Smart Grid System is got lowest rank.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"27 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138981593","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}
SDN-Enabled IoT Networks bring about a transformative shift in conventional network models by integrating the core principles of Software-Defined Networking (SDN) into the realm of the Internet of Things (IoT). This integration empowers the agile and effective management of IoT devices, facilitating smooth connectivity, optimized distribution of resources, and flexible network setups. Through the consolidation of control and the utilization of virtualization methods, SDN-Enabled IoT Networks amplify scalability, security, and real-time responsiveness. This addresses the obstacles presented by the extensive proliferation of IoT devices. This paradigm transition heralds a fresh era of interconnectedness, where SDN assumes a central role in harmonizing the intricate interplay of IoT devices and services. The rapid expansion of Internet of Things (IoT) devices has introduced unparalleled complexities in overseeing networks and establishing connections. This compels the need for inventive strategies to effectively manage the substantial surge of IoT devices and their ever-changing connectivity prerequisites. Software-Defined Networking (SDN) emerges as a promising approach to tackle these issues by enabling the flexible management of networks and allocation of resources. This study investigates the amalgamation of SDN within the realm of IoT, aiming to streamline device connections, optimize data transmission efficiency, and accommodate adaptable network setups. Introducing an innovative weighted sum technique for resource allocation optimization, this work lays the foundation for a comprehensive framework that bolsters IoT network performance and expandability. Four different SDN implementations are examined, including the Conventional IoT Network, SDN-enabled IoT utilizing Centralized Control, SDN-enabled IoT employing Distributed Control, SDN-enabled IoT with Hierarchical Control, and SDN-enabled IoT utilizing Hybrid Control. The assessment considers various aspects such as Enhanced Scalability, Enhanced Traffic Engineering, Heightened Security, Implementation Complexity, Difficulty of Migration, and Reliance on Vendors. The Conventional IoT Network secures a moderate 3rd position with a Preference Score of 0.56030, while the SDN-enabled IoT with Centralized Control holds the 5th rank at 0.49732, despite excelling in specific domains. The SDN-enabled IoT with Distributed Control achieves the top rank with a notable Preference Score of 0.79414 due to comprehensive performance, followed by the SDN-enabled IoT with Hierarchical Control securing the 2nd spot (Preference Score: 0.57022), and the SDN-enabled IoT with Hybrid Control taking the 4th position (Preference Score: 0.51300), particularly excelling in Traffic Engineering.
{"title":"Enabling Efficient IoT Device Connectivity and Dynamic Network Management through SDN: A Weighted Sum Method Approach","authors":"","doi":"10.46632/daai/3/5/3","DOIUrl":"https://doi.org/10.46632/daai/3/5/3","url":null,"abstract":"SDN-Enabled IoT Networks bring about a transformative shift in conventional network models by integrating the core principles of Software-Defined Networking (SDN) into the realm of the Internet of Things (IoT). This integration empowers the agile and effective management of IoT devices, facilitating smooth connectivity, optimized distribution of resources, and flexible network setups. Through the consolidation of control and the utilization of virtualization methods, SDN-Enabled IoT Networks amplify scalability, security, and real-time responsiveness. This addresses the obstacles presented by the extensive proliferation of IoT devices. This paradigm transition heralds a fresh era of interconnectedness, where SDN assumes a central role in harmonizing the intricate interplay of IoT devices and services. The rapid expansion of Internet of Things (IoT) devices has introduced unparalleled complexities in overseeing networks and establishing connections. This compels the need for inventive strategies to effectively manage the substantial surge of IoT devices and their ever-changing connectivity prerequisites. Software-Defined Networking (SDN) emerges as a promising approach to tackle these issues by enabling the flexible management of networks and allocation of resources. This study investigates the amalgamation of SDN within the realm of IoT, aiming to streamline device connections, optimize data transmission efficiency, and accommodate adaptable network setups. Introducing an innovative weighted sum technique for resource allocation optimization, this work lays the foundation for a comprehensive framework that bolsters IoT network performance and expandability. Four different SDN implementations are examined, including the Conventional IoT Network, SDN-enabled IoT utilizing Centralized Control, SDN-enabled IoT employing Distributed Control, SDN-enabled IoT with Hierarchical Control, and SDN-enabled IoT utilizing Hybrid Control. The assessment considers various aspects such as Enhanced Scalability, Enhanced Traffic Engineering, Heightened Security, Implementation Complexity, Difficulty of Migration, and Reliance on Vendors. The Conventional IoT Network secures a moderate 3rd position with a Preference Score of 0.56030, while the SDN-enabled IoT with Centralized Control holds the 5th rank at 0.49732, despite excelling in specific domains. The SDN-enabled IoT with Distributed Control achieves the top rank with a notable Preference Score of 0.79414 due to comprehensive performance, followed by the SDN-enabled IoT with Hierarchical Control securing the 2nd spot (Preference Score: 0.57022), and the SDN-enabled IoT with Hybrid Control taking the 4th position (Preference Score: 0.51300), particularly excelling in Traffic Engineering.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133482640","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}
Android-based power-saving framework" that is universally recognized. However, I can provide you with information about power-saving techniques and strategies commonly used in Android development up to that point. Keep in mind that developments might have occurred after September 2021. Android devices are known for their versatility and feature-rich environment, but this can come at the cost of increased power consumption. To mitigate this issue, developers and device manufacturers have employed various power-saving techniques and frameworks. Here are some common strategies and frameworks: Doze Mode and App Standby: Android introduced Doze Mode, which helps conserve battery life by delaying background CPU and network activity when a device is idle. App Standby takes this further by putting apps into a low-power state when they aren't actively used, reducing their impact on battery life. Background Execution Limits: Android limits background execution of apps to prevent unnecessary battery drain. Apps can only run background tasks within specific restrictions, ensuring that they don't continuously consume resources. JobScheduler: This framework allows apps to schedule tasks at optimal times, which can help consolidate tasks and reduce the frequency of waking up the device, thus saving power. Battery Optimization: Android provides a battery optimization feature that allows users to prioritize apps and restrict background activity for specific apps, helping to save power. Location Services: Managing location updates efficiently can significantly impact battery life. Using lower accuracy settings or batching location updates can reduce the power consumed by location services. Wakelocks and Alarms: Developers can use wakelocks and alarms to keep the device awake for specific tasks. However, these should be used judiciously, as they can lead to increased power consumption if not managed properly. Optimized Networking: Using techniques like Volley or OkHttp for efficient network requests, and optimizing the use of background data syncing, can help reduce power consumption. Background Syncing: DEMATEL is widely accepted for analyzing the overall relationship of factors and classifying factors into cause-and-effect types. Therefore, this article considers each source as a criterion in decision-making. To deal with a mixture of conflicting evidence, the significance and level of significance of each piece of evidence can be determined using DEMATEL; however, expanding the DEMATEL method with the source theory is required for better conclusions. Screen brightness & colour scheme, CPU frequency, Network, Low power localization and Wi-Fi. Rank using the DEMATEL for Android-based power-saving framework in Screen brightness & colour scheme is got the first rank whereas is the CPU frequency is having the Lowest rank.
{"title":"Android-Based Power-Saving Framework for Mobile Devices Using the DEMATEL Method","authors":"","doi":"10.46632/daai/3/5/4","DOIUrl":"https://doi.org/10.46632/daai/3/5/4","url":null,"abstract":"Android-based power-saving framework\" that is universally recognized. However, I can provide you with information about power-saving techniques and strategies commonly used in Android development up to that point. Keep in mind that developments might have occurred after September 2021. Android devices are known for their versatility and feature-rich environment, but this can come at the cost of increased power consumption. To mitigate this issue, developers and device manufacturers have employed various power-saving techniques and frameworks. Here are some common strategies and frameworks: Doze Mode and App Standby: Android introduced Doze Mode, which helps conserve battery life by delaying background CPU and network activity when a device is idle. App Standby takes this further by putting apps into a low-power state when they aren't actively used, reducing their impact on battery life. Background Execution Limits: Android limits background execution of apps to prevent unnecessary battery drain. Apps can only run background tasks within specific restrictions, ensuring that they don't continuously consume resources. JobScheduler: This framework allows apps to schedule tasks at optimal times, which can help consolidate tasks and reduce the frequency of waking up the device, thus saving power. Battery Optimization: Android provides a battery optimization feature that allows users to prioritize apps and restrict background activity for specific apps, helping to save power. Location Services: Managing location updates efficiently can significantly impact battery life. Using lower accuracy settings or batching location updates can reduce the power consumed by location services. Wakelocks and Alarms: Developers can use wakelocks and alarms to keep the device awake for specific tasks. However, these should be used judiciously, as they can lead to increased power consumption if not managed properly. Optimized Networking: Using techniques like Volley or OkHttp for efficient network requests, and optimizing the use of background data syncing, can help reduce power consumption. Background Syncing: DEMATEL is widely accepted for analyzing the overall relationship of factors and classifying factors into cause-and-effect types. Therefore, this article considers each source as a criterion in decision-making. To deal with a mixture of conflicting evidence, the significance and level of significance of each piece of evidence can be determined using DEMATEL; however, expanding the DEMATEL method with the source theory is required for better conclusions. Screen brightness & colour scheme, CPU frequency, Network, Low power localization and Wi-Fi. Rank using the DEMATEL for Android-based power-saving framework in Screen brightness & colour scheme is got the first rank whereas is the CPU frequency is having the Lowest rank.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133209157","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 present generation of all ages is terribly facing the challenges of obesity in recent times. The people suffering from this disorder practice different diet plans for weight reduction without considering the balanced proportion of nutrients in their diet. This paper aims in highlighting the ill effects of unbalanced diet plans and proposes a machine learning (ML) model based on support vector machine to make decisions on the balanced nature of the diet. The efficiency of the proposed ML model is compared with other ML algorithms. The accuracy results of the proposed model are more convincing in comparison with other ML algorithms. The proposed ML model is applied to deterministic type of secondary data sets and this shall be extended by applying to fuzzy data sets. This research work applies the algorithms of machine learning to health-based decision-making systems
{"title":"Machine Learning Algorithms in Identifying Balanced Diet Plan for Healthy Life style","authors":"M. Nivetha, P. Pandiammal, Gandhi Ramila","doi":"10.46632/daai/3/5/2","DOIUrl":"https://doi.org/10.46632/daai/3/5/2","url":null,"abstract":"The present generation of all ages is terribly facing the challenges of obesity in recent times. The people suffering from this disorder practice different diet plans for weight reduction without considering the balanced proportion of nutrients in their diet. This paper aims in highlighting the ill effects of unbalanced diet plans and proposes a machine learning (ML) model based on support vector machine to make decisions on the balanced nature of the diet. The efficiency of the proposed ML model is compared with other ML algorithms. The accuracy results of the proposed model are more convincing in comparison with other ML algorithms. The proposed ML model is applied to deterministic type of secondary data sets and this shall be extended by applying to fuzzy data sets. This research work applies the algorithms of machine learning to health-based decision-making systems","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114358080","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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