This The DDoS attack threat is evolving, and because of this, organizations are discovering and using new modern technologies to lay the ground for more effective defensive strategies. This paper is devoted to the investigation of the most efficient methods fighting DDoS – downtime of the network, and ensuring cybersecurity on different domains. First of all, the integration of Convolutional Neural Networks (CNNs) into cybersecurity is a very promising move with respect to fighting exactly the phishing and application-layer DDoS attacks in greater details than the machine learning approaches like the LSTMs and SAEs. Another aspect of building the effective opposition against the dummy data attacks on the critical infrastructures, for example on the power systems, is creating the multi-dimensional mitigation models composed of various timely detection techniques and robust network architecture. In addition, the usage of Physically Unclonable Functions (PUFs) in network architectures provides a means of authentication as well as access control that can improve the resilience of a network against DDoS attacks. PUFs enables the blockade of unwanted packets of high volume traffic, allowing granular traffic filtration and isolation. By using hardware solutions such as Distributed-Denial-of-Service (DDoS) attack prevention, SDN-biased security frame with deep learning algorithms can improve network resilience with significant detection and response to slow-rate DDoS attacks. At last EWMA, KNN, and CUSUM as statistical methods integrated with FOG computing architectures ensure real time and effective solution for the detection and mitigation of DDoS attacks in the IoT networks, making them immune to the current as well as the continuously emerging cyber threats. Through the integration of these cutting edge methods, organizations will be able to hold their ground against cyberattacks catalyzed by DDoS menace and stay ahead of dynamic threats whenever they arise. Keywords— Cloud computing, Data threats, Data Protection, Cloud security.
{"title":"Surveying Emerging Trends in DDoS Defense","authors":"Sumith Pandey","doi":"10.55041/ijsrem34483","DOIUrl":"https://doi.org/10.55041/ijsrem34483","url":null,"abstract":"This The DDoS attack threat is evolving, and because of this, organizations are discovering and using new modern technologies to lay the ground for more effective defensive strategies. This paper is devoted to the investigation of the most efficient methods fighting DDoS – downtime of the network, and ensuring cybersecurity on different domains. First of all, the integration of Convolutional Neural Networks (CNNs) into cybersecurity is a very promising move with respect to fighting exactly the phishing and application-layer DDoS attacks in greater details than the machine learning approaches like the LSTMs and SAEs. Another aspect of building the effective opposition against the dummy data attacks on the critical infrastructures, for example on the power systems, is creating the multi-dimensional mitigation models composed of various timely detection techniques and robust network architecture. In addition, the usage of Physically Unclonable Functions (PUFs) in network architectures provides a means of authentication as well as access control that can improve the resilience of a network against DDoS attacks. PUFs enables the blockade of unwanted packets of high volume traffic, allowing granular traffic filtration and isolation. By using hardware solutions such as Distributed-Denial-of-Service (DDoS) attack prevention, SDN-biased security frame with deep learning algorithms can improve network resilience with significant detection and response to slow-rate DDoS attacks. At last EWMA, KNN, and CUSUM as statistical methods integrated with FOG computing architectures ensure real time and effective solution for the detection and mitigation of DDoS attacks in the IoT networks, making them immune to the current as well as the continuously emerging cyber threats. Through the integration of these cutting edge methods, organizations will be able to hold their ground against cyberattacks catalyzed by DDoS menace and stay ahead of dynamic threats whenever they arise. Keywords— Cloud computing, Data threats, Data Protection, Cloud security.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141110117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper we understand the significance of multinational companies controlling the market through mergers, acquisitions and investments in various countries this is how it makes dominance in the market. According to OECD in 2018 MNCs control between third of the world’s production. The ability to control the market is done by various socioeconomic and political factors which give the MNCs a edge compared to the small corporations competing in the same sector. In this research we deeply scrutinize the special benefits and advantages obtained by MNCs which in turn favors them to dominate the market. Keywords- Conglomerates, Monopoly, MNCs , Disrupt Monopoly, OECD, Socio-economic
{"title":"Strategy for Dominance of Multinational Conglomerates","authors":"Aditya Nag","doi":"10.55041/ijsrem34448","DOIUrl":"https://doi.org/10.55041/ijsrem34448","url":null,"abstract":"This paper we understand the significance of multinational companies controlling the market through mergers, acquisitions and investments in various countries this is how it makes dominance in the market. According to OECD in 2018 MNCs control between third of the world’s production. The ability to control the market is done by various socioeconomic and political factors which give the MNCs a edge compared to the small corporations competing in the same sector. In this research we deeply scrutinize the special benefits and advantages obtained by MNCs which in turn favors them to dominate the market. Keywords- Conglomerates, Monopoly, MNCs , Disrupt Monopoly, OECD, Socio-economic","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141110402","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}
Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieLens datasets were selected to test the effectiveness of our new recommendation algorithm. With the input information, the hidden features of the movies and the users were mined using deep learning to build a deep-learning network algorithm model for training to further predict movie scores. With a learning rate of 0.001, the root mean squared error (RMSE) scores achieved 0.9908 and 0.9096 for test sets of MovieLens 100 K and 1 M datasets, respectively. The scoring prediction results show improved accuracy after incorporating the potential features and connections in multimodal data with deep-learning technology. Compared with the traditional collaborative filtering algorithms, such as user-based collaborative filtering (User-CF), item-based content-based filtering (Item-CF), and singular-value decomposition (SVD) approaches, the multimodal movie recommendation system using deep learning could provide better personalized recommendation results. Meanwhile, the sparse data problem was alleviated to a certain degree. We suggest that the recommendation system can be improved through the combination of the deep-learning technology and the multimodal data analysis. Keywords: recommendation system; deep learning; matrix factorization; multimodal technique
推荐系统是应对信息过载的最佳方法，被广泛用于为用户提供高效的个性化内容和服务。在过去的十年中，许多推荐算法被广泛应用于各种电子商务应用中，包括电影流媒体服务。然而，许多电影推荐系统经常会遇到稀疏数据冷启动问题。本文报告了一种基于多模态数据分析和深度学习的个性化多模态电影推荐系统。我们选取了真实世界中的 MovieLens 数据集来测试新推荐算法的有效性。通过输入信息，利用深度学习挖掘电影和用户的隐藏特征，建立深度学习网络算法模型进行训练，进一步预测电影评分。在学习率为 0.001 的情况下，MovieLens 100 K 和 1 M 数据集测试集的均方根误差（RMSE）分别为 0.9908 和 0.9096。得分预测结果表明，利用深度学习技术整合多模态数据中的潜在特征和连接后，准确率得到了提高。与基于用户的协同过滤（User-CF）、基于项的内容过滤（Item-CF）和奇异值分解（SVD）等传统协同过滤算法相比，利用深度学习的多模态电影推荐系统能提供更好的个性化推荐结果。同时，稀疏数据问题也得到了一定程度的缓解。我们建议通过深度学习技术与多模态数据分析的结合来改进推荐系统。关键词：推荐系统；深度学习；矩阵因式分解；多模态技术
{"title":"Movie Lens – Movie Recommendation System Using Deep Learning","authors":"Sreeja B","doi":"10.55041/ijsrem33379","DOIUrl":"https://doi.org/10.55041/ijsrem33379","url":null,"abstract":"Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieLens datasets were selected to test the effectiveness of our new recommendation algorithm. With the input information, the hidden features of the movies and the users were mined using deep learning to build a deep-learning network algorithm model for training to further predict movie scores. With a learning rate of 0.001, the root mean squared error (RMSE) scores achieved 0.9908 and 0.9096 for test sets of MovieLens 100 K and 1 M datasets, respectively. The scoring prediction results show improved accuracy after incorporating the potential features and connections in multimodal data with deep-learning technology. Compared with the traditional collaborative filtering algorithms, such as user-based collaborative filtering (User-CF), item-based content-based filtering (Item-CF), and singular-value decomposition (SVD) approaches, the multimodal movie recommendation system using deep learning could provide better personalized recommendation results. Meanwhile, the sparse data problem was alleviated to a certain degree. We suggest that the recommendation system can be improved through the combination of the deep-learning technology and the multimodal data analysis. Keywords: recommendation system; deep learning; matrix factorization; multimodal technique","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141112892","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 rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is tool that helps to detect intrusions by inspecting the network traffic. A system called an intrusion detection system (IDS) observes network traffic for malicious transactions and sends immediate alerts when it is observed. It is software that checks a network or system for malicious activities or policy violations. Each illegal activity or violation is often recorded and notified to an administrator. IDS monitors a network or system for malicious activity and protects a computer network from unauthorized access from users, including perhaps insiders. The intrusion detector learning task is to build a predictive model capable of distinguishing between ‘malicious connections’ and ‘genuine connections’. Keywords: Cyber security, intrusion detection, malware, machine learning, deep learning, deep neural networks, CNN,
{"title":"Deep Learning Approach for Intrusion Detection System","authors":"Niharika A P","doi":"10.55041/ijsrem33646","DOIUrl":"https://doi.org/10.55041/ijsrem33646","url":null,"abstract":"The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is tool that helps to detect intrusions by inspecting the network traffic. A system called an intrusion detection system (IDS) observes network traffic for malicious transactions and sends immediate alerts when it is observed. It is software that checks a network or system for malicious activities or policy violations. Each illegal activity or violation is often recorded and notified to an administrator. IDS monitors a network or system for malicious activity and protects a computer network from unauthorized access from users, including perhaps insiders. The intrusion detector learning task is to build a predictive model capable of distinguishing between ‘malicious connections’ and ‘genuine connections’. Keywords: Cyber security, intrusion detection, malware, machine learning, deep learning, deep neural networks, CNN,","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141110855","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}
Machine learning techniques have transformed healthcare by enabling precise and timely disease prediction. The capacity to forecast multiple diseases simultaneously can greatly enhance early diagnosis and treatment, leading to improved patient outcomes and lower healthcare expenses. This research paper delves into the use of machine learning algorithms for predicting various diseases, highlighting their advantages, challenges, and prospects. It provides a comprehensive overview of different machine learning models and the data sources frequently employed in disease prediction. Furthermore, it emphasises the importance of feature selection, model evaluation, and the integration of diverse data types to improve prediction accuracy. The findings underscore the significant potential of machine learning in predicting multiple diseases and its impact on public health. Specifically, the study demonstrates the application of a machine learning model to determine if an individual is affected by certain diseases. This model is trained using sample data to enhance its predictive capabilities. Key Words: Disease Prediction, Disease data, Machine Learning.
{"title":"HEALTH PREDICTION USING MACHINE LEARNING","authors":"SAURABH MISHRA,","doi":"10.55041/ijsrem34438","DOIUrl":"https://doi.org/10.55041/ijsrem34438","url":null,"abstract":"Machine learning techniques have transformed healthcare by enabling precise and timely disease prediction. The capacity to forecast multiple diseases simultaneously can greatly enhance early diagnosis and treatment, leading to improved patient outcomes and lower healthcare expenses. This research paper delves into the use of machine learning algorithms for predicting various diseases, highlighting their advantages, challenges, and prospects. It provides a comprehensive overview of different machine learning models and the data sources frequently employed in disease prediction. Furthermore, it emphasises the importance of feature selection, model evaluation, and the integration of diverse data types to improve prediction accuracy. The findings underscore the significant potential of machine learning in predicting multiple diseases and its impact on public health. Specifically, the study demonstrates the application of a machine learning model to determine if an individual is affected by certain diseases. This model is trained using sample data to enhance its predictive capabilities. Key Words: Disease Prediction, Disease data, Machine Learning.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141111539","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}
Sleep disorders are prevalent health concerns affecting millions of individuals worldwide, with adverse impacts on overall well-being and cognitive function. Detecting and diagnosing these disorders accurately is crucial for effective treatment planning and management. This project focuses on utilizing Electroencephalogram (EEG) signals, a non-invasive method for monitoring brain activity, to detect sleeping disorders. By leveraging advanced signal processing techniques and machine learning algorithms, this research aims to develop a robust and accurate system capable of identifying various types of sleep disorders, such as insomnia, sleep apnea, and narcolepsy, based on EEG data. The proposed approach holds the potential to enhance early detection, personalized treatment strategies, and ultimately improve the quality of life for individuals affected by sleep disorders. Keywords-Ambulatory EEG, automatic scoring, deep learning, electroencephalography, sleep staging.
{"title":"Sleep Disorder Detection Using EEG Signals","authors":"Shashank S G","doi":"10.55041/ijsrem34611","DOIUrl":"https://doi.org/10.55041/ijsrem34611","url":null,"abstract":"Sleep disorders are prevalent health concerns affecting millions of individuals worldwide, with adverse impacts on overall well-being and cognitive function. Detecting and diagnosing these disorders accurately is crucial for effective treatment planning and management. This project focuses on utilizing Electroencephalogram (EEG) signals, a non-invasive method for monitoring brain activity, to detect sleeping disorders. By leveraging advanced signal processing techniques and machine learning algorithms, this research aims to develop a robust and accurate system capable of identifying various types of sleep disorders, such as insomnia, sleep apnea, and narcolepsy, based on EEG data. The proposed approach holds the potential to enhance early detection, personalized treatment strategies, and ultimately improve the quality of life for individuals affected by sleep disorders. Keywords-Ambulatory EEG, automatic scoring, deep learning, electroencephalography, sleep staging.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108464","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 "AI YouTube Video Summary using NLP" project introduces an innovative solution to the burgeoning challenge of digesting vast amounts of video content on platforms like YouTube. With the exponential growth of online video, users often face time constraints and information overload, hindering their ability to extract valuable insights efficiently. Our project addresses this issue by harnessing the capabilities of Artificial Intelligence (AI) and Natural Language Processing (NLP) to automatically generate concise summaries of YouTube videos. Through a seamless integration with the MERN stack, our system enables users to input video URLs and receive summaries in three distinct forms: short, long, and key insights. By automating the process of transcript extraction, linguistic analysis, and summarization, our system streamlines content consumption, offering users a time-saving and effective method for accessing essential information. By leveraging machine learning algorithms and linguistic analysis techniques, our system accurately identifies and distills key themes, concepts, and insights embedded within the video content. This empowers users to gain comprehensive understanding without the need for exhaustive viewing, thereby enhancing their browsing experience and knowledge acquisition. In essence, the "AI YouTube Video Summary using NLP" project represents a significant advancement in content consumption methodologies, offering a practical solution to the challenges posed by the proliferation of video content online. Through our innovative approach, we aim to revolutionize the way users engage with YouTube videos, facilitating efficient information extraction and empowering them to make the most of their online viewing experience. Keywords: Artificial Intelligence (AI), Natural Language Processing (NLP), Text Summarization, Multimedia Content Analysis, Automatic Summarization.
{"title":"AI YOUTUBE VIDEO SUMMARY USING NLP","authors":"R.Dinesh Kumar,","doi":"10.55041/ijsrem34537","DOIUrl":"https://doi.org/10.55041/ijsrem34537","url":null,"abstract":"The \"AI YouTube Video Summary using NLP\" project introduces an innovative solution to the burgeoning challenge of digesting vast amounts of video content on platforms like YouTube. With the exponential growth of online video, users often face time constraints and information overload, hindering their ability to extract valuable insights efficiently. Our project addresses this issue by harnessing the capabilities of Artificial Intelligence (AI) and Natural Language Processing (NLP) to automatically generate concise summaries of YouTube videos. Through a seamless integration with the MERN stack, our system enables users to input video URLs and receive summaries in three distinct forms: short, long, and key insights. By automating the process of transcript extraction, linguistic analysis, and summarization, our system streamlines content consumption, offering users a time-saving and effective method for accessing essential information. By leveraging machine learning algorithms and linguistic analysis techniques, our system accurately identifies and distills key themes, concepts, and insights embedded within the video content. This empowers users to gain comprehensive understanding without the need for exhaustive viewing, thereby enhancing their browsing experience and knowledge acquisition. In essence, the \"AI YouTube Video Summary using NLP\" project represents a significant advancement in content consumption methodologies, offering a practical solution to the challenges posed by the proliferation of video content online. Through our innovative approach, we aim to revolutionize the way users engage with YouTube videos, facilitating efficient information extraction and empowering them to make the most of their online viewing experience. Keywords: Artificial Intelligence (AI), Natural Language Processing (NLP), Text Summarization, Multimedia Content Analysis, Automatic Summarization.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141111380","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}
Marketing and sales promotion plans should begin with an overview of the campaign's background, goals, and methodology. It establishes the framework for the other parts of the marketing strategy and is thus crucial to them. Potentially addressed in the introduction are the following significant points: Good or Service Provide a high-level summary of the advertised item or service to kick things off. The marketing strategies discussed later in the article are supported by this background knowledge. Be very explicit about what you want to achieve with the sales and marketing campaign. Whether the objective is to introduce a new product, increase sales, or enhance brand awareness among consumers, it must be clearly articulated. For any advertising effort to be effective, you need to identify your target audience. This section may contain demographic information such as age, gender, location, socioeconomic status, etc. Knowing one's target audience inside and out is critical for crafting effective marketing messages. Goals for Marketing: Outline the specific outcomes you desire from your marketing and sales initiatives. Goals should be SMART (specific, measurable, attainable, relevant, and time-bound) and align with the overall objectives of the organisation. Outline the marketing objectives and the strategies that will be implemented to achieve them. This includes things like sales, influencer partnerships, social media promotions, advertising, etc. Financial Resources: Provide a brief overview of the financial resources that will be utilised for the marketing and sales promotion drives. So now we're all on the same page, and we have all the tools we need to put the plan into action. The desired result is a discussion of the expected outcomes of the sales promotion and advertising campaign. Revenue projections, increases in brand awareness, targets for new client acquisition, etc. are all instances of such measures. Appropriately acknowledge and appreciate those who have contributed to developing the plan for marketing and sales promotion. Taking everything into account, the introduction does a fantastic job of setting the stage for the sales promotion and marketing materials and providing readers with an idea of what to expect in terms of tone and substance. Being both brief and informative, it should stimulate interest in the next marketing activities.
{"title":"Marketing and Sales Promotion","authors":"Y. Balarabe","doi":"10.55041/ijsrem34481","DOIUrl":"https://doi.org/10.55041/ijsrem34481","url":null,"abstract":"Marketing and sales promotion plans should begin with an overview of the campaign's background, goals, and methodology. It establishes the framework for the other parts of the marketing strategy and is thus crucial to them. Potentially addressed in the introduction are the following significant points: Good or Service Provide a high-level summary of the advertised item or service to kick things off. The marketing strategies discussed later in the article are supported by this background knowledge. Be very explicit about what you want to achieve with the sales and marketing campaign. Whether the objective is to introduce a new product, increase sales, or enhance brand awareness among consumers, it must be clearly articulated. For any advertising effort to be effective, you need to identify your target audience. This section may contain demographic information such as age, gender, location, socioeconomic status, etc. Knowing one's target audience inside and out is critical for crafting effective marketing messages. Goals for Marketing: Outline the specific outcomes you desire from your marketing and sales initiatives. Goals should be SMART (specific, measurable, attainable, relevant, and time-bound) and align with the overall objectives of the organisation. Outline the marketing objectives and the strategies that will be implemented to achieve them. This includes things like sales, influencer partnerships, social media promotions, advertising, etc. Financial Resources: Provide a brief overview of the financial resources that will be utilised for the marketing and sales promotion drives. So now we're all on the same page, and we have all the tools we need to put the plan into action. The desired result is a discussion of the expected outcomes of the sales promotion and advertising campaign. Revenue projections, increases in brand awareness, targets for new client acquisition, etc. are all instances of such measures. Appropriately acknowledge and appreciate those who have contributed to developing the plan for marketing and sales promotion. Taking everything into account, the introduction does a fantastic job of setting the stage for the sales promotion and marketing materials and providing readers with an idea of what to expect in terms of tone and substance. Being both brief and informative, it should stimulate interest in the next marketing activities.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141112180","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}
Background: Protective layers of skin cover the body. Plant-based herbal body lotion soothes and moisturises. Treatments commonly include succulent aloe vera, which heals, reduces pain, and moisturises. For hundreds of years, it has healed skin burns and injuries. Aim: This study aims on the pharmaceutical assessment of Aloe-vera by formulating an herbal Body lotion. Material and Method: Aloe-vera, Honey, Glycerin, Rose Water and Triethanolamine were taken for the formulation of herbal body lotion. Evaluation parameters were also performed to evaluate the formulation and to make sure that the subjected formulation is not harmful for the human mankind. Result: The aloe vera body lotion was formulated by using various type of ingredients such as Aloe- vera, glycerin, rose water, honey and Triethanolamine. Aloe-vera contain antimicrobial and hydrating properties protect skin against microbial degradation and moisture to skin. Conclusion: herbal body lotion is prepared for tropical administration. Aloe vera is used in lotion to provide synergistic effect as well as moisturizing effect on skin. Herbal remedies are experiencing a surge in popularity worldwide. The utilization of aloe vera, honey, Coconut oil, Lemon Oil and glycerin in the formulation of an herbal lotion is an exemplary notion. Keywords: Herbal body lotion, aloe-vera, honey, skin, glycerin, pharmaceutical assessment etc.
{"title":"Pharmaceutical Assessment of Body Lotion: A Herbal Formulation and its Potential Benefits","authors":"Rathod Arti Vasantrao","doi":"10.55041/ijsrem34273","DOIUrl":"https://doi.org/10.55041/ijsrem34273","url":null,"abstract":"Background: Protective layers of skin cover the body. Plant-based herbal body lotion soothes and moisturises. Treatments commonly include succulent aloe vera, which heals, reduces pain, and moisturises. For hundreds of years, it has healed skin burns and injuries. Aim: This study aims on the pharmaceutical assessment of Aloe-vera by formulating an herbal Body lotion. Material and Method: Aloe-vera, Honey, Glycerin, Rose Water and Triethanolamine were taken for the formulation of herbal body lotion. Evaluation parameters were also performed to evaluate the formulation and to make sure that the subjected formulation is not harmful for the human mankind. Result: The aloe vera body lotion was formulated by using various type of ingredients such as Aloe- vera, glycerin, rose water, honey and Triethanolamine. Aloe-vera contain antimicrobial and hydrating properties protect skin against microbial degradation and moisture to skin. Conclusion: herbal body lotion is prepared for tropical administration. Aloe vera is used in lotion to provide synergistic effect as well as moisturizing effect on skin. Herbal remedies are experiencing a surge in popularity worldwide. The utilization of aloe vera, honey, Coconut oil, Lemon Oil and glycerin in the formulation of an herbal lotion is an exemplary notion. Keywords: Herbal body lotion, aloe-vera, honey, skin, glycerin, pharmaceutical assessment etc.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141107817","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}
Plant and tree populations must be preserved and supported in order to mitigate the growing issues brought about by food and water scarcity brought on by population increase and climate change. The occurrence of plant diseases is a major issue in agriculture as it severely reduces agricultural output. In order to overcome this difficulty, scientists are investigating novel approaches that make use of sensors and imaging to collect data on plant health in order to detect diseases early on. The goal of this project is to create a "Smart Plant Health Care System" that combines embedded technologies such as Arduino, Raspberry Pi, and Jetson Nano for pesticide remediation controlled by Arduino and image-based illness diagnosis. More specifically, convolutional neural networks (CNNs) are implemented for real-time illness diagnosis using the processing capacity and adaptability of the Raspberry Pi. Keywords: Smart Agriculture, Plant Health Monitoring, Disease Detection,
必须保护和支持植物和树木种群，以缓解人口增长和气候变化带来的粮食和水资源短缺所造成的日益严重的问题。植物病害的发生是农业的一个主要问题，因为它会严重降低农业产量。为了克服这一困难，科学家们正在研究利用传感器和成像技术收集植物健康数据的新方法，以便及早发现病害。该项目的目标是创建一个 "智能植物健康护理系统"，将 Arduino、Raspberry Pi 和 Jetson Nano 等嵌入式技术结合起来，实现由 Arduino 控制的农药修复和基于图像的疾病诊断。更具体地说，利用 Raspberry Pi 的处理能力和适应性，实现了卷积神经网络（CNN），用于实时疾病诊断。关键词智能农业 植物健康监测 病害检测
{"title":"SMART PLANT HEALTH CARE SYSTEM: Image Based Disease Detection and Pesticide Remediation","authors":"Rohan S Savadakar","doi":"10.55041/ijsrem34613","DOIUrl":"https://doi.org/10.55041/ijsrem34613","url":null,"abstract":"Plant and tree populations must be preserved and supported in order to mitigate the growing issues brought about by food and water scarcity brought on by population increase and climate change. The occurrence of plant diseases is a major issue in agriculture as it severely reduces agricultural output. In order to overcome this difficulty, scientists are investigating novel approaches that make use of sensors and imaging to collect data on plant health in order to detect diseases early on. The goal of this project is to create a \"Smart Plant Health Care System\" that combines embedded technologies such as Arduino, Raspberry Pi, and Jetson Nano for pesticide remediation controlled by Arduino and image-based illness diagnosis. More specifically, convolutional neural networks (CNNs) are implemented for real-time illness diagnosis using the processing capacity and adaptability of the Raspberry Pi. Keywords: Smart Agriculture, Plant Health Monitoring, Disease Detection,","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141109483","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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