Mr.Chethan Kumar K.M, Mr.Ramakrishna C N K.M, Mrs. R Srividya, Mrs. K.M Shantha, Mrudula K
Abstract—Infertility is one of society's physical, social, and psychological difficulties. "Failure to obtain a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse," according to the definition. Ovulation induction has remained a watershed moment in the lives of women. Infertility is a prevalent problem that is sometimes misunderstood. Male infertility has affected an increasingly large population over the past few decades, affecting over 186 million people globally. The advent of assisted reproductive technologies (ARTs) and artificial intelligence (AI) has changed the landscape of diagnosis and treatment of male infertility. Because of its effects on families, its importance to study in related fields such as fertility trends and reproductive health, and its implications for practitioners who work with individuals and couples facing infertility. Infertility is an important topic for family scientists. Inability or difficulty in conceiving is a physically and psychologically draining experience for a woman. Polycystic Ovary Syndrome (PCOS) has been determined as one of the serious health problems in women that affects the fertility of women and leads to significant health conditions. Therefore, early diagnosis of polycystic ovary syndrome can be effective in the treatment process Keywords—infertility; hormones; clinical data,PCOS,adolescene ,harmone,
{"title":"Men and Female Infertility: Multidisciplinary Review","authors":"Mr.Chethan Kumar K.M, Mr.Ramakrishna C N K.M, Mrs. R Srividya, Mrs. K.M Shantha, Mrudula K","doi":"10.55041/ijsrem36694","DOIUrl":"https://doi.org/10.55041/ijsrem36694","url":null,"abstract":"Abstract—Infertility is one of society's physical, social, and psychological difficulties. \"Failure to obtain a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse,\" according to the definition. Ovulation induction has remained a watershed moment in the lives of women. Infertility is a prevalent problem that is sometimes misunderstood. Male infertility has affected an increasingly large population over the past few decades, affecting over 186 million people globally. The advent of assisted reproductive technologies (ARTs) and artificial intelligence (AI) has changed the landscape of diagnosis and treatment of male infertility. Because of its effects on families, its importance to study in related fields such as fertility trends and reproductive health, and its implications for practitioners who work with individuals and couples facing infertility. Infertility is an important topic for family scientists. Inability or difficulty in conceiving is a physically and psychologically draining experience for a woman. Polycystic Ovary Syndrome (PCOS) has been determined as one of the serious health problems in women that affects the fertility of women and leads to significant health conditions. Therefore, early diagnosis of polycystic ovary syndrome can be effective in the treatment process Keywords—infertility; hormones; clinical data,PCOS,adolescene ,harmone,","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"13 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815862","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 Smart Baby Monitoring System using the YOLO V8 algorithm is designed to enhance infant monitoring by leveraging advanced computer vision techniques. This project utilizes YOLO (You Only Look Once) version 8, a state-of-the-art object detection algorithm, implemented with Python and frameworks like Tensor Flow or PyTorch, to detect and track objects in real-time video feeds. The system incorporates features for facial recognition to identify known caregivers and alert mechanisms for unusual activities or emergencies. The user interface provides real-time alerts, visualizations, and historical data analysis for caregivers via a web or mobile application. By leveraging YOLO V8's efficiency in object detection and Python's capabilities for data processing and integration, this system aims to enhance safety, improve care giving efficiency, and provide peace of mind to parents and caregivers.
{"title":"SMART BABY MONITORING SYSTEM USING YOLO V8 ALGORITHM","authors":"Er.M. Meena, Dr.G Ramesh","doi":"10.55041/ijsrem36698","DOIUrl":"https://doi.org/10.55041/ijsrem36698","url":null,"abstract":"The Smart Baby Monitoring System using the YOLO V8 algorithm is designed to enhance infant monitoring by leveraging advanced computer vision techniques. This project utilizes YOLO (You Only Look Once) version 8, a state-of-the-art object detection algorithm, implemented with Python and frameworks like Tensor Flow or PyTorch, to detect and track objects in real-time video feeds. The system incorporates features for facial recognition to identify known caregivers and alert mechanisms for unusual activities or emergencies. The user interface provides real-time alerts, visualizations, and historical data analysis for caregivers via a web or mobile application. By leveraging YOLO V8's efficiency in object detection and Python's capabilities for data processing and integration, this system aims to enhance safety, improve care giving efficiency, and provide peace of mind to parents and caregivers.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"6 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816128","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}
Financial fraud, particularly credit card fraud, is a pressing concern in the realm of digital transactions. The number of phone scams is increasing daily as con artists use phone calls to target victims for nefarious ends. Individuals are falling for con artists' proposals, becoming victims and giving up their personal information, leaving them open to abuse. Effective detection techniques are becoming more and more necessary. In this study, we offer an efficient approach to scam call identification utilizing speech-to-text libraries and the machine learning technique Naïve Bayes classifier. Our technology, which translates voice to text, uses this text to evaluate conversations in real time. It looks for trends and suspicious phrases that point to attempted scams, including asking for credit card numbers, passwords, or other sensitive information. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. Keyword: Spam Detection, Naïve Bayes, Natural Language Processing, Machine Learning.
{"title":"Scam Call Detection Using NLP and Naïve Bayes Classifier","authors":"Valarmathi C, S. Sharanya","doi":"10.55041/ijsrem36688","DOIUrl":"https://doi.org/10.55041/ijsrem36688","url":null,"abstract":"Financial fraud, particularly credit card fraud, is a pressing concern in the realm of digital transactions. The number of phone scams is increasing daily as con artists use phone calls to target victims for nefarious ends. Individuals are falling for con artists' proposals, becoming victims and giving up their personal information, leaving them open to abuse. Effective detection techniques are becoming more and more necessary. In this study, we offer an efficient approach to scam call identification utilizing speech-to-text libraries and the machine learning technique Naïve Bayes classifier. Our technology, which translates voice to text, uses this text to evaluate conversations in real time. It looks for trends and suspicious phrases that point to attempted scams, including asking for credit card numbers, passwords, or other sensitive information. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. The user will be able to decide whether or not to trust and continue with the call by using the alert prompt that appears as a pop-up message if the words are found to be suspicious. The user will take certain measures, such as ending the conversation right away, blocking the number, and reporting it further, if they don't trust the call. Our strategy is to successfully handle scam calls through ongoing adaptation and learning, boosting user security and confidence in phone conversations. Keyword: Spam Detection, Naïve Bayes, Natural Language Processing, Machine Learning.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing complexity and dynamic nature of the modern workplace necessitate not only technical expertise but also a strong command of soft skills. Soft skills, often regarded as interpersonal or people skills, play a crucial role in enhancing individual performance and overall organizational success. This paper explores the various dimensions of soft skills, their significance in the professional environment, and effective strategies for their development. The paper argues that soft skills are indispensable for fostering a collaborative, adaptable, and productive workplace.
{"title":"Soft Skills and Their Importance in the Workplace","authors":"Sweta K Mor","doi":"10.55041/ijsrem36696","DOIUrl":"https://doi.org/10.55041/ijsrem36696","url":null,"abstract":"The increasing complexity and dynamic nature of the modern workplace necessitate not only technical expertise but also a strong command of soft skills. Soft skills, often regarded as interpersonal or people skills, play a crucial role in enhancing individual performance and overall organizational success. This paper explores the various dimensions of soft skills, their significance in the professional environment, and effective strategies for their development. The paper argues that soft skills are indispensable for fostering a collaborative, adaptable, and productive workplace.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"84 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817497","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 use of social media sites like Facebook and Twitter, along with the advent of the Internet, has allowed for the dissemination of information at a level never before seen... More people than ever before are making and sharing content on social media, and unfortunately, some of it is false or otherwise unfounded. It is difficult to automate the process of determining if a written article contains misinformation or disinformation. Prior to reaching a conclusion on an article's veracity, even a domain expert must consider several factors. Automated news article categorization is our proposed usage of a machine learning ensemble technique in this study. In this study, we examine various linguistic characteristics that can be used to distinguish between real and fake news. Taking use of these features, we evaluate the performance of a variety of machine learning algorithms trained using various ensemble methods on four real-world datasets. Results from experiments show that our suggested ensemble learner method outperforms individual learners. Keywords: World Wide Web, Social Media platforms, Information distribution, Content Sharing Textual Features, Machine Learning, Machine Learning ensemble technique, Real-worlds dataset etc.
{"title":"Fake News Detection Using Machine Learning","authors":"Preeti Barla, Smruti Ranjan Swain","doi":"10.55041/ijsrem36559","DOIUrl":"https://doi.org/10.55041/ijsrem36559","url":null,"abstract":"The rapid use of social media sites like Facebook and Twitter, along with the advent of the Internet, has allowed for the dissemination of information at a level never before seen... More people than ever before are making and sharing content on social media, and unfortunately, some of it is false or otherwise unfounded. It is difficult to automate the process of determining if a written article contains misinformation or disinformation. Prior to reaching a conclusion on an article's veracity, even a domain expert must consider several factors. Automated news article categorization is our proposed usage of a machine learning ensemble technique in this study. In this study, we examine various linguistic characteristics that can be used to distinguish between real and fake news. Taking use of these features, we evaluate the performance of a variety of machine learning algorithms trained using various ensemble methods on four real-world datasets. Results from experiments show that our suggested ensemble learner method outperforms individual learners. Keywords: World Wide Web, Social Media platforms, Information distribution, Content Sharing Textual Features, Machine Learning, Machine Learning ensemble technique, Real-worlds dataset etc.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"67 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817900","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}
Melanoma is a type of carcinoma with a notably high mortality rate. Accurate diagnosis of this aggressive cancer is crucial due to its severe implications. Key diagnostic indicators include asymmetrical shape, heterogeneous color, diameter greater than 6 mm, and irregular borders, which dermatologists typically identify through visual examination. The conventional method for carcinoma detection is biopsy, involving the removal or scraping of skin samples for extensive laboratory testing. This process is both painful and time- consuming. To improve patient experience and enhance diagnostic efficiency, computer-based detection using image processing techniques and deep learning algorithms, specifically Convolutional Neural Networks (CNNs), has been developed to accurately identify melanoma. Keywords: Deep learning, CNN, Computer- based detection
{"title":"Melanoma Detection and Classification using Deep Learning","authors":"Bhavani C N, D. B B","doi":"10.55041/ijsrem36685","DOIUrl":"https://doi.org/10.55041/ijsrem36685","url":null,"abstract":"Melanoma is a type of carcinoma with a notably high mortality rate. Accurate diagnosis of this aggressive cancer is crucial due to its severe implications. Key diagnostic indicators include asymmetrical shape, heterogeneous color, diameter greater than 6 mm, and irregular borders, which dermatologists typically identify through visual examination. The conventional method for carcinoma detection is biopsy, involving the removal or scraping of skin samples for extensive laboratory testing. This process is both painful and time- consuming. To improve patient experience and enhance diagnostic efficiency, computer-based detection using image processing techniques and deep learning algorithms, specifically Convolutional Neural Networks (CNNs), has been developed to accurately identify melanoma. Keywords: Deep learning, CNN, Computer- based detection","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"25 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816975","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) has emerged as a transformative force across various sectors, revolutionizing processes, enhancing efficiency, and redefining innovation. This research paper delves into the multifaceted landscape of AI, focusing on its applications, knowledge representation, and implications for innovation. The paper begins by exploring the diverse applications of AI across healthcare, gaming, finance, data security, social media, robotics, and e-commerce. In healthcare, AI aids in diagnosis and patient care, while in gaming, it enables strategic game play and enhances user experience. The finance sector leverages AI for automation, analytics, and algorithmic trading, improving decision-making and customer service. AI also plays a vital role in ensuring data security through advanced detection systems, manages vast social media data for enhanced user engagement, and drives innovation in robotics and e-commerce. Moving forward, the paper delves into the realm of expert systems and knowledge representation, elucidating the role of AI in simulating human expertise and modeling complex information structures. It discusses various aspects of knowledge representation, such as propositional knowledge representation, image retrieval, functional relationships between objects, and class representation formalism, highlighting their significance in developing intelligent systems. Furthermore, the paper examines the integration of AI in maintenance practices, both for tangible systems like engineering workshops and intangible products like data extraction wrappers. It underscores the importance of AI in optimizing operational efficiency, reducing downtime, and ensuring continuous data extraction. Lastly, the paper explores the concept of deep learning as a general- purpose invention, discussing its potential implications for innovation, management, institutions, and policy. It addresses key issues such as the management and organization of innovation, intellectual property rights, competition policy, and the cumulative knowledge production facilitated by deep learning. In conclusion, this research paper provides a comprehensive overview of AI's transformative potential, emphasizing the need for further research and analysis to fully comprehend its impact on society, economy, and innovation.
{"title":"Research Paper on Artificial Intelligence","authors":"Brahmansh Sharma","doi":"10.55041/ijsrem36678","DOIUrl":"https://doi.org/10.55041/ijsrem36678","url":null,"abstract":"Artificial Intelligence (AI) has emerged as a transformative force across various sectors, revolutionizing processes, enhancing efficiency, and redefining innovation. This research paper delves into the multifaceted landscape of AI, focusing on its applications, knowledge representation, and implications for innovation. The paper begins by exploring the diverse applications of AI across healthcare, gaming, finance, data security, social media, robotics, and e-commerce. In healthcare, AI aids in diagnosis and patient care, while in gaming, it enables strategic game play and enhances user experience. The finance sector leverages AI for automation, analytics, and algorithmic trading, improving decision-making and customer service. AI also plays a vital role in ensuring data security through advanced detection systems, manages vast social media data for enhanced user engagement, and drives innovation in robotics and e-commerce. Moving forward, the paper delves into the realm of expert systems and knowledge representation, elucidating the role of AI in simulating human expertise and modeling complex information structures. It discusses various aspects of knowledge representation, such as propositional knowledge representation, image retrieval, functional relationships between objects, and class representation formalism, highlighting their significance in developing intelligent systems. Furthermore, the paper examines the integration of AI in maintenance practices, both for tangible systems like engineering workshops and intangible products like data extraction wrappers. It underscores the importance of AI in optimizing operational efficiency, reducing downtime, and ensuring continuous data extraction. Lastly, the paper explores the concept of deep learning as a general- purpose invention, discussing its potential implications for innovation, management, institutions, and policy. It addresses key issues such as the management and organization of innovation, intellectual property rights, competition policy, and the cumulative knowledge production facilitated by deep learning. In conclusion, this research paper provides a comprehensive overview of AI's transformative potential, emphasizing the need for further research and analysis to fully comprehend its impact on society, economy, and innovation.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"69 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817796","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}
Solid waste management is one of the challenges faced by many cities. Poor solid waste management will lead to various problems in health, environment, and socio-economic aspects. Pune has been innovative in its solid waste management. To help achieve this the city has tied up with a group of marginalized women at the forefront of a campaign to clean the city. Through an agreement with the Pune Municipal Corporation (PMC), more than 3,000 women workers provide door-to-door waste collection services to over 600,000 homes in the city, The waste generators, i.e. the householders are major part of the waste management process. Their attitude and habit about the waste generated in the household and how it is handed to the municipal corporation affects the whole waste management of the city. In this study descriptive quantitative questioner was prepared by researcher. A total 708 respondents or the householder participated in this study. Results show that people are more aware about the disposal of dry waste even its is smaller part of the composition of the total waste. It also shows that there is a rational bias when it comes to peoples’ belief and their actions when it comes to recycling. Keywords: SWM, Habits of householders, Pune solid waste management
{"title":"Habits & Attitude of Householders About Solid Waste Management in Pune City","authors":"Medha Tadpatrikar, Dr Rajshree Rathod","doi":"10.55041/ijsrem36680","DOIUrl":"https://doi.org/10.55041/ijsrem36680","url":null,"abstract":"Solid waste management is one of the challenges faced by many cities. Poor solid waste management will lead to various problems in health, environment, and socio-economic aspects. Pune has been innovative in its solid waste management. To help achieve this the city has tied up with a group of marginalized women at the forefront of a campaign to clean the city. Through an agreement with the Pune Municipal Corporation (PMC), more than 3,000 women workers provide door-to-door waste collection services to over 600,000 homes in the city, The waste generators, i.e. the householders are major part of the waste management process. Their attitude and habit about the waste generated in the household and how it is handed to the municipal corporation affects the whole waste management of the city. In this study descriptive quantitative questioner was prepared by researcher. A total 708 respondents or the householder participated in this study. Results show that people are more aware about the disposal of dry waste even its is smaller part of the composition of the total waste. It also shows that there is a rational bias when it comes to peoples’ belief and their actions when it comes to recycling. Keywords: SWM, Habits of householders, Pune solid waste management","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"44 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141814997","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 the field of computer vision and document analysis, the identification and categorization of Indian scripts in natural scene images pose a difficult yet crucial challenge. The variety of characters and intricate writing styles in Indian scripts require reliable solutions for precise identification under different environmental conditions. This study presents a novel CNN model designed for identifying scripts in Indian multilingual document images captured by cameras. Experimental evaluations of the model's performance were conducted with two regional languages (Odia and Telugu) and one national language (Hindi). The average accuracy in script recognition for the three language combinations is 95.66%, with Odia achieving 99.00%, Hindi 90.33%, and Telugu 98.12%. The model achieved the highest accuracy in recognition. The model achieved the highest accuracy in recognition Keywords: Text Recognition, Image Augmentation, CNN, LSTM, VGG, ResNet, DenseNet, Datasets, Natural Images
{"title":"Recognition And Classification of Indian Scripts in Natural Scene Images","authors":"Suryosnata Behera, Dr.SatyaRanjan Pattanaik","doi":"10.55041/ijsrem36661","DOIUrl":"https://doi.org/10.55041/ijsrem36661","url":null,"abstract":"In the field of computer vision and document analysis, the identification and categorization of Indian scripts in natural scene images pose a difficult yet crucial challenge. The variety of characters and intricate writing styles in Indian scripts require reliable solutions for precise identification under different environmental conditions. This study presents a novel CNN model designed for identifying scripts in Indian multilingual document images captured by cameras. Experimental evaluations of the model's performance were conducted with two regional languages (Odia and Telugu) and one national language (Hindi). The average accuracy in script recognition for the three language combinations is 95.66%, with Odia achieving 99.00%, Hindi 90.33%, and Telugu 98.12%. The model achieved the highest accuracy in recognition. The model achieved the highest accuracy in recognition Keywords: Text Recognition, Image Augmentation, CNN, LSTM, VGG, ResNet, DenseNet, Datasets, Natural Images","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"14 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816199","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}
Jahnavi Raghava Singh, J. Gopi, ,V.Anil Santosh, Ddd Suri Babu
In this study, a machine learning model is developed to classify different types of cancer using convolutional neural networks (CNNs) for image processing. The core objective is to achieve a performance level comparable to that of dermatologists. The model is trained on a substantial dataset of medical images, enabling it to learn and recognize various characteristics indicative of different cancer types. By leveraging the power of CNNs, the model can process these images effectively, identifying subtle patterns and features that are often challenging to detect with the naked eye. The training process involves feeding the CNN with labelled images, enabling it to differentiate between benign and malignant cases with high accuracy. Through rigorous testing, the model demonstrates competence on par with experienced dermatologists, both in terms of sensitivity and specificity. This equivalence in performance is particularly significant as it underscores the model's potential to aid in clinical settings, providing reliable second opinions and enhancing diagnostic workflows. A user interface is also developed to allow input images to be analysed by the trained CNN model. This interface not only displays the model’s predictions but also provides essential metrics such as confidence scores and probability distributions. These metrics offer valuable insights into the model's decision-making process, aiding clinicians in understanding and trusting the AI's assessments. Overall, the findings suggest that convolutional neural networks hold substantial promise for improving cancer diagnosis. The model's high performance in classification tasks demonstrates its viability as a tool for supporting dermatologists in clinical practice. By reducing diagnostic errors and accelerating the identification process, this technology has the potential to significantly impact patient outcomes and advance the field of medical imaging and diagnostics. Keywords: Convolutional Neural Networks (CNNs); Cancer Classification; Medical Image Processing; Dermatology AI; Diagnostic Accuracy
{"title":"Categorizing Dermatological Malignancies Via Computational Methods","authors":"Jahnavi Raghava Singh, J. Gopi, ,V.Anil Santosh, Ddd Suri Babu","doi":"10.55041/ijsrem36704","DOIUrl":"https://doi.org/10.55041/ijsrem36704","url":null,"abstract":"In this study, a machine learning model is developed to classify different types of cancer using convolutional neural networks (CNNs) for image processing. The core objective is to achieve a performance level comparable to that of dermatologists. The model is trained on a substantial dataset of medical images, enabling it to learn and recognize various characteristics indicative of different cancer types. By leveraging the power of CNNs, the model can process these images effectively, identifying subtle patterns and features that are often challenging to detect with the naked eye. The training process involves feeding the CNN with labelled images, enabling it to differentiate between benign and malignant cases with high accuracy. Through rigorous testing, the model demonstrates competence on par with experienced dermatologists, both in terms of sensitivity and specificity. This equivalence in performance is particularly significant as it underscores the model's potential to aid in clinical settings, providing reliable second opinions and enhancing diagnostic workflows. A user interface is also developed to allow input images to be analysed by the trained CNN model. This interface not only displays the model’s predictions but also provides essential metrics such as confidence scores and probability distributions. These metrics offer valuable insights into the model's decision-making process, aiding clinicians in understanding and trusting the AI's assessments. Overall, the findings suggest that convolutional neural networks hold substantial promise for improving cancer diagnosis. The model's high performance in classification tasks demonstrates its viability as a tool for supporting dermatologists in clinical practice. By reducing diagnostic errors and accelerating the identification process, this technology has the potential to significantly impact patient outcomes and advance the field of medical imaging and diagnostics. Keywords: Convolutional Neural Networks (CNNs); Cancer Classification; Medical Image Processing; Dermatology AI; Diagnostic Accuracy","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"11 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816226","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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