Journal of Scientific Research and Technology最新文献

{"title":"Human Trafficking- A Contemporary Form Of Slavery","authors":"Princy Verma","doi":"10.61808/jsrt97","DOIUrl":"https://doi.org/10.61808/jsrt97","url":null,"abstract":"Human trafficking is a growing menace which is ploughed into every country. The infection is so deeply rooted that it is an utter violation of sovereignty of any state by failure to observe the existing system of legal framework. Trafficking not only contravenes with the elemental human rights of an individual but is also a violation of ethics and morals a nation must hold in its essence. It is such a menace where the rights keep on violating throughout the vicious cycle and the misery never comes to an end. Trafficking here is compared with a ultra-modern form of slavery in the form of bonded labour, prostitution, domestic servitude and other kinds of toiling where thse victim has to act against his own will. No section of society or industry has remained untouched by trafficking. The victims are generally the people belonging to the economically disadvantaged section of society or those who lost their source of livelihood because of the natural calamities or armed conflicts. Some are even trapped by offering them a better standard of life. Human trafficking is on a boom in the entire world. It uses a variety of structures. The victims are pushed into organizations where they are unwillingly employed without any pecuniary benefit while the others are placed into such arrangements where they cannot seek help from anybody hence slamming the escape routes. The menace of human trafficking though spreading like a wildfire in the recent times but it was long established in the ancient India. It is nothing but a modern form of human subjugation. Earlier it was limited to the national boundaries but in the recent decades it has spread throughout the international borders. It does not only exert influence on the country of origin and destination but also leave its impact on the areas of transit. This paper deals with the offence of slavery in its modern sense which is human trafficking. It incorporates within itself a number of other offences that occur during the chain of transit.","PeriodicalId":506407,"journal":{"name":"Journal of Scientific Research and Technology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668784","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}

{"title":"Analysis Of Emotions Through Speech Recognition","authors":"Mr. Anandappa, Mrs. Kavita Mudnal","doi":"10.61808/jsrt95","DOIUrl":"https://doi.org/10.61808/jsrt95","url":null,"abstract":"Speech emotion recognition (SER) is a burgeoning field in AI that analyzes vocal characteristics to understand human emotions. It delves deeper than the literal meaning of words, uncovering emotional cues hidden within speech patterns. Pitch, loudness, and speech rate are just a few features that vary with emotional state. SER utilizes machine learning algorithms to classify these features into categories like happiness, sadness, or anger. This technology offers a treasure trove of possibilities, from enhancing human-computer interaction to revolutionizing customer service and even aiding in mental health assessments. As SER continues to evolve, it holds the potential to transform how we connect with machines, fostering deeper understanding and richer emotional experiences.","PeriodicalId":506407,"journal":{"name":"Journal of Scientific Research and Technology","volume":"119 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140678226","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}

{"title":"A Review On Early Detection Of Chronic Kidney Disease","authors":"Mamatha B, Sujatha P Terdal","doi":"10.61808/jsrt96","DOIUrl":"https://doi.org/10.61808/jsrt96","url":null,"abstract":"Early detection of Chronic Kidney Disease (CKD) is critical for timely intervention and effective treatment. Deep learning algorithms have demonstrated promise in medical applications, including disease detection. In this study, we propose a deep learning-based system for early CKD detection using the Chronic Kidney Disease dataset from Kaggle. Additionally, we incorporate the Grasshopper Optimization Algorithm (GOA) for feature selection to enhance the system's performance and interpretability. Our system employs a convolutional neural network (CNN) architecture to analyze clinical and laboratory attributes from the CKD dataset, obtained from Kaggle, consisting of 4,000 instances with 25 attributes. These attributes encompass patient demographics, blood tests, and medical history, providing a comprehensive representation of CKD-related factors. To improve the system's performance, we integrate the GOA for feature selection. The GOA is a nature-inspired metaheuristic optimization algorithm that mimics the foraging behavior of grasshoppers. It aims to identify the most relevant attributes associated with CKD from the dataset. By selecting a subset of informative features, we enhance the model's predictive accuracy and reduce overfitting. During the training phase, the CNN learns to automatically extract relevant features and patterns associated with CKD from the selected attributes. Additionally, data preprocessing techniques such as normalization and feature scaling are applied to further improve the model's performance and generalizability. To evaluate the system's performance, we conduct experiments using a separate test dataset comprising 1,000 instances from the CKD dataset. The incorporation of the GOA for feature selection in our deep learning system not only improves its performance but also enhances interpretability. By identifying the most relevant attributes associated with CKD, we focus on key biomarkers and risk factors, enhancing the system's accuracy and providing valuable insights into the disease. Our research showcases the potential of deep learning algorithms, coupled with GOA-based feature selection, for early CKD detection. By leveraging the Kaggle CKD dataset and incorporating the GOA, we contribute to improving the accuracy and applicability of the system in real-world clinical settings. To handle Big data we are proposing to implement this problem on Pyspark one of the Big data computational environments for effective learning. In this platform, we can dynamically scale the infrastructure as per the demand of the data. Ultimately, our work aims to advance the early detection and management of CKD, leading to improved patient outcomes and more effective healthcare interventions.","PeriodicalId":506407,"journal":{"name":"Journal of Scientific Research and Technology","volume":"117 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140678132","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}

{"title":"Digital Marketing Of Startup Businesses","authors":"Meha Agarwal, Dr. Archana Sharma","doi":"10.61808/jsrt94","DOIUrl":"https://doi.org/10.61808/jsrt94","url":null,"abstract":"This dissertation's overarching goal is to investigate how digital marketing may help new businesses get off the ground and competing. Digital marketing's impact on start-up growth, brand awareness, consumer loyalty, and customer relationship strength would be the subject of future study. There is a lack of literature on this topic; the only relevant study we could locate focused on start-ups and social media, suggesting that the former positively impacts the latter's capacity for innovation. The study relied on a qualitative research strategy based on semi-structured interviews with five startup companies to compile its findings. The study also made use of secondary data culled from online resources, journals, and peer-reviewed papers.Research shows that digital marketing is an effective and innovative strategy for attracting, retaining, and growing a business's clientele. Websites, industry-specific media, and online discussion groups have proven to be the most fruitful avenues for new businesses. It goes on to say that new businesses may make a lot of progress with digital marketing by raising customer awareness, trust, and brand recognition. But, when they first launch, the majority of start-ups are hesitant to use digital marketing methods.The analysis not only highlighted the advantages, but it also revealed the digital marketing tactics that worked best for new businesses. A new channel for reaching consumers and building brands, social media marketing has just arisen. Startups were able to successfully create a strong online presence in large part due to their ability to target certain demographics and customize content to connect with those audiences. Despite the bright potential of digital marketing, the study also shed light on a sobering fact: many new businesses are hesitant to completely commit to digital marketing techniques when they first launch.","PeriodicalId":506407,"journal":{"name":"Journal of Scientific Research and Technology","volume":"28 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711072","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}

{"title":"An Emerging Era Of Research In Agriculture Using AI","authors":"Anurag Chandra Mishra, Joydeep Das, Ram Awtar","doi":"10.61808/jsrt93","DOIUrl":"https://doi.org/10.61808/jsrt93","url":null,"abstract":"AI-driven precision agriculture, predictive analytics, robots, and market intelligence boost contemporary agriculture's production, efficiency, and sustainability. Precision agriculture, powered by AI algorithms, gives farmers detailed insights into crop health, soil conditions, and weather patterns for data-driven resource allocation and management. AI in agriculture's predictive analytics helps stakeholders forecast crop yields, market dynamics, and climate-related dangers, improving resilience and strategic planning. AI has great promise to solve agriculture's complicated problems. AI technologies allow computers to mimic human cognition and evaluate massive volumes of data to draw conclusions. AI can improve resource utilization, productivity, decision-making, and environmental effect in agriculture. AI-powered precision agriculture, crop monitoring, supply chain optimization, and market analysis are making agriculture more sustainable and resilient. To show AI's influence on farming, we explored precision agriculture, predictive analytics, robots, and supply chain optimization. Farmers may optimize resource usage, manage risks, and make data-driven choices using these tools, enhancing output, sustainability, and resilience.","PeriodicalId":506407,"journal":{"name":"Journal of Scientific Research and Technology","volume":"24 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711326","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}

{"title":"Cloud Computing And Security Issues In The Cloud","authors":"Anandappa Mr, Mrs. Kavita Mudnal","doi":"10.61808/jsrt92","DOIUrl":"https://doi.org/10.61808/jsrt92","url":null,"abstract":"For future of computing, cloud computing serves as conceptual and infrastructure foundations. There is significant shift in worldwide computer infrastructure toward cloud-based architectures. To take benefit of Cloud Computing, it is vital to deploy it across wide range of industries. However, Security remains primary concern in Cloud Computing environment. There is fresh business paradigm cloud-based technologies because of evolution of cloud services and providers. Because of the widespread use of the internet in many businesses as well as geographically dispersed cloud servers, confidential material of various organizations is typically stored on remote servers and in locations that could potentially be exposed by unwanted parties in cases where those servers are compromised. In the absence of reliable security, cloud computing's flexibility and benefits would be deemed unreliable. Our paper offers a overview of cloud computing ideas and also safety challenges that arise into regards of cloud technology including cloud architecture.","PeriodicalId":506407,"journal":{"name":"Journal of Scientific Research and Technology","volume":"22 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140375305","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}

{"title":"Online Voting System","authors":"Noor Ahmed, Prof. Anupama Pattanasetty","doi":"10.61808/jsrt91","DOIUrl":"https://doi.org/10.61808/jsrt91","url":null,"abstract":"Having a democratic voting system in place is crucial for any nation due to the general distrust of the conventional voting system. Individuals have seen the infringement of their basic rights. Lack of transparency has been a problem with several electronic voting methods. The government has a hard time winning the confidence of its citizens since most voting processes aren't transparent enough. It is easy to abuse, which is why both the old and new digital voting systems have failed. Finding solutions to issues with both the paper and electronic voting systems, such as voting-related injustices and accidents, is the main goal. A fair election with less injustice is possible with the use of blockchain technology integrated into the voting process. Both digital and physical voting methods have their limitations, making them unsuitable for widespread use. This evaluates the importance of finding a way to protect people's democratic rights. To foster confidence between voters and election officials, this article introduces a platform built on blockchain technology, which maximises system stability and transparency. Without the need for traditional polling places, the proposed technology lays the groundwork for digital voting using blockchain. A scalable blockchain may be supported by our suggested architecture via the use of adaptable consensus algorithms. The voting process is made more secure with the use of the Chain Security algorithm. When conducting a chain transaction, smart contracts provide a safe channel of communication between the user and the network. There has also been talk of the voting system's security being based on blockchain technology.","PeriodicalId":506407,"journal":{"name":"Journal of Scientific Research and Technology","volume":" 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140211179","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}

{"title":"Implications Of Loss Aversion And Investment Decisions","authors":"Sudha V Ingalagi, Mamata","doi":"10.61808/jsrt90","DOIUrl":"https://doi.org/10.61808/jsrt90","url":null,"abstract":"As a behavioral bias known as \"Loss Aversion,\" it states that people are more negatively affected by the prospect of losing money than they are by the prospect of gaining it. Concerning its effect on investors, the findings of the many research conducted on loss aversion have been contradictory. Individuals who engage in the Indian stock markets via brokerage companies are the target of this research, which seeks to answer the question, \"Is loss aversion real?\" and how does it influence investing decisions. This research also looks at the potential effects of gender, income, investing history, and risk perception on loss aversion. The research relied on primary data gathered via a structured questionnaire and analyzed using statistical procedures such as linear regression, independent t-test, and analysis of variance. According to the study's findings, loss aversion bias influences investors' investing choices and is significantly impacted by the respondents' gender.","PeriodicalId":506407,"journal":{"name":"Journal of Scientific Research and Technology","volume":"48 S231","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140224575","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}

{"title":"Gesture Identification Model In Traditional Indian Performing Arts By Employing Image Processing Techniques","authors":"Jyoti, Swaroopa Shastri","doi":"10.61808/jsrt89","DOIUrl":"https://doi.org/10.61808/jsrt89","url":null,"abstract":"Classical dance forms are an integral part of the Indian culture and heritage. Kathakali is an Indian classical dance composed of complex hand gestures, body moments, facial expressions and background music. Kathakali mudras are difficult to understand common peoples. There are 24 classes of hand gestures. The images of hand mudras of kathakali dance are collected from the dataset. The proposed work explores the possibilities of recognizing classical dance mudras in kathakali dance forms in india. This system has achieved an accuracy of 84% with Convolutional Neural Network for classifying the mudras.","PeriodicalId":506407,"journal":{"name":"Journal of Scientific Research and Technology","volume":"7 1‐2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140224538","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}

{"title":"Big Data and Machine Learning Based Early Chronic Kidney Disease Prediction","authors":"Asra Fatima, Shireen Fatima, Ayesha Kiran","doi":"10.61808/jsrt88","DOIUrl":"https://doi.org/10.61808/jsrt88","url":null,"abstract":"A chronic kidney disease, sometimes called a chronic renal disease, is characterized by a gradual decline in kidney purpose or abnormal kidney purpose which continues for months or even years. Patients with a domestic past of chronic kidney disease (CKD), high BP, or other kidney-related conditions are often the first to have chronic kidney disease (CKD) identified during screenings. Consequently, effective illness prevention and therapy rely on early prediction. Methods from the field of machine learning, including XGBoost, KNN, Decision Tree, and Random Forest, are being considered for use in this CKD project. The final product uses the fewest characteristics possible to determine whether the patient has chronic kidney disease (CKD).","PeriodicalId":506407,"journal":{"name":"Journal of Scientific Research and Technology","volume":"6 s1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262318","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}