International Journal of Innovative Research in Computer Science and Technology最新文献

{"title":"Design And Development of Sketch Based Image Retrieval Using Deep Learning","authors":"Dr S. A. Talekar, Shravani A. Lajurkar, Divya S. Patil, Rutika A. Benke, Pranjal A. Kunde","doi":"10.55524/ijircst.2024.12.3.3","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.3.3","url":null,"abstract":"In this cutting edge, the common wrong doing rate is expanding day-by-day and to manage up with this the criminal divisions as well ought to discover ways in which would speed up the by and large preparation and offer assistance in bringing one to justice. In response to rising crime rates, law enforcement agencies are turning to advanced algorithms capable of matching freehand sketches with images in databases. These algorithms, utilizing sophisticated feature extraction techniques and deep learning models, significantly enhance identification accuracy. By leveraging Sketch based image retrieval technology, investigations are expedited, leading to quicker suspect apprehensions and resolution of criminal cases. This results in improved public safety and justice outcomes, as well as more efficient law enforcement practices overall.","PeriodicalId":502773,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141024310","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":"Intelligent Plant Disease Diagnosis Using Machine Learning Techniques- A Review","authors":"Anuj Kumar Kem, Ayush Chauhan, Mohan Agnihotri, Aniruddh Kumar","doi":"10.55524/ijircst.2024.12.3.10","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.3.10","url":null,"abstract":"No matter how we look at it, there are no chances to live without the flora surrounding us. One can be troubled by a broad range of diseases that attack the integrity of his or her health. Basically, all the plant parts are fruits, stems, roots, leaves, and so on. The time and money in terms of having successfully figured out the disease of a plant are much less than that if a diagnosis error has been made. Sustained economic losses caused by plant disease are due to the facilitation of rot production, which involves the reduction of agricultural product yields and quantities. Creating measures that would halt the destruction of crops due to plant diseases is essential since the contributing factor of 70% of agricultural produce to GDP is high. This group of illnesses must be watched closely since the diseases start as soon as the plants have begun their growing process. The conventional approach to surveillance at this point specifically is to carry out an examination, which is quite costly in terms of money. Automated for faster and more effective processing of this operation. Many researchers, by using various methods, have created networks that are mostly exemplified in diverse forms. It is also worthwhile to note that in the field of agriculture, it is very important that the plants are sorted by type. Diagnosis on pathology datasets with the aid of image feature extraction and transformation methods that are appropriate to the illness.","PeriodicalId":502773,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"53 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141142334","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":"Charging Ahead- Addressing Key Barriers to Electric Vehicle Market Penetration in India","authors":"Nitya Yadav, Pratibha, Ramendra Tripathi, Neha, Vivek Kushawaha","doi":"10.55524/ijircst.2024.12.3.9","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.3.9","url":null,"abstract":"The transition to electric vehicles (EVs) represents a pivotal step towards sustainable transportation, particularly in the context of India's burgeoning population and rapid urbanization. Despite significant potential, the widespread adoption of EVs in India is hampered by various barriers, including infrastructure limitations, policy gaps, high upfront costs, and consumer perceptions. This research paper aims to analyze these key barriers and propose strategies to accelerate the market penetration of EVs in India. Through a comprehensive literature review, policy analysis, and examination of case studies, this paper identifies the challenges hindering EV adoption and offers recommendations for addressing them. Proposed strategies include infrastructure development, policy interventions, consumer awareness initiatives, and technological innovations. By implementing these recommendations, India can overcome barriers to EV adoption and realize the environmental and economic benefits associated with clean mobility.","PeriodicalId":502773,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"2016 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141026972","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":"Enhanced Counterfeit Detection of Bangladesh Currency through Convolutional Neural Networks: A Deep Learning Approach","authors":"Abhijit Pathak, Arnab Chakraborty, Minhajur Rahaman, T. Rafa, Ummay Nayema","doi":"10.55524/ijircst.2024.12.2.2","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.2.2","url":null,"abstract":"Counterfeiting poses a significant threat to the stability of Bangladesh's currency, the Taka, necessitating advanced methods for detection and prevention. This paper presents an innovative approach to counterfeit detection using Convolutional Neural Networks (CNNs), a deep learning technology. Explicitly focused on Bangladesh's currency, this method aims to enhance the accuracy and efficiency of counterfeit detection by leveraging the power of artificial intelligence. The proposed approach involves training CNNs on a dataset of authentic and counterfeit Bangladeshi currency images, allowing the network to learn intricate features and patterns indicative of counterfeit notes. By exploiting the hierarchical structure of CNNs, the system can automatically extract discriminative features from currency images, enabling robust detection of counterfeit banknotes. The CNN-based approach offers several advantages compared to traditional methods, which often rely on manual inspection or rule-based algorithms. It can handle complex visual information more accurately and efficiently, making it well-suited for detecting subtle counterfeit features. Furthermore, the adaptability of CNNs allows for continuous learning and improvement, ensuring resilience against evolving counterfeit techniques. The efficacy of the proposed method is validated through extensive experimentation and evaluation, demonstrating its superior performance in detecting counterfeit Bangladesh currency notes. By harnessing the capabilities of deep learning, this approach not only enhances the security of Bangladesh's financial system but also serves as a scalable solution applicable to other currencies and regions facing similar challenges. In conclusion, the integration of Convolutional Neural Networks represents a significant advancement in counterfeit detection technology, offering a powerful and versatile tool for safeguarding the integrity of Bangladesh's currency and combating financial fraud on a global scale.","PeriodicalId":502773,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"172 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140279597","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":"Research on the Application of Semantic Network in Disease Diagnosis Prompts Based on Medical Corpus","authors":"Yufeng Li, Weimin Wang, Xu Yan, Min Gao, Mingxuan Xiao","doi":"10.55524/ijircst.2024.12.2.1","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.2.1","url":null,"abstract":"Portion of the causes of medical errors in outpatient clinics are incorrect treatment resulting from misdiagnosis. Misdiagnosis between diseases is often caused by similar and indistinguishable symptoms. Currently, disease knowledge and related symptom words that are prone to misdiagnosis are scattered in various medical literature or open online databases. Therefore, it is possible to merge symptom words of related diseases, build an ontology based on the semantic relationship between symptoms, and associate the association between diseases and symptoms. This project finally established the \"Disease Symptoms\" Semantic Network (DSSN). Using DSSN as a basic data set can serve as prompts for diseases that are easily misdiagnosed, assisting doctors in accurately diagnosing diseases. This plays a significant role in reducing the misdiagnosis rate.","PeriodicalId":502773,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"29 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140268286","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":"Enhancing Liver Segmentation: A Deep Learning Approach with EAS Feature Extraction and Multi-Scale Fusion","authors":"Weimin Wang, Yufeng Li, Xu Yan, Mingxuan Xiao, Min Gao","doi":"10.55524/ijircst.2024.12.1.6","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.1.6","url":null,"abstract":"Deep learning technology have been broadly used in segmentation tasks of liver. To address the limitation of suboptimal segmentation for small targets, an end-to-end EAS(ECA-Attention and Separable convolution) U-Net is proposed based on deep learning. The basic module employs depthwise separable convolutional modules instead of convolutional modules to reduce the parameters count and enhance the extraction of deep-layer information. The pyramid module based on Efficient Channel Attention (ECA) is utilized to obtain different receptive fields. And that model can overcome the limitation of the U-Net model with a single receptive field and improve the segmentation capability for targets of different sizes. A deep supervision module with multi-scale output fusion is designed to extract detailed information about liver with high quality. The proposed method is tested on the Liver Tumor Segmentation (LiTS) dataset for liver segmentation, achieving a Dice Similarity Coefficient (DSC) of 92.20% for liver segmentation. Compared to existing models, the proposed method demonstrates higher accuracy in liver segmentation.","PeriodicalId":502773,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"28 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140519100","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 Cyber Security Threats Using Machine Learning Techniques","authors":"Ranjana B Nadagoudar","doi":"10.55524/ijircst.2024.12.1.7","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.1.7","url":null,"abstract":"Nowadays malware detection is a problem that researchers have tried to solve for so many years by using enormous type of methods. The behaviors of two given malware variants remain similar, although their signatures could also be distinct. The proposed project mainly concentrates on classifying the malware families by considering the malware API sequence or API commands. This type of classification is helpful for the analyst as it helps them to get a better insight into the functioning of the malware.","PeriodicalId":502773,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"14 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140519948","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":"Revitalizing Infrastructure: Assessing Concrete Jacketing for Reinforced Concrete Column Rehabilitation","authors":"S. A. R. Sidik Hasibuan","doi":"10.55524/ijircst.2024.12.1.8","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.1.8","url":null,"abstract":"The rehabilitation of deteriorating civil engineering infrastructure, encompassing bridges, buildings, columns, beams, supporting beams, marine structures, and roads, presents a formidable challenge in contemporary engineering practice. As society's demand for upgraded infrastructure escalates, the imperative to address deteriorating structures becomes increasingly urgent. Consequently, the rehabilitation of existing infrastructure has been identified as a paramount concern warranting immediate attention. Among the myriad techniques employed, retrofitting columns with concrete jacketing stands out as one of the most prevalent methods for enhancing column strength. This investigation delves into the efficacy of concrete jacketing as a means of revitalizing reinforced concrete columns. The results evince a substantial augmentation in column capacity following retrofitting, underscoring the potential of such methods to bolster the load-bearing capabilities of reinforced concrete columns. Furthermore, the analysis reveals a more uniform stress distribution in retrofitted columns compared to their unmodified counterparts, thereby mitigating issues related to uneven stress distribution. In summation, the findings underscore the considerable promise of concrete jacketing in fortifying the strength, structural integrity, and longevity of deteriorated infrastructure.","PeriodicalId":502773,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"29 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522702","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":"Artificial Intelligence in Medical Science: The New Age of Healthcare","authors":"Zahra Jabeen, Khushboo Mishra, Binay Kumar Mishra","doi":"10.55524/ijircst.2024.12.1.3","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.1.3","url":null,"abstract":"AI showcases an ocean of opportunities to health care, enhancing a variety of common medical practices–from identifying the best treatment plans for patients suffering with critical illnesses to diagnosing ailments. Artificial Intelligence can help foretell health trajectories, self-operate administrative tasks and entrust the treatments. This research article will discuss emerging and current AI tools present for accelerating patient care and their possible benefits, challenges associated with the use of these tools and policy options to address challenges or improve benefits of the use of these tools. Recent breakthroughs in the application of Artificial Intelligence in healthcare has been outlined. The probable direction of AI augmented healthcare system in future has also been discussed.","PeriodicalId":502773,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"154 1-2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140516872","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":"Applications of Machine Learning in Predictive Analysis and Risk Management in Trading","authors":"Kavin Karthik V","doi":"10.55524/ijircst.2023.11.6.4","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.6.4","url":null,"abstract":"The stock market is considered the primary domain of importance in the financial sector where Artificial Intelligence combined with various algorithmic practices empowers investors with data-driven insights, enhancing decision-making, predicting trends, and optimizing risk management for more informed and strategic financial outcomes. This research paper delves into the real-world applications of machine learning and algorithmic trading, observing their historical evolution together and how both of these can go hand in hand to control risk and forecast the movement of a stock or an index and its future. The research is structured to provide comprehensive insights into two major subdomains in the application of AI in algorithmic trading: risk management in equity markets and predictive analysis of stock trends through the application of machine learning models and training the current existing data which is feasible and training them with respect to historical scenarios of various market trends along with various fundamental and technical analysis techniques with the help of various deep learning algorithms. For risk management of a portfolio in finance, various machine learning models can be employed, depending on the specific needs and goals of the portfolio manager or risk analyst and implementing various value-at-risk algorithms along with deep learning techniques in order to assess risk at particular trade position and to manage volatile trades at unprecedented situations. The significance of this research paper lies in its practical applicability, offering real-world solutions to enhance trading strategies and decision-making processes with a focus on mitigating risk and capitalizing on market opportunities and also giving clear insights with respect to the current practical limitations of application of the provided solution and future scope to overcome the same.","PeriodicalId":502773,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139271214","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}