International Journal of Emerging Science and Engineering最新文献

{"title":"Human Deep Neural Networks with Artificial Intelligence and Mathematical Formulas","authors":"Harsha Magapu, Magapu Radha Krishna Sai, Bhimaraju Goteti","doi":"10.35940/ijese.c9803.12040324","DOIUrl":"https://doi.org/10.35940/ijese.c9803.12040324","url":null,"abstract":"Human deep neural networks (HDNNs) are a type of artificial neural network that is inspired by the structure and function of the human brain. HDNNs are composed of multiple interconnected layers of neurons, which are able to learn complex patterns from data. HDNNs have been shown to be very effective at solving a wide range of problems, including image recognition, natural language processing, and machine translation. HDNNs are often used in conjunction with artificial intelligence (AI) to create intelligent systems that can mimic human cognitive abilities. For example, HDNNs have been used to develop AI systems that can understand and respond to human language, and that can learn from their experiences and improve their performance over time. Human deep neural networks (HDNNs) are a type of artificial neural network that is inspired by the structure and function of the human brain. HDNNs are composed of multiple interconnected layers of neurons, which are able to learn complex patterns from data. HDNNs have been shown to be very effective at solving a wide range of problems, including image recognition, natural language processing, and machine translation. HDNNs are often used in conjunction with artificial intelligence (AI) to create intelligent systems that can mimic human cognitive abilities. For example, HDNNs have been used to develop AI systems that can understand and respond to human language, and that can learn from their experiences and improve their performance over time.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":"33 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140362158","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 Comparative Study of Quality of Service (QoS) Metrics in Reactive Routing Protocols DSR and AODV in Manet","authors":"Diego A Sánchez C, John J. Tucker Yepez, Gabriela C. Durán Tapia, Heydi M. Roa López","doi":"10.35940/ijese.c2560.12030224","DOIUrl":"https://doi.org/10.35940/ijese.c2560.12030224","url":null,"abstract":"This study is based on the analysis of specific Quality of Service (QoS) metrics. The acquisition of these metrics was carried out by modifying codes in C++ language within the Ns3 network simulation software. The choice of the Random Way Point mobility model contributed to the generation of metrics, which were subsequently used in the evaluation and comparison of two selected protocols, DSR and AODV. These evaluations focused on critical parameters such as Throughput, Delay, and Jitter. To conduct meaningful comparisons, three different scenarios were designed, each characterized by the variation in the number of nodes used. This approach allowed for a comprehensive assessment of the protocols' effectiveness in different MANET network configurations. Ultimately, the selection of the most accurate protocols was based on a detailed analysis of metrics in various MANET scenarios. This process provided a deeper understanding of how DSR and AODV perform in specific environments, enabling the identification of more effective protocols according to the particular demands of each scenario.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":"129 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140423866","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":"Effects of Wastewater Sludge Addition on Fired Clay Bricks: Enhancing Performance and Sustainable Construction Practices","authors":"Anju P Babu, Sujin S, Anoop S S, Sooraj R Suresh, S. R. H. Nath","doi":"10.35940/ijese.b4323.12020124","DOIUrl":"https://doi.org/10.35940/ijese.b4323.12020124","url":null,"abstract":"This study explores the impact of incorporating wastewater sludge into fired clay bricks to improve their performance and promote sustainable construction. Physical, mechanical, and environmental properties of the sludge-amended bricks are investigated to assess their suitability as an alternative construction material. Lab tests are conducted to characterize the sludge and clay, and brick samples with varying sludge content are produced. Physical properties such as water absorption, are analyzed to determine the influence of sludge addition on the bricks' structural characteristics. Mechanical tests, including compressive and flexural strength evaluations, assess the performance of the sludge-amended bricks compared to traditional clay bricks. Additionally, environmental aspects are considered to evaluate the sustainability of the sludge-amended bricks. Life cycle assessment and carbon footprint analysis quantify the environmental benefits and drawbacks associated with their production and use. The findings of this study contribute to the knowledge of sustainable construction materials by exploring the potential utilization of wastewater sludge in fired clay brick production.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":"58 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140481406","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":"Multimodal Biometrics for Human Identification usingArtificial Intelligence","authors":"Boda Aruna, Dr. M Kezia Joseph","doi":"10.35940/ijese.a4278.1212123","DOIUrl":"https://doi.org/10.35940/ijese.a4278.1212123","url":null,"abstract":"Multimodal biometric systems combine multiple biometric modalities to enhance the accuracy and security of human identification. Instead of relying on a single biometric trait (such as fingerprint or face), these systems use a combination of different biometric characteristics to provide a more robust and reliable identification process. The key idea behind multimodal biometrics is that the fusion of diverse biometric data can overcome the limitations of individual modalities, resulting in higher accuracy and lower error rates.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":" 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139141530","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":"Ensemble Learning for Heart Disease Diagnosis: AVoting Classifier Approach","authors":"Yogesh S, Paneer Thanu Swaroop C, Ruba Soundar K","doi":"10.35940/ijese.j2555.11111223","DOIUrl":"https://doi.org/10.35940/ijese.j2555.11111223","url":null,"abstract":"Cardiovascular disease remains a serious public health problem internationally, responsible for a considerable number of fatalities. Early and correct detection of cardiovascular illness is crucial for optimal care and control of the condition. In this paper, we present an ensemble learning technique that includes voting classifiers to increase the reliability of cardiovascular disease diagnosis. We obtained a set of data from five cardiology databases, which included the Cleveland, Hungary, Switzerland, Long Beach VA and Statlog (Heart) datasets, which supplied us with a total of 1189 entries. We employed a feature engineering approach to extract relevant features from the dataset, enabling us to acquire vital information to enhance our model's performance. We trained and evaluated several machine learning algorithms, such as Random Forests, MLP, K-Nearest Neighbors, Extra Trees, XGBoost, Support Vector Machines, AdaBoost, Decision Trees, Linear Discriminant Analysis, and Gradient Boosting, and then incorporated these models using voting classifiers to produce more reliable and accurate models. Our findings reveal that the proposed ensemble learning process outperforms standalone models and conventional ensemble approaches, obtaining an accuracy rate of 91.4%. Our technique is likely to benefit clinicians in the early diagnosis of heart problems and improve patient outcomes. This work has major significance for the area of cardiology, indicating the possibility for machine learning approaches to boost both the reliability and accuracy of heart disease identification. The recommended ensemble learning technique may be adopted in hospitals to enhance patient care and eventually lessen the worldwide impact of cardiovascular disease. Further study is required to investigate the uses of predictive modeling in cardiology and other medical domains.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":"17 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139205717","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":"COVID-19 Sentiment Analysis using K-Means and DBSCAN","authors":"Smitesh D. Patravali, D. S. P. Algur","doi":"10.35940/ijese.l2558.11111223","DOIUrl":"https://doi.org/10.35940/ijese.l2558.11111223","url":null,"abstract":"The analysis of sentiment towards COVID-19 plays a crucial role in understanding public opinion. This research paper proposes sentiment analysis using K-means and DBSCAN clustering algorithms on the dataset of tweets related to COVID-19. Pre-processing and extraction of features is carried out using Term Frequency-Inverse Document Frequency (Tf-idf) to capture the weight of words in the dataset. K-means clustering is explored to group similar sentiments together, enabling the identification of sentiment clusters related to COVID-19. The DBSCAN algorithm is then employed to identify outliers and noise in the sentiment clusters. The evaluation metrics considered were accuracy, recall, F1-score, and precision. It was observed that DBSCAN was more effective in identifying underlying patterns in the data more accurately.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":"148 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139206392","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":"E-mail Fraud Detection","authors":"Arju Kumar, Saurav Kumar, K. Kumar, Dr. Bharat Bhushan Naib","doi":"10.35940/ijese.b7797.0811923","DOIUrl":"https://doi.org/10.35940/ijese.b7797.0811923","url":null,"abstract":"Spam issues have become worse on social media platforms and apps with the growth of IoT. To solve the problem, researchers have suggested several spam detection techniques. Spam rates are still high despite the use of anti-spam technologies and tactics, especially given the ubiquity of rogue e-mails that lead to dangerous websites. By using up memory or storage space, spam e-mails may cause servers to run slowly. One of the most essential methods for identifying and eliminating spam is filtering e-mails. To this end, various deep learning and machine learning technologies have been used, including Naive Bayes, decision trees, SVM, and random forest. E-mail and Internet of Things spam filters use various machine learning approaches and systems are categorized in this research. Additionally, as more people use mobile devices and SMS services become more affordable, the issue of spam SMS messages is spreading worldwide. This study suggests using a variety of machine learning approaches to detect and get rid of spam as a solution to this problem. According to the trial findings, the TF-IDF with Random Forest classification algorithm outperformed the other examined algorithms in accuracy %. It is only possible to gauge performance on accuracy since the dataset is imbalanced. Therefore, the algorithms must have good precision, recall, and F-measure.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":"518 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116241710","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":"Effect of Ovality on the Buckling Behavior of Thin-Walled Liquid-Filled Conical Tanks","authors":"Girmay Mengesha Azanaw","doi":"10.35940/ijese.b7816.0711823","DOIUrl":"https://doi.org/10.35940/ijese.b7816.0711823","url":null,"abstract":"This study investigates the influence of ovality on the buckling behavior of thin-walled liquid-filled conical tanks through comprehensive numerical simulations. The structural stability of conical tanks is of paramount importance in various engineering applications, such as storage vessels and aerospace structures. However, the presence of ovality, characterized by deviations from perfect circularity, can significantly affect the structural response and integrity of these tanks. To explore the effects of ovality, a finite element analysis (FEA) approach is employed, considering various ovality levels in the tank geometry. A comprehensive parametric study is conducted, varying key parameters such as tank dimensions, material properties, and liquid filling levels. The buckling behavior of the tanks is assessed by examining critical buckling loads and corresponding deformation modes. Results from the numerical simulations reveal that even small levels of ovality can substantially reduce the buckling load capacity of conical tanks. As ovality increases, the onset of buckling occurs at lower applied loads, leading to premature structural failure. The presence of liquid filling further exacerbates the buckling phenomenon, with the liquid sloshing effect amplifying the structural response. The study also investigates the influence of different materials on the buckling behavior of conical tanks subjected to ovality. It is found that the material stiffness and yield strength play a crucial role in determining the critical buckling load and mode shape. Furthermore, the effect of liquid fill level is explored, demonstrating that higher fill levels increase the vulnerability to buckling.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124524398","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":"Computer Forensics and Advanced Methodology","authors":"Dr. Oghene Augustine Onome","doi":"10.35940/ijese.g2552.0611723","DOIUrl":"https://doi.org/10.35940/ijese.g2552.0611723","url":null,"abstract":"The field of computer forensics emerged in response to the substantial increase in computer-related crimes occurring annually. This rise in criminal activity can be attributed to the rapid expansion of the internet, which has provided perpetrators with increased opportunities for illicit actions. When a computer system is compromised and an intrusion is detected, it becomes crucial for a specialized forensics team to investigate the incident with the objective of identifying and tracing the responsible party. The outcome of such forensic efforts often leads to legal action being taken against those accountable for the wrongdoing. The methodology employed in computer forensics continually evolves alongside advancements in crime approaches, particularly as attackers leverage emerging technologies. To ensure the accuracy of forensic investigations, it is imperative that the scientific knowledge underlying the forensic process be complemented by the integration of technological tools. A plethora of hardware and software options are available to facilitate the analysis and interpretation of forensic data, thereby enhancing the efficiency and effectiveness of investigations. While the fundamental objectives of computer forensics primarily involve the seamless preservation, identification, extraction, documentation, and analysis of data, the widespread adoption of this discipline is contingent upon the law enforcement community's ability to keep pace with advancements in computing technology. Furthermore, the prevalence of diverse computer devices resulting from the emergence of microcomputer technology also plays a crucial role in shaping the field of computer forensics. This research paper aims to provide a comprehensive overview of computer forensics, encompassing advanced methodologies and detailing various technology tools that facilitate the forensic process. Specific areas of focus include the analysis of encrypted drives, disk analysis techniques, analysis toolkits, investigations involving volatile memory, and the examination of captured network packets. By exploring these aspects, this paper aims to contribute to the existing body of knowledge in the field of computer forensics and support practitioners in their pursuit of effective investigative techniques.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116335401","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":"Genetic Algorithms: A Solution to Fiber Reinforced Composite Drilling Challenges","authors":"S. Bhardwaj, Prof. Neeraj Bhargava, Dr. Ritu Bhargava","doi":"10.35940/ijese.f2548.0511623","DOIUrl":"https://doi.org/10.35940/ijese.f2548.0511623","url":null,"abstract":"Natural fiber composites are a group of materials that have gained increasing attention in recent years due to their potential to replace traditional materials in various applications. However composite materials are made up of layers of fibers and resin that can separate from each other during drilling, leading to delamination. This paper proposes a multi-objective optimization approach for drilling natural fiber composites, considering three key drilling parameters: cutting speed, feed rate and tool geometry. The objective is to minimize delamination and thrust force. Multiple linear regression analysis is employed to develop the regression equations for each objective function, which are then optimized simultaneously using a multi-objective genetic algorithm (MOGA). The results demonstrate that the proposed approach can effectively identify the optimal drilling parameters that balance the trade-offs between the competing objectives. The proposed approach can be useful for improving the efficiency and quality of drilling natural fiber composites, which are increasingly used in various industrial applications.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132403485","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}