International Journal of Engineering and Computer Science最新文献

A FRAMEWORK FOR MANAGEMENT OF LEAKS AND EQUIPMENT FAILURE IN OIL WELLS 油井泄漏和设备故障管理框架

International Journal of Engineering and Computer Science Pub Date : 2024-07-23 DOI: 10.18535/ijecs/v13i07.4842

Dennis, T. L., A. V I E, Emmah, V. T.

{"title":"A FRAMEWORK FOR MANAGEMENT OF LEAKS AND EQUIPMENT FAILURE IN OIL WELLS","authors":"Dennis, T. L., A. V I E, Emmah, V. T.","doi":"10.18535/ijecs/v13i07.4842","DOIUrl":"https://doi.org/10.18535/ijecs/v13i07.4842","url":null,"abstract":"Oil is a precious and critical natural energy resource that is used in numerous ways to drive various industries worldwide. The extraction of oil from underground reservoirs is a complex process that requires a lot of planning, careful execution, and risk management. In this paper, CNN is employed to extract relevant features from sensor primary data collected from various wells. Detecting undesirable events such as leaks and equipment failure in oil wells is crucial for preventing safety hazards, environmental damage and financial losses, making it challenging to identify issues in a timely and accurate manner. This dissertation describes a hybrid model for detecting undesirable events in oil and gas wells using a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) techniques. The CNN architecture enables effective information extraction by applying convolutional layers and pooling operations to identify patterns and spatial dependencies in the data. The extracted features are then fed into an LSTM network, which can capture temporal dependencies and learning long-term patterns. By utilizing LSTM, the model can effectively analyse the time series data and detect the occurrence of undesirable events, such as abnormal pressure, fluid leakage, or equipment malfunction, in oil and gas wells. The hybrid model leveraging CNN for feature extraction and LSTM for detecting undesirable events in the oil and gas industry presents a comprehensive approach to enhance well monitoring and prevent potential hazards. Achieving high accuracy rates of 99.8% for training and 99.78% for testing demonstrates the efficacy of the proposed model in accurately identifying and classifying undesirable events in oil and gas wells.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"30 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813031","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}

引用次数: 0

Predictive Analytics for Demand Forecasting: A deep Learning-based Decision Support System 需求预测分析：基于深度学习的决策支持系统

International Journal of Engineering and Computer Science Pub Date : 2024-07-21 DOI: 10.18535/ijecs/v13i07.4853

Saurabh Kumar, Mr. Amar Nayak

引用次数: 0

A Model for Detection of Malwares on Edge Devices 边缘设备恶意软件检测模型

International Journal of Engineering and Computer Science Pub Date : 2024-07-21 DOI: 10.18535/ijecs/v13i07.4846

Nwagwu, C .B., Taylor O. E., Nwiabu N.D

{"title":"A Model for Detection of Malwares on Edge Devices","authors":"Nwagwu, C .B., Taylor O. E., Nwiabu N.D","doi":"10.18535/ijecs/v13i07.4846","DOIUrl":"https://doi.org/10.18535/ijecs/v13i07.4846","url":null,"abstract":"Abstract- Malware detection is a significant challenge in today's digital landscape. As new forms of malware are continuously being developed, traditional detection techniques often fall short due to their inability to detect these new strains. This paperintroduces meaningful features that effectively capture various types of malware, including viruses, worms, Trojans and Ransomware on Edge devices. The paper used a model that implemented Random forest classifier for feature selection and a support vector machine (SVM) model for Malware detection. Object-Oriented Analysis and Design (OOAD) methodology was used to as the design methodology, which involved identifying and modeling the different components of the system and their interactions. The system was developed using Python programming language, with an emphasis on model deployment via Python Flask for web-based testing and execution. The experimental results demonstrate the effectiveness of the proposed systems when compared with other existing system. The result gotten from proposed system is better than that of the existing system by achieving a detection accuracy of 99.98% which is better than existing techniques. This dissertation presents a promising direction for improving malware detection using support vector machine (SVM) model and highlights the potential for collaborative learning approaches to overcome the challenges of traditional centralized approaches. This result simulates edge device that performs malware detection. It measures the latency for each detection and prints whether the latency is high or low. After the simulation, it plots a graph to visualize the latency over multiple requests. Which shows that the proposed model had low latency between 0.25secs to 0.15 secs on multiple requests.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"33 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141818537","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}

引用次数: 0

Data-Driven Approach to Automated Lyric Generation 自动歌词生成的数据驱动方法

International Journal of Engineering and Computer Science Pub Date : 2024-07-21 DOI: 10.18535/ijecs/v13i07.4839

Jeyadev Needhi, D. Kk, Vishnu G, Ram Prasath G

引用次数: 0

ENHANCE DOCUMENT VALIDATION UIPATH POWERED SIGNATURE VERIFICATION 加强文件验证 uipath 支持签名验证

International Journal of Engineering and Computer Science Pub Date : 2024-07-17 DOI: 10.18535/ijecs/v13i07.4851

Mrs. K. Gowri, A. Aswath, A. P. Adarsh, R. S. K. Gowtham Balaji

引用次数: 0

Feature value quantization and reduction process for predicting heart attack possibility and the level of severity by a machine learning model 通过机器学习模型预测心脏病发作可能性和严重程度的特征值量化和还原过程

International Journal of Engineering and Computer Science Pub Date : 2024-07-17 DOI: 10.18535/ijecs/v13i07.4831

Md Zawharul Islam, Md. Atahar Ishrak, A. H. M. Kamal

{"title":"Feature value quantization and reduction process for predicting heart attack possibility and the level of severity by a machine learning model","authors":"Md Zawharul Islam, Md. Atahar Ishrak, A. H. M. Kamal","doi":"10.18535/ijecs/v13i07.4831","DOIUrl":"https://doi.org/10.18535/ijecs/v13i07.4831","url":null,"abstract":"Heart disease is a prevalent condition nowadays that, if undiagnosed, can be deadly. To predict heart disease, \u0000researchers designed many machine learning models. In this study, we propose a model that chooses fewer attribute columns for training, and we use these chosen features to determine the heart problem severity. Correlation Repeated Heat map and Information Gain were used for selecting the features. To train our model we used the UCI Cleveland heart disease dataset. We removed duplicate data to improve the accuracy score, and we also encoded the categorical data collection using the OneHot(OH) encoding method, which can improve prediction accuracy. Support Vector, Logistic Regression, K-Nearest Neighbour, Naive Bayes, Decision Tree, Random Forest, Adaboost, and XGBoost are the eight classifier algorithms that are used in this process overall. Based on repeated heat map correlation, we compare the accuracy score each time. In this proposed method, the Adaboost classification algorithm used by the fbs row heat map achieves the highest accuracy for heart disease detection and it is 92%. By choosing features according to the information gain value, we compare the accuracy score each time in information gain. For both XGBoost and Logistic Regression, we got an accuracy score of 93.44%. However, compared to the XGBoost classification technique, Logistic Regression requires less time. Accuracy, precision, recall, f1-score, sensitivity, specificity, and the AUC of ROC charts were used to evaluate the performance of the model. Overall, the results of our model demonstrate that it is reliable and accurate in identifying cardiac disease and its level of severeness.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141830728","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}

引用次数: 0

Enhancing Plant Disease Detection through Transfer Learning by Incorporating MemoryAugmented Networks and Meta-Learning Approaches 结合记忆增强网络和元学习方法，通过迁移学习提高植物病害检测能力

International Journal of Engineering and Computer Science Pub Date : 2024-07-14 DOI: 10.18535/ijecs/v13i07.4852

Dr. Mohana Priya C

引用次数: 0

Performance Optimization of Voice-Assisted File Management Systems 语音辅助文件管理系统的性能优化

International Journal of Engineering and Computer Science Pub Date : 2024-07-14 DOI: 10.18535/ijecs/v13i07.4854

Jeyadev Needhi, Ram Prasath G, Vishnu G, D. Kk

引用次数: 0

Machine Learning Algorithms for Predictive Maintenance in Autonomous Vehicles 用于自动驾驶汽车预测性维护的机器学习算法

International Journal of Engineering and Computer Science Pub Date : 2024-07-11 DOI: 10.18535/ijecs/v13i01.4786

Chirag Vinalbhai Shah

引用次数: 0

The Convergence of AI, ML, and IoT in Automotive Systems: A Future Perspective on Edge Computing 人工智能、ML 和物联网在汽车系统中的融合：边缘计算的未来视角

International Journal of Engineering and Computer Science Pub Date : 2024-07-11 DOI: 10.18535/ijecs/v11i05.4673

Dilip Kumar Vaka

{"title":"The Convergence of AI, ML, and IoT in Automotive Systems: A Future Perspective on Edge Computing","authors":"Dilip Kumar Vaka","doi":"10.18535/ijecs/v11i05.4673","DOIUrl":"https://doi.org/10.18535/ijecs/v11i05.4673","url":null,"abstract":"Edge computing, where sensing, control, and intelligent processing occur near where data is acquired, is poised to be a fundamental enabler of several imminent disruptive future computing paradigms for emerging applications such as CPS, IoT, and more sophisticated AI-driven services. In this context, we posit the convergence of AI, ML, and IoT in automotive systems, the infrastructure required to enable it, and where edge computing will play a pivotal role in the real-world deployment of this ecosystem. We also review a few digital infrastructure technologies that can vastly enhance these next-generation digital automotive systems. This is examined through the investigation of real-world scenarios provided by our partner companies, the prominent Consumer Electronics Show (CES), and other sources. First, it is demonstrated through several industrial benchmarks that the proposed digital infrastructure technologies provide significant alleviation in terms of application accuracy, and at times even take the benefits beyond even 1x equivalent DNN accelerator-based systems in resource-constrained edge computing environments. After this, the challenges of designing and deploying them in real-world automotive systems are outlined. The paper concludes with the verifiable thesis that edge computing technologies need to play a significant role in the next-generation digital automotive system development so that ML-driven AI systems of the future are designed and deployed successfully in the field and can deliver their intent of providing superior user experience, enhanced safety, and convenience.\u0000 ","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"138 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655885","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}

引用次数: 0