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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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An Extensive Machine Learning Framework Employing Ensemble Classifiers for Heart Disease detection

An Extensive Machine Learning Framework Employing Ensemble C...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： E. Chandralekha S. Vinodhini V Kandasamy P. Rama S Ravikumar T.R Saravanan Dept.of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of Information Technology Velammal Engineering College Chennai India Dept. of Information Technology Panimalar Engineering College Chennai India Dept. of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Dept. of Computational Intelligence SRM Institute of Science and Technology Chennai India

Heart disease is a significant global health challenge, impacting many lives and often being a leading cause of mortality and illness. Detecting heart disease accurately and promptly is essential for effective medical care. Due to the complex interplay of risk factors and the intricate nature of diagnosis, advanced technologies like machine learning are being explored. This study introduces a comprehensive framework that employs machine learning techniques for heart disease detection. The framework involves critical stages such as data preprocessing, feature selection, and training ensemble classifiers. Experimental results highlight the importance of specific attributes in each classifier, offering insights into predictions. Notably, attributes like oldpeak, thalach, ca, cp, and thal are key features in the Random Forest classifier, while Gradient Boosting underscores ca, cp, thal, oldpeak, and age. AdaBoost and the Extra Trees Classifier reveal similar significant attributes. Evaluation metrics emphasize the models’ robust performance, with the Extra Trees Classifier achieving the highest accuracy. This research contributes to a practical heart disease detection framework with potential for improving medical decision-making.

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Microwave-based Concentration detection system of Pulverized Coal

Microwave-based Concentration Detection System of Pulverized...

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Energy, Power and Electrical Engineering (EPEE), international conference on

作者： Cenjie Yuan Qunli Dong Shuai Li Xiongjie Jiang Feng Gao Zhenming Song Bing Tian Haoyu Tian Zhejiang Zheneng Jiahua Power Generator Co. Ltd Jiaxing China Zhejiang Energy Digital Technology Co. Ltd Hangzhou China

In light of the lack of consensus and effective means for measuring pulverized coal concentration in dilute-phase pneumatic conveying, this paper proposes a microwave-based pulverized coal concentration detection system. The sensor employs multiple electrodes with freely switchable transceiving states, enabling the construction of a full cross-section signal path and allowing insertion loss measurement. A mapping function utilizing L1 distance is established for insertion loss, mapping it to the mixed dielectric constant of the pulverized coal-air mixture, thereby estimating pulverized coal concentration. Simultaneously, a concentration classification algorithm is proposed. The effectiveness and accuracy of the sensor measurement are verified through concentration calibration and realtime classification. Experimental results demonstrate the system's ability to detect and classify various pulverized coal concentrations, offering novel insights into concentration measurement of gas-solid mixtures in dilute-phase pneumatic conveying conditions.

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Utilizing Social Media Platforms for Artificial intelligence-Based detection and Developing Effective Communications in Disaster-Affected Areas

Utilizing Social Media Platforms for Artificial Intelligence...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Ganesh Waghmare Saurabh Kumar R. Udhayakumar B. Raja Mannar Rajesh Singh Navneet Rawat Lexicon Management Institute of Leadership and Excellence Pune Maharashtra Invertis Institute of Computer Applications Invertis University Bareilly Department of English Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu Lecturer - Business Administration St.Theresa Intl College Thailand Uttaranchal Institute of Technology Uttaranchal University India Department of Management Studies Graphic Era Deemed to be University Dehradun Uttarakhand India

Disaster response organizations must have timely access to current and pertinent disaster information in order to formulate wise management decisions and decrease the impact of disasters. Social media has developed recently as a proactive avenue for disaster communications. It has made it easier for groups that deal with disasters to get a tonne of information in real time about them from the people that are affected. Researchers and professionals are now able to fully realise the significance of such a user-generated content for aiding sensible decision-making in catastrophe management thanks to advancements in artificial intelligence. This paper provides an overview of how artificial intelligence techniques are now being used to evaluate social media content connected to disasters in order to support disaster management at various stages. It gives a brief overview of a few increasingly digitally systems that use information from social media to manage disasters. This study also highlights a number of research opportunities and obstacles for handling disaster-related information on social media.

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Image Manipulation detection and Classification Using SIFT and SURF with Support Vector Machine Classifier

Image Manipulation Detection and Classification Using SIFT a...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Ghazi Mohamad Ramadan Zamen Latef Naser Sripelli Jagadish Rekha Phadke Muntather Almusawi College of MLT Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq

Enhance digital trust and integrity by mastering image forgery detection in machine learning. Uncover the hidden truth behind visuals, ensuring authenticity in a world inundated with digital content. Various image manipulation detection machines have been innovated in the past, but these models face limitations like demanding domain expertise, hyperparameter tuning, time-consuming processes, and overlapping issues. Initially employ the histogram equalization to enhance dataset images, reducing unwanted noise and generating synthetic data for class balance. The pre-processed images are then fed into the model for ongoing analysis. In this paper, propose a Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF)-based SVM for image manipulation detection and classification, achieving outstanding performance metrics on the CASIA 1.0 dataset: 0.96 accuracy, 0.8120 IOU score, 0.7710 MCC, 0.8850 F1 score, 0.98 precision, and 0.92 sensitivity. This model surpasses the capabilities of existing models, including the Two-Stream Boundary-aware Network, Discrete Cosine Transformation (DCT)-based multitask learning network, and Multilevel Local Binary Pattern Texture Descriptor.

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A Review on Unconstrained Real-Time Rotation-Invariant Face detection

A Review on Unconstrained Real-Time Rotation-Invariant Face ...

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international conference on Intelligent Communication and Computational Techniques (ICCT)

作者： Shubh Lakshmi Agrwal Sudheer Kumar Sharma Vibhor Kant Department of Computer Science & Engineering Manipal University Jaipur LNM Institute of Information Technology Jaipur India Department of Computer Science & Engineering LNM Institute of Information Technology Jaipur India Department of Computer Science & Engineering RGSC Banaras Hindu University Varanasi India

With the amazing growth of image and video databases, there is a vast need for intelligent systems to automatically understand and look at information since doing it by hand is getting very hard. Faces are significant in social interactions because they show the feelings and identity of a person. People are not much better than machines at recognizing different faces. The automatic face detection system is a key in head pose tracking, face verification, face recognition, face tracking, face animation, face modeling, facial expression recognition, age and gender recognition, and behavior analysis in a crowd. Face detection is a way for a computer to find out the size and location of a face in an image. Face detection has been an outstanding issue in computer vision literature. This paper provides an overview of pose and rotation invariant face detection approaches with architecture designs and performance on popular benchmark datasets. The benchmark datasets used for face detection are listed as their key features. This paper also talks about different applications and challenges with face detection. Also, we set up special discussions on the practical aspects of making a face-detection system that works well. We end this paper by suggesting a few promising directions for future research.

关键词： Databases Face recognition Computer architecture Benchmark testing Feature extraction Real-time systems Magnetic heads

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Real Time Object detection with Image Recognition and Web Scraper

Real Time Object Detection with Image Recognition and Web Sc...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Shweta Chauhan Rahul Kumar Apex Institute of Technology (CSE) University Centre for Research and Development Chandigarh University Mohali India Department of Electronics and Communication Engineering Malla Reddy Engineering College (MREC) Hyderabad India

The problem of price discrepancies between physical stores and online retailers has been a growing concern for consumers and businesses alike. In this work, our proposed real-time object recognition method has a lot of potential for resolving the issue of price differences between traditional retailers and online suppliers. Our system has the potential to help customers make wise purchase decisions and save money by giving them a quick way to spot possible cost reductions. We anticipate that our research will serve as a catalyst for further research and technological developments in the area of price comparison systems, creating a more level playing field and competitive market.

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On the Development of a Wearable Multi-Spectral Photoplethysmographic Device for Heart Rate detection

On the Development of a Wearable Multi-Spectral Photoplethys...

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international Symposium on Artificial intelligence and Signal Processing (AISP)

作者： V. Devaki Madan Kumar Lakshmanan T. Jayanthi Department of Biomedical Engineering SRM Institute of Science and Technology Kattankulathur India Central Electronics Engineering Research Institute (CSIR-CEERI) Chennai India

Wearables, including fitness bands and smartwatches, can monitor regular photoplethysmographic signals, an optical measurement of the arterial pulse wave strongly impacted by the heart and blood vessels. Recently, there has been an increased demand for a wearable to detect cardiovascular abnormalities using photoplethysmography. The Multi-Spectral Photoplethysmograph (MSPPG) evaluates the peripheral blood volume pulsations at varying vascular depths. The sensing technology based on Multi-Spectral Photoplethysmography is regarded as superior to the commonly available photoplethysmography technology since it reduces motion artefacts and provides detailed information at different wavelengths. The Heart Rate (HR), a strong indicator of cardiac abnormalities, is detected using the Multi-Spectral Photoplethysmography (MSPPG) by utilising the optical wearable sensor SFH7072 with an analog front end and microcontroller; which aids in the decision-making process, particularly in the case of Cardio-Vascular diseases. The investigation is carried out by detecting heart rate using MSPPG in healthy volunteers by employing Electrocardiography (ECG) as a gold standard reference. A comparison of 50 healthy volunteers’ signal recordings signifies the highest degree of correlation between heart rate measured from MSPPG and ECG. The results indicate that MSPPG yields accurate peak intervals where the heart rate can be computed in healthy participants under ideal circumstances, emphasizing that this technique may be a possible alternative to ECG and that the sensor can be further employed for cardiovascular disease diagnosis.

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Forest Fire detection via the Internet of Things-Based systems

Forest Fire Detection via the Internet of Things-Based Syste...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Prafful Negi Samta Kathuria Rajesh Singh Vikrant Pachouri Namrata Kathuria Uttaranchal Institue of Technology Uttaranchal University Dehradun India Division of Research & Innovation Uttaranchal University Dehradun India Division of Research & Innovation UIT Uttaranchal University Dehradun India Law College Dehradun Uttaranchal University Dehradun India

One of the disasters caused by natural or human causes directly affect the economy, society, and environment on a large scale and makes it more difficult to meet some of the Sustainable Development Goals. The promotion of sustainable forest management help to achieve environmental sustainability, a goal that has gained international attention. Internet of Things (IoT), a revolutionary technology of the fourth industrial revolution, brings us toward achieving sustainable forest management by preventing hazards from occurring in the forest by employing a large number of smart devices to gather data, monitor the forest, and timely detection of fire. The current study extensively assesses how IoT is utilized to design various tools to promptly detect fire and manage it. Finally, based on the analysis, the challenges and suggestions for future improvement are made with regard to attaining forest sustainability by preventing forest fires and making the IoT based system more reliable.

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Identifying Unusual Human Movements Using Multi-Agent and Time-Series Outlier detection Techniques

Identifying Unusual Human Movements Using Multi-Agent and Ti...

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Advanced Research in Computing (ICARC), international conference on

作者： Ppg Dinesh Asanka Chathura Rajapakshe Masakazu Takahashi Department of Industrial Management University of Kelaniya Kelaniya Sri Lanka Graduate School of Innovation and Technology Management Yamaguchi University Yamaguchi Japan

This research paper has introduced knowledge-driven multi-agent technology for automated machine learning in time series analysis in the context of human mobility. The main objective of this research is to identify unusual human mobility using Time Series outlier detection techniques with a more efficient multi-agent system. detection of unusual human movement can be helpful for many domains, such as security, marketing, and health. A mobile dataset in Hiroshima, Japan between 2019-December to 2020-November was used for this research. The mobile dataset was converted to time series for multiple locations in Hiroshima, Japan. Since many different parameters are selected for time series, the message space multi-agent technique is used. Sub agents are introduced for duplicate removal, missing data replacement, and outlier detection. Multiple processing agents and a control agent were introduced to predict the missing values to improve the efficiency of the model. Finally, using the Seasonal-Trend decomposition techniques, unusual movements are identified, and unusual human movements are plotted with the holidays. Multiple outlier points were detected for all the locations, and there were more than a hundred outlier points were detected for the selected locations.

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Bangladeshi Fresh-Rotten Fruit & Vegetable detection Using Deep Learning Deployment in Effective Application

Bangladeshi Fresh-Rotten Fruit & Vegetable Detection Using D...

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Computer Communication and Artificial intelligence (CCAI), international conference on

作者： Md Abrar Hamim Jeba Tahseen Kazi Md Istiyak Hossain Nasrin Akter Umme Fatema Tuj Asha Department of Computer Science Daffodil International University Dhaka Bangladesh

Finding rotten fruits and vegetables has been important, especially in the agricultural industry. Computer vision has significant applications in the automation of damaged, freshness detection of fruits and vegetables. In recent decades, the farming sector has discovered computer machine vision and image processing technology to be more and more beneficial, particularly for implementations in quality control by identifying rotten and freshness. Farmers cannot contribute effectively between fresh and rotten fruits, vegetables because this is mainly done by people. People tire out after performing the same task for several days, whereas robots do not. By identifying weaknesses in agricultural product, the study suggested a technique for minimizing human effort and worktime. Vegetables and fruits with defects might affect healthy fruits if they are not identified in time. As an outcome, we put up a methodology to stop rottenness from spreading. The suggested model detects between fresh and decaying fruits and vegetables depending on the input fruit and vegetable photos. In this work, we used six different types of fruits and vegetables like carrot, potato, calabash, cucumber, eggplant, and cauliflower, as well as fruits likes mango, banana, star fruit, jackfruit, guava, and papaya. This study discusses multiple image processing methods for rottenness categorization of fruits and vegetables. A Convolutional Neural Network (CNN), KNN, and SVM are used to gather the features from the data fruit and vegetable photos. On Google and Kaggle datasets, the efficiency of the suggested model is evaluated, and CNN model shows the greatest accuracy which is 95 percent.

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