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Automatic Identification of Teff (Eragrostis Tef) Flour Adulteration Using Machine Learning 10

Automatic Identification of Teff (Eragrostis Tef) Flour Adul...

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Bochera, Zekiyos Abayneh Gizaw, Solomon Clement, Onime Addis Ababa University Dept. of Computational Data Science Addis Ababa Ethiopia Addis Ababa University Dept. of Computer Science Addis Ababa Ethiopia Trieste Italy

ISBN: (纸本)9798331510138

Food adulteration, the act of mixing foodstuffs with lower-quality foodstuffs or other food and non-food items, has become a global issue, particularly in developing countries like Ethiopia. This is a significant problem as it can lead to nutritional deficiencies and in some cases, illnesses, diseases or even death. The identification of adulterated food typically requires physio-chemical laboratory tests that vary depending on the type and level of adulterant. Teff flour, a daily staple food in Ethiopia, is often adulterated with wood flour and gypsum powder. In this research work, we utilized computer vision techniques to extract textural and color features from images of both adulterated and pure Teff flour. We then trained a Support Vector Machine (SVM) based machine learning model to identify adulterated Teff flour and predict the level of adulteration. The presented results show varying levels of prediction accuracy depending on the type of Teff flour, the level of adulteration. The developed model was also found to provide good results with images sizes ranging from 128 × 128 to 2048 × 2048 pixels. The results show overall model accuracy was above 90% when the adulteration level of red Teff flour is more than 25% and above 84% when the adulteration level of white wood flour was above 25%. © 2024 IEEE.

关键词： Food chemistry

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Symptoms-based Classification of Disease Using Various ML Algorithms and Interpretation using LIME and SHAP KERNELS 4

Symptoms-based Classification of Disease Using Various ML Al...

引用

4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Soujanya, K.J. Kodipalli, Ashwini Gosai, Sanjeev Sneha, B.J. Rao, Trupthi Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bangalore India

ISBN: (纸本)9798350354218

Using a variety of machine learning techniques, this research study suggests a unique method for classifying diseases using symptom-based analysis. To improve model transparency and comprehension, the study makes use of the interpretability tools, SHAP kernels and LIME. The research attempts to close the gap between interpretability and prediction accuracy in medical diagnosis by combining two approaches. A thorough assessment of symptom-based categorization is ensured by the use of many machine learning methods. The application of LIME and SHAP kernels aids in the clarification of model predictions and provides information on the primary symptoms that impact illness categorization. This study advances the creation of accurate and comprehensible illness categorization models, promoting confidence in the use of machine learning methods in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Mitigating Imbalanced data in Online Social Networks using Stratified K-Means Sampling 8

Mitigating Imbalanced Data in Online Social Networks using S...

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8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Kathiravan, P. Shanmugavadivu, P. Saranya, R. Central University of Tami Nadu Department of Computer Science Thiruvarur India The Gandhigram Rural Institute Dept. of Computer Science and Applications India

ISBN: (纸本)9798350399646

The K-means clustering technique is widely used in many fields, such as anomaly detection, customer segmentation, cyber-physical system, medical diagnoses, sentiment analysis, fraud detection, and other similar tasks. We used this k-means technique in handling imbalanced datasets by preserving minority class structure using the stratified resampling technique. For this experimental study, we used a benchmark dataset from Kaggle. It is a labeled dataset collected from online social media regarding fake news. This proposed model, The Stratified k-means Sampling (SKMS), is compared with Synthetic Minority Oversampling Technique (SMOTE) by empirically experimenting using different machine learning algorithms. Random Forest (RF) algorithm gives significant accuracy, and Support Vector Classification (SVC) produces a better F1-score than other algorithms. The SMOTE technique was compared with the same dataset using these same algorithms. While SKMS seeks to preserve the structure of the minority class, SMOTE aims to diversify the minority class by interpolating between existing samples. Depending on the dataset, one might be more relevant than the other. © 2023 IEEE.

关键词： Classification (of information)

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IoT-Enabled Covert Surveillance Camera Identification through IR Sensor 2

IoT-Enabled Covert Surveillance Camera Identification throug...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ruksana, Shaik Imran, Shaik Farheen, Shaik Fatima Kireeti, Krovi Mohan Sailaja, K.L. VR Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India VR Siddhartha Engineering College Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350371314

A rising number of spy cameras installed in sensitive locations globally poses an escalating threat to personal privacy. Detecting these small and well-concealed cameras with the naked eye is challenging. Despite the abundance of Android apps, accurate detection remains elusive, with many requiring subscriptions and some charging users without prior notice. Furthermore, most IoT devices are designed to detect wired cameras, overlooking the prevalence of wireless spy cameras. Existing studies often rely on impractical methods, such as wireless transmission detection. The primary objective of this paper is to develop a cost-effective, scalable, robust, and user-friendly spy camera detector. This detector identifies all night vision cameras emitting infrared light, promptly alerting the user through a beep sound from the attached buzzer. In addressing the imperative need for heightened privacy protection, the IoT spy camera detector adept.y spots concealed cameras via IR signals. By offering user-friendly location alerts through a reassuring buzzer, it actively contributes to enhancing security measures against evolving privacy threats, creating a safer environment for all. © 2024 IEEE.

关键词： Cost effectiveness

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Machine Learning Classifiers for Predicting the Risk of Cardiovascular Disease in Healthcare 4.0 3

Machine Learning Classifiers for Predicting the Risk of Card...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kumari, Palak Kumar, Ashish Chatterjee, Kakali Master of Computer Applications Patna Women's College Patna India National Institute of Technology Patna Dept. of Computer Science and Engineering Patna India

ISBN: (纸本)9798331528201

Cardiovascular disease (CVD) generic phrase used to describe heart or blood vessel illness. Conditions that impact the body's, brain's, or heart's blood flow. Reduced blood flow can result from factors such as blood clots, leading to heart attack, angina, or stroke. Other potential meanings of CVD. These models offer key benefits like clear interpretability and the ability to analyze complex patterns, significantly enhancing the accuracy of cardiovascular disease risk predictions. The demerits of using SVM, LR, K-NN, and MLP for CVD prediction include inter-pretability issues, overfitting risks, data requirements, feature selection challenges, scalability concerns, algorithm complexity, and class imbalance. Collaboration between machine learning experts and healthcare professionals is essential for developing accurate and reliable CVD prediction models that enhance patient care. CVD is a significant health threat, often leading to fatal complications. Early detection is crucial for effective intervention. This study employs SVM (accuracy is 0.84%), LR (accuracy is 0.86%), K-NN (accuracy is 0.94%), and MLP (accuracy is 0.79%) machine learning techniques to enhance CVD detection by addressing outliers and null values, contributing to advancing automatic detection initiatives. © 2024 IEEE.

关键词： Nearest neighbor search

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Facetpass- Intelligent Facial Recognition Authentication System Security and Usability 2

Facetpass- Intelligent Facial Recognition Authentication Sys...

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2nd International Conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Sathya, K. Esther, J. Kavitha, S. Kamalakumari, J. SRM Institute of Science and Technology Faculty of Science and Humanities Department of Computer Applications Chengalpattu Tamil Nadu Kattankulathur603203 India Tagore College of Arts and Science Department of Computer Science Chromepet Chennai India Agurchand Manmull Jain College Dept. Of. Computer Applications Meenambakkam Chennai India

ISBN: (纸本)9798350349221

The security of authentication system completely is connected with confidentiality and strength. Even if graphical authentication system eradicates the weakness of the text password, single-level password system is inadequate to protect our information. Hence, FacetPass a novel password technique is proposed with 4 × 4 of 16 facets arrangement with multiple colours. Facet touch, fading and swap is applied with automatic facial recognition approach which secures from exhaustive attack, technology-based recording, dictionary, and smudge attack. A scrutiny is systematically conducted on the threat of FacetPass in both usability and security with 113 physical users of various people from different age groups, and educational status. The performance of the FacetPass system exceeds the security and usability of other graphical password systems, including VAP codes, CD-GPs, and EvoPass. © 2024 IEEE.

关键词： Face recognition

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Mathematical Morphology Adopted Automatic Detection of Ground Glass Opacities in Lung CT Images of COVID-19 Patients

Mathematical Morphology Adopted Automatic Detection of Groun...

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Chatterjee, Soma Chatterjee, Rohit Kamal Goswami, R.T. Techno International New Town Dept. of Computer Applications Kolkata India Birla Institute of Technology Dept. of Computer Science & Engineering Mesra Ranchi India Techno International New Town Dept. of Computer Science Kolkata India

ISBN: (纸本)9798350399387

The severity of the nCOVID infection relies on the presence of Ground Glass Opacities (GGO) present in the patient's chest CT scan images. Although, detecting and delineating the precise boundaries of GGO in the chest CT images is challenging. Here, we proposed a fast and novel technique to automatically segment the regions containing GGO in lung CT images using mathematical morphology. We have tested our algorithm on the chest CT images of 145 Covid-positive cases. This unique segmentation approach correctly segments the lung field from chest CT images and identifies GGO with average sensitivity, specificity, and accuracy of 96.89%, 95.23%, and 97.22%, respectively. We used expert radiologists' hand-curated segmentation of GGO as ground truth for quantificational performance analysis. Our research results indicate that this algorithm performs well found in the literature. © 2023 IEEE.

关键词： COVID-19

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Evaluating 10Gbps Wireless Optical Communication Using NRZ Modulation: Analyzing BER, Signal Quality, and System Performance 7

Evaluating 10Gbps Wireless Optical Communication Using NRZ M...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Khant, Shailesh Makwana, Mayur Patel, Atul Faculty of Computer Science and Applications Charusat Changa Anand India Dept. Electronics and Communication Engineering Charusat Changa Anand India

ISBN: (纸本)9798350350067

Free space optical (FSO) communication is a fast and wireless data transfer technique that serves as a viable alternative to traditional fiber-optic systems. This study illustrates how FSO can effectively address the growing demand for rapid data transmission and suggests its potential applicability in various scenarios. FSO can be employed to establish connections between buildings within a corporate campus without the need for physical wiring, or it can provide high-speed internet access in locations where installing cables is challenging. We used Optsim software to assess critical parameters such as Bit Error Rate (BER), eye diagrams, power, and signal quality. The results offer valuable insights into enhancing the efficiency and reliability of FSO systems. FSO can find applications in cellular networks to interconnect network equipment, as well as in high-definition video surveillance. These findings highlight the areas in which FSO can advance communication technology and provide valuable insights for future developments. © 2024 IEEE.

关键词： Bit error rate

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Integrating IoT in Transportation for Smart City Development for Secured data Processing 4

Integrating IoT in Transportation for Smart City Development...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Priya, R. Lakshmi, R. Anandha Sudha, C. Pal, Souvik Ali, Ghassan Ahmed Alajmani, Samah H. Vels Institute of Science Technology and Advanced Studies Dept. of Computer Applications Chennai India Anna Adarsh College for Women Dept. of Computer Applications Chennai India SSKV College of Arts and Science for Women Dept. of Computer Applications Kanchipuram India Saveetha College of Liberal Arts & Sciences Saveetha Institute of Medical & Technical Sciences Chennai India Universiti Islam Sultan Sharif Ali Faculty of Islamic Technology Brunei College of Computer & IT Taif University Department of Information Technology Saudi Arabia

ISBN: (纸本)9798350353266

In this study, we propose the architecture and implementation of an IoT and machine learning powered Integrated Transport Management (ITM-SCD)for Smart City Development to mitigate the issues described above. The design takes all potential transportation system difficulties into account by utilizing many scenarios. The model integrates the various city systems into a single, cohesive whole, with an emphasis on secured data. People's movements can be monitored that smart transportation, healthcare, and entertainment can be provided. We discuss the challenges of transit security and smart city applications, as well as possible solutions based on AI and ML. Then, for smart health, transportation, and energy, some solutions to these privacy and security issues are offered. © 2024 IEEE.

关键词： Traffic congestion

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Comparison of Faster RCNN and YOLO V3 for Video Anomaly Localization

Comparison of Faster RCNN and YOLO V3 for Video Anomaly Loca...

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2023 International Conference on Power, Instrumentation, Control and Computing, PICC 2023

作者： Anoopa, S. Salim, A. Nadera Beevi, S. College of Engineering Trivandrum Dept. of Computer Science and Engineering Kerala India Government Engineering College Thrissur Dept. of Computer Science and Engineering Kerala India Tkm College of Engineering and Technology Dept. of Computer Applications Kerala India

ISBN: (纸本)9798350334463

Video Anomaly Localization techniques are interesting and emerging tasks in computer vision that is used to locate the position of an anomalous object with bounding boxes. Convolutional neural network-based object localization methods have been developed which detect and classify objects with high precision and accuracy. Many algorithms have been developed for object detection and localization with excellent outcomes. These algorithms are fast RCNN, faster RCNN, and YOLO. In this paper, a comprehensive analysis of faster RCNN and YOLO V3 algorithms for anomaly localization is performed based on the parameters FPS, mAP, Precision, Recall and F1 Score. © 2023 IEEE.

关键词： Convolutional neural networks

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