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Utilizing Smartphone Usage pattern for Gender recognition applying machine learning Algorithms 4

Utilizing Smartphone Usage Pattern for Gender Recognition ap...

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4th international conference on Electronics, Communication and Aerospace Technology, ICECA 2020

作者： Polin, Johora Akter Khan, Omayer Marouf, Ahmed Al Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728163871

Smartphones can connect people over long distance within few seconds and perform critical tasks easily. the concept of global village has turned into reality through the optimum usage of smartphones. Smartphone is not just being used as a helping hand, it also contains the user's identity and behavior through its usage pattern. this paper has utilized the smartphone usage pattern to identify the gender of its user. In this process, the proposed research work has analyzed 1284 instances of male and female of a specific age group (18-30 years) are collected with a survey that incorporates relevant features from the subjects and their gender to create a labelled dataset. For better approximation and experimentation, 10 different machine learning algorithms such as Naïve Bayes, decision tree, random forest etc. are compared. the compared algorithms are taken from five different categories of classification algorithms such as tree-based classifiers, Bayes, function-based etc. To evaluate performance of different algorithms, accuracy, precision, recall and f-score measurements are utilized. Among these algorithms, random forest has performed better by showing accuracy of about 84.11%. From this research, the factors involved in smartphone are known and its usage is impactful for predicting users' gender. With the understanding of this traits, security, biometric, and privacy concerns can be developed to a higher extent. © 2020 ieee.

关键词： Smartphones

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Averting Frustration in Geriatric Patients through Identification, 2D Mapping and Navigation Using machine learning 4

Averting Frustration in Geriatric Patients through Identific...

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4th international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2022

作者： Malhotra, Alekha Bhatia, Stuti Jain, Reetu Class of 2023 Cathedral and John Connon School 6 Purushottamdas Thakurdas Rd Marg Azad Maidan Fort Maharashtra Mumbai400001 India On My Own Technology Pvt. Ltd. 2A Pace House 7 Swastik Society Gulmohar Road Vile Parle West Mumbai400049 India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

Alzheimer's patients experience agitation and frequently display aggressive behaviors due to their inability to retain simple information, such as people's names, and often misplace items, among other causes. the incurable nature of this disease creates the need for assistive technology to help patients. this proposed methodology, MemoryMap, consists of a pair of glasses, a camera, hardware for audio output and input and a microprocessor. the algorithm uses image-processing and machine learning to perform object and facial recognition, map out the locations of both the object and patient and provides a navigation system to guide patients to any object they have misplaced. this prototype continually records the patient's view through the camera and performs real-time analysis on the video feed using OpenCV, allowing the identification of the object or face desired by the user through a NumPy model. Each object is recognised and entered into a database in the form of an excel sheet using XLWT and XLRT to maintain the location and time frame. Visual feedback of the navigation route along with audio feedback for recognition enables the patient to become more self-sufficient. this prototype is targeted toward patients in the Mild Stage of Alzheimer's and works towards relieving their irritation caused due to memory loss. © 2022 ieee.

关键词： Cameras

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and Applications, ICMISC 2023

4th International Conference on Recent Trends in Machine Lea...

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and Applications, ICMISC 2023

ISBN: (纸本)9789819994410

the proceedings contain 69 papers. the special focus in this conference is on Recent Trends in machine learning. the topics include: Implementation of Dual-Band Dielectric Resonator Antenna for 5G Applications;defect Detection in Metal Surfaces Using Computer Vision;liver Cirrhosis Prediction Using machine learning Classification Techniques;a Recent Survey on Risk Factors Affecting the Blood Pressure in India;Real-Time Monitoring System for Breakdown Analysis and OEE in the Wire Drawing Industry;tooth Sensitivity Device—Detection and Diagnosing of Sensitivity in the Dental Pulp;recognition of Skin Cancer;ioT-Based Smart Street Lighting Surveillance System;rock Segmentation of Real Martian Scenes Using Dual Attention Mechanism-Based U-Net;IAAS: IoT-Based Automatic Attendance System with Photo Face recognition in Smart Campus;hardware Implementation of Moving Object Detection Using Background Subtraction Algorithm;crime pattern Identification and Prediction Using machine learning;IMICE: An Improved Missing Data Imputation Using machine learning;analyzing Students’ Opinion on E-learning—Indian Students’ Perspective;rash Driving Detection and Alerting System;Logistic-Based OVA-CNN Model for Alzheimer’s Disease Detection and Prediction Using MR Images;Comparative Study of CNNs for Camouflaged Object Detection;3D Avatar Reconstruction Using Multi-level Pixel-Aligned Implicit Function;Helmet Detection Using YOLO-v5 and Paddle OCR for Embedded Systems;defogNet: A Residual Network for Removal of Fog Using Weighted Combination Loss;Text-to-Image Generation Model with DNN Architecture and Computer Vision for Embedded Devices Using Quantization Technique;one-Shot learning for Archaeological Site Data Using Deep Neural Network on Embedded Systems;enhanceNet: A Deep Neural Network for Low-Light Image Enhancement with Image Restoration;intelligent Prediction of Cardiac Abnormality.

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A Proficient Model to Classify Bangladeshi Bank Notes for Automatic Vending machine Using a Tiny Dataset with One-Shot learning Siamese Networks 11

A Proficient Model to Classify Bangladeshi Bank Notes for Au...

引用

11th international conference on Computing, Communication and Networking Technologies, ICCCNT 2020

作者： Hossain, Md. Ekram Islam, Arni Islam, Md. Sanzidul Daffodil International University Department of Software Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728168517

Automatic vending machine is a necessity at this technological era. It's a step to go toward the vendor-less shop management, which supports the commandment of the 4th Industrial Revolution. But image recognition system needs a lot of data to get the pattern. Deep learning applications is very computationally extravagant for getting good features and find in many cases that little data cannot give good learning features. We have used and reworked the architecture of Siamese Neural Network for One-shot learning to recognize the Bangladeshi bank notes with a tiny dataset. In this article we analyzed 20 images only for 5 different Bangladeshi bank notes what people used regularly. this research will help general people to get better experience with vending machines which can recognize notes with one data example only. We used 5 notes (5,10,20,50,100 TAKA) and get excellent result with 97.38% accuracy using help of convolutional architecture. © 2020 ieee.

关键词： Deep learning

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Wood Species recognition Using machine learning 4

Wood Species Recognition Using Machine Learning

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4th international conference on Microelectronics, Signals and Systems, ICMSS 2021

作者： Neethu, S. Baby Syla, L. A P J Abdul Kalam Technical University Department of Computer Applications CET Trivandrum India

ISBN: (纸本)9781665448857

Although a computer vision-based automated wood species detection system is not extensively employed today, it is in high demand in a range of industries. Because each species of wood has distinct features and traits that determine how it is used and implemented. Human experts have traditionally been used nearly entirely in the identifying process, which is based on a variety of features of wood, such as texture, structure, and colour. Even competent scientists have difficulties distinguishing between different species of wood found in nature. the most difficult aspect of wood recognition is that some wood species have a texture that is identical to that of others, hence feature extraction is the most important step in the process. So in this work, two statistical feature extraction techniques are used 1) Local Binary pattern (LBP) 2) Gray Level Co-Occurrence Matrix (GLCM). Features extracted from both methods are combined to see whether they increase classification accuracy or not. Multi-SVM is used for classification. For this study, a new dataset is created which includes the three most common tree species in Indian territory. this study found that integrating LBP and GLCM features enhanced classification accuracy. Proposed system produces a result of 97.2%, which is better than individual approaches. © 2021 ieee.

关键词： Local binary pattern

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ISM 2022 - 4th international conference on Industry 4.0 and Smart Manufacturing

ISM 2022 - 4th International Conference on Industry 4.0 and ...

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4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

the proceedings contain 199 papers. the topics discussed include: infrastructure network support and leapfrogging Africa to Industry 4.0: the case of Tanzania;comparison of energy-use efficiency for lettuce plantation under nutrient film technique and deep-water culture hydroponic systems;an edge-cloud based reference architecture to support cognitive solutions in process industry;logistics 4.0 in intermodal freight transport;analysis of sustainable concrete obtained from the by-products of an industrial process and recycled aggregates from construction and demolition waste;intelligent concrete surface cracks detection using computer vision, pattern recognition, and artificial neural networks;spatial change recognition model using artificial intelligence to remote sensing;smart trip prediction model for metro traffic control using data mining techniques;encryption and generation of images for privacy-preserving machine learning in smart manufacturing;and using analytical and data-driven methods to develop a soft-sensor for flow rate monitoring in tube extrusion.

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A novel fuzzy anomaly detection method based on clonal selection clustering algorithm

A novel fuzzy anomaly detection method based on clonal selec...

引用

4th international conference on machine learning and Cybernetics

作者： Lang, Fenghua Li, Jian Yang, Yixian Beijing Univ Posts & Telecommun Informat Secur Ctr State Key Lab Networking & Switching Beijing 100876 Peoples R China

ISBN: (纸本)3540335846

this paper presents a novel unsupervised fuzzy clustering method based on clonal selection algorithm for anomaly intrusion detection in order to solve the problem of fuzzy k-means algorithm which is particularly sensitive to initialization and fall easily into local optimization. this method can quickly obtain the global optimal clustering with a clonal operator which combines evolutionary search, global search, stochastic search and local search, then detect abnormal network behavioral patterns with a fuzzy detection algorithm. Simulation results on the data set KDD CUP99 show that this method can efficiently detect unknown intrusions with lower false positive rate and higher detection rate.

关键词： pattern recognition

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A Survey on Statistical pattern Feature Extraction

引用

4th international conference on Intelligent Computing

作者： Ding, Shifei Jia, Weikuan Su, Chunyang Jin, Fengxiang Shi, Zhongzhi China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221008 Peoples R China Acad Sinica Key Lab Intelligent Informat Proc Inst Comop Technol Beijing 100080 Peoples R China Agr Univ Coll Plant Protection Tai An Shandong 271018 Peoples R China Shandong Univ Sci Technol Coll Geoinformation Sci Engn Qingdao 266510 Peoples R China

ISBN: (纸本)9783540859833

the goal of statistical pattern feature extraction (SPFE) is 'low loss dimension reduction'. As the key link of pattern recognition, dimension reduction has become the research hot spot and difficulty in the fields of pattern recognition, machine learning, data mining and so on. pattern feature extraction is one of the most challenging research fields and has attracted the attention from many scholars. this paper summarily introduces the basic principle of SPFE, and discusses the latest progress of SPFE from the aspects such as classical statistical theories and their modifications, kernel-based methods, wavelet analysis and its modifications, algorithms integration and so on. At last we discuss the development trend of SPFE.

关键词： Extraction

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Analysis of Bangla Transformation of Sentences Using machine learning 1

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4th international conference on Deep learning, Artificial Intelligence and Robotics, ICDLAIR 2022

作者： Das, Rajesh Kumar Sammi, Samrina Sarkar Kobra, Khadijatul Ajmain, Moshfiqur Rahman khushbu, Sharun Akter Noori, Sheak Rashed Haider Daffodil International University Dhaka1341 Bangladesh

ISBN: (数字)9783031303968

ISBN: (纸本)9783031303951

In many languages, various language processing tools have been developed. the work of the Bengali NLP is getting richer day by day. Sentence pattern recognition in Bangla is a subject of attention. Additionally, our motivation was to work on implementing this pattern recognition concept into user-friendly applications. So, we generated an approach where a sentence (sorol, jotil and jougik) can be correctly identified. Our model accepts a Bangla sentence as input, determines the sentence construction type, and outputs the sentence type. the most popular and well-known six supervised machine learning algorithms were used to classify three types of sentence formation: Sorol Bakko (simple sentence), Jotil Bakko (complex sentence) and Jougik Bakko(compound sentence). We trained and tested our dataset, which contains 2727 numbers of data from various sources. We analyzed our dataset and got accuracy, precision, recall, f1-score and confusion matrix. We get the highest accuracy with the decision tree classifier, which is 93.72%. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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A Privacy-Preserving Classifier in Statistic pattern recognition 4th

A Privacy-Preserving Classifier in Statistic Pattern Recogni...

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4th international conference on Cloud Computing and Security (ICCCS)

作者： Wang, Qi Zhou, Dehua Guan, Quanlong Li, Yanling Yang, Jimian Jinan Univ Dept Comp Sci Guangzhou Guangdong Peoples R China Jinan Univ Network & Educ Technol Ctr Guangzhou Guangdong Peoples R China

ISBN: (纸本)9783030000097;9783030000080

machine learning classification and pattern recognition are widely used in various scenarios nowadays, such as medical diagnosis and face recognition, both need to estimate the similarity measure between different samples. In such applications, it is critical to protect the privacy of both the private input data and the machine learning model. In this paper, we propose a privacy-preserving Mahalanobis distance scheme for the statistic pattern recognition, and construct a protocol for the privacy-preserving prediction phase by using the labeled-homomorphic encryption scheme, which combines linearly homomorphic encryption and pseudo-random function. And we consider an outsouring scenario, which most work to be outsourced to the cloud server. Our design goal is to ensure that the client's private data is permanently confidential and protect the secret model in cloud server. Most of the previous work proposed complex schemes with too many interactions between the clients and cloud server, and we propose an efficient scheme to minimal the complexity of the client side.

关键词： Classification Homomorphic encryption Cloud computing

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