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computer Vision Based Object Detection and Recognition System for Image Searching 12

Computer Vision Based Object Detection and Recognition Syste...

引用

12th International Conference on Electrical and computer engineering, ICECE 2022

作者： Nayeem, Tanvir Ahamed Motaharuzzaman, S.M. Hoque, Anika Tabassum Rahman, Md. Habibur Shahjalal University of Science & Technology Dept. of Computer Science & Engineering Sylhet Bangladesh Sylhet Agricultural University Dept. of Agriculture Sylhet Bangladesh Dhaka International University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350398793

computer Vision is a concept which works with the methods for automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. To fulfill the new challenges, the system which is proposed in this paper, is mainly being used for object detection and recognition of images so that the image search engine becomes more fruitful. A large dataset of 15 million high-resolution images have been taken from ImageNet with more than 21K different classes to the purpose of object detection and recognition system for image search engine which have been trained through machine learning. TensorFlow is an open source library for software which uses machine learning techniques as Convolution Neural Network to train datasets and to provide the percentage accuracy of objects in an image. The datasets have been trained so that the objects can be detected and figured out. The trained datasets create a protocol buffer (.pb) file which ensures the serialization of datasets according to their classes for matching the patterns. For giving input as well as viewing the result with best guesses, it has been designed a demo image search engine using Laravel5.4 framework of PHP language. By uploading several images on the demo image search engine, best guesses can be shown where the input image matches the pattern with protocol buffer file to provide the output results. There has been achieved highest accuracy rate of 99.983% best guesses and lowest accuracy rate of 89.282% best guesses for this proposed system which is better than the previous state-of-art. © 2022 IEEE.

关键词： Python

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An Overview: Orthogonal Frequency Division Multiplexing Techniques for Visible Light Communication systems 4

An Overview: Orthogonal Frequency Division Multiplexing Tech...

引用

4th West Asian Symposium on Optical and Millimeter-Wave Wireless Communications, WASOWC 2022

作者： Habibi, Saba Omidi, Mohammadjavad Sadat Tabataba, Foroogh Yazdian, Ehsan Isfahan University of Technology Dept. Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9781665409131

A great deal of attention is being paid to OFDM modulation in visible light communication systems and it because of its advantages such as reducing the intersymbol interference (ISI) and using a simple equalizer to compensate the optical channel. However, transmitted optical signals have been restricted to be real and nonnegative. Therefore, OFDM modification techniques that meet the requirement of optical signals, have played a pivotal role in wireless optical communication researches. This paper surveys and analyzes different OFDM techniques and compares them with each other. © 2022 IEEE.

关键词： Orthogonal frequency division multiplexing

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On Neural-Network-Based Detection for Hypertensive Subjects Using Classification of Retinal Fundus Photographs 53

On Neural-Network-Based Detection for Hypertensive Subjects ...

引用

53rd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2023

作者： Sonetsuji, Yuki Isokawa, Teijiro Kamiura, Naotake Tabuchi, Hitoshi University of Hyogo Graduate School of Engineering Dept. of Electronics and Computer Science Himeji Japan Hiroshima University Dept. of of Technology and Design Thinking for Medicine Hiroshima Japan

ISBN: (纸本)9781665464161

In this paper, a method of detecting hypertensive subjects is proposed, using retinal fundus photographs classified by convolutional neural networks (CNNs for short). The proposed method employs Inception-v3 model as a CNN. The data to be presented to the proposed model are prepared from retinal fundus photographs. The scheme of fine tuning is conducted to construct a discrimination model. The model specifies the photographs corresponding to the subjects being in hypertensive states. Experimental results establish that the proposed method can achieves favorable metric values. Especially, Accuracy value achieved by the proposed method is comparatively high for the small scale of dataset, compared with the previously proposed method. © 2023 IEEE.

关键词： Ophthalmology

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Adjustment of Interval of Voices in Cognitive Shuffle Method by Body Movement Feedback with Binaural Sound 23

Adjustment of Interval of Voices in Cognitive Shuffle Method...

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23rd ACIS International Summer Virtual Conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Summer 2022

作者： Fukumoto, Makoto Konishi, Hirohisa Fukuoka Institute of Technology Dept. of Computer Science and Engineering Fukuoka Japan

ISBN: (纸本)9798350396379

Insomnia remains a severe problem in social and personal. Sound and music have been used to solve the problem. Cognitive shuffle is one of the insomnia treatments by using voices for shuffling the user's thoughts. The purpose of this study is to propose a method for adjusting the interval of voices used in the cognitive shuffle method. Based on the hypothetic arousal level of the user estimated by body movement, the interval is determined. When the arousal level was lower, the intervals were set longer. A feedback system was constructed to investigate the proposed method's fundamental efficiency on sleep. A sheet-type pressure sensor was used to measure the user's body movement. Moreover, the binaural sound was used to create voices in the cognitive shuffle method. The comparing experiment was conducted with a sound condition with the proposed method and control with no sound. As experimental results, a marginal difference was observed between the two conditions in the number of body movements. No significant difference in subjective sleepiness after the experiment was observed. © 2022 IEEE.

关键词： Feedback

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Effect of Picture Prime on the Response Time and Error Rate Among Bilingual Children

Effect of Picture Prime on the Response Time and Error Rate ...

引用

2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022

作者： Banerjee, Java Roy, Hrishikesh Chakraborty, Baisakhi Basu, Anupam National Institute of Technology Dept. of Computer Science and Engineering Durgapur India

ISBN: (纸本)9781665477192

The Priming phenomena establishes a positive result in response to various word or picture naming tasks due to preceding manifestation in the form of a word or picture. The response time in identifying the words is less in the context of words appearing after a semantically connected word or picture compared to the words in segregation or words anteceded by a semantically unconnected word or picture. In order to investigate this phenomenon in the context of Bengali-speaking children, fifteen primary school children with ages ranging between 7 to 10 years were involved in the picture priming experiment. The experiment comprised of 25 words to be read by the children under two conditions consisting of without prime and with picture prime. Error rates of the participants are measured based on their performance during the experiment. The reaction time of the participants is also computed in seconds with reference to the two conditions that is the availability of prime and no prime. Analysis of the student's performance during the above experiment showed that the response time and error rate are comparatively reduced while identifying the target word in the condition of the presence of picture prime. To the best of our knowledge, this is the first-ever investigation regarding the effect of the presence of picture prime among Bengali-speaking children. © 2022 IEEE.

关键词： Errors

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Detection of SQL Injection Attacks Using Machine Learning Techniques 5

Detection of SQL Injection Attacks Using Machine Learning Te...

引用

5th International Conference on Electrical, Communication and computer engineering, ICECCE 2024

作者： Shakya, R.D.N. Dharmaratne, D.N.S. Sandirigama, Manjula University of Ruhuna Department of ICT Matara Sri Lanka Ltd Malabe Sri Lanka University of Peradeniya Dept. of Computer Engineering Peradeniya Sri Lanka

ISBN: (纸本)9798331529437

As listed in the Open Worldwide Application Security Project (OWASP), injections are one of the most frequent threats to web applications. SQL injections (SQLi) took precedence among the injections. This research focused on SQLi attack detection with the aid of Machine Learning (ML) techniques. Standing strong on the thorough literary review, the study tried to find a meaningfully capable set of ML algorithms that could be utilized to design and develop a real-time SQLi attack detection model. The research experiment utilized K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) to fulfill the primary research aim, which is the detection of SQLi attacks. The research used a dataset which consisted of 53,261 data points of both SQLi attack payloads and non-malicious payloads. The SQLi attack payloads covered a wide range of different SQL statements. After the implementation of the experimental designs for three separate ML models, the performance results metrics were generated. As the ML model performance results metrics, it uses classification accuracy, classification error, precision, true positive rate, false positive rate, and specificity. The KNN algorithm achieved 99.55% accuracy, while RF achieved 87.72% and the SVM achieved 98.28%. The KNN algorithm secured the highest rate of accuracy among the three models for SQLi attack detection. The findings of the research show the potential of ML models in SQLi attack detection. The research results can be utilized for the development of ML-enabled attack detection solutions particularly for web applications. © 2024 IEEE.

关键词： Support vector machines

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Analysis of ECG Image File Encryption using ECDH and AES-GCM Algorithm 7

Analysis of ECG Image File Encryption using ECDH and AES-GCM...

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7th International Workshop on Big Data and Information Security, IWBIS 2022

作者： Oktivasari, Prihatin Agustin, Maria Akbar, Rahmat Esa Mohammad Kurniawan, Asep Zain, Ayu Rosyida Murad, Fachroni Arbi Politeknik Negeri Jakarta Dept. of Informatics and Computer Engineering Depok Indonesia

ISBN: (数字)9781665489508

ISBN: (纸本)9781665489508

Image data is used in various fields, including the health sector. One of the image data in the health sector is an electrocardiogram (ECG). The ECG contains the identifying information of a person which must be guarded and secured. ECG image data security can be done by encrypting image data using encryption algorithms. The encryption algorithms used in this thesis are ECDH and AES-GCM. ECDH is used to generate key pairs which are then used as keys for AES-GCM encryption and decryption. The encryption and decryption process are carried out in the python programming language. The results of the encryption time and decryption time increase because of the dimensions and size of the ECG image file. The nonce value and authentication tag are checked to be able to perform the decryption process. The histogram test results prove the uniformity of pixels in the encrypted ECG image file. PSNR and SSIM test results prove the difference between the initial and encrypted ECG image files. The results of the NIST Statistical Test Suite test prove that the algorithm used produces random output so that it can be used to secure ECG image files. © 2022 IEEE.

关键词： Electrocardiograms

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Advancing Robust Few-shot Surface Defect Detection through Meta-learning 9

Advancing Robust Few-shot Surface Defect Detection through M...

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9th International Conference on Integrated Circuits, Design, and Verification, ICDV 2024

作者： Putri, Wenny Ramadha Li, Yung-Hui Wang, Jia Ching National Central University Dept. of Computer Science and Information Engineering Taoyuan Taiwan Hon Hai Research Institute AI Research Center Taipei Taiwan

ISBN: (纸本)9798350371864

The primary limitation of deep learning (DL) techniques in detecting manufacturing defects is the requirement for a substantial volume of labeled data. Nevertheless, obtaining precise annotations is an arduous process due to the diverse range of defect categories. To tackle this issue, we propose MetaFormer, a transformer-based few-shot surface defect detection that learns the meta-features from a large-scale dataset of base classes. This method enables generalization to a novel class with only a few training examples. We demonstrate the effectiveness of our model on the steel surface defect dataset, surpassing the performance of the baseline model despite using only a limited number of annotated examples. © 2024 IEEE.

关键词： Zero-shot learning

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Combining Global Contextual and Multi-Scale Features in ACSOA based Multi-Branch Deep Learning for Sensor based HAR 10

Combining Global Contextual and Multi-Scale Features in ACSO...

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10th International Conference on Electrical Energy systems, ICEES 2024

作者： Anitha, Cuddapah Mahaveerakannan, R. Archana Jenis, R.M. Sathyendra Kumar, V. Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India St.Joseph's College of Engineering OMR Computer Science and Engineering Chennai600 119 India Dept. of CSE Andhra Pradesh India

ISBN: (纸本)9798350353778

Research in human-cyber-physical systems is seeing a substantial push towards human activity recognition (HAR) using smartphone sensors. In computer vision, HAR is still an important but difficult topic to solve. Healthcare and eldercare are two areas where HAR is most suited for integration with other machineries like the Internet of Things. Thanks to advancements in deep learning, HAR presentation optimisation is now possible through automated high-level feature extraction. This study introduces a novel multi-branch deep learning framework that combines global contextual information with multi-scale features to reduce feature redundancy and enhance recognition accuracy. The framework includes two branches: one for extracting global context and another for capturing multi-scale local features using a fully convolutional network (FCN). The advanced crow search optimization algorithm (ACSOA) selects the most relevant features, further boosting classification accuracy. Human activities are classified based on prediction scores from both branches. The study also examines the impact of feature maps, filter sizes, decision fusion strategies, and convolution layers on recognition accuracy. The proposed method achieves approximately 98% accuracy on the UCI HAR and WIDSM datasets, outperforming single-layer decision fusion approaches. © 2024 IEEE.

关键词： Deep learning

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Quality of Services in Hybrid SDN (hSDN): A Review 7

Quality of Services in Hybrid SDN (hSDN): A Review

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7th International Conference on Communication and Electronics systems, ICCES 2022

作者： Mehraban, Samiullah Yadav, R.K. Delhi Technological University Dept. of Computer Science and Engineering Delhi India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

As now a day we see that internet has become the backbone of each and every digital society, smart devices as well as the growth of new technology and concepts like IOT, Machine learning 4G, 5G and social network that totally chanced all network requirement. In the last decade Software Define Networking (SDN) changed the computer Network to an important and significant model as it composed of three different layer Data layer, Control layer and Application layer, main Idea behind SDN is separation of Control layer and Data layer and centralization of control layer to improve efficiency and performance in the network. As Software Define N has lots of benefit compare to legacy network but still deployment of pure SDN is not an easy work and it has its own limitation and some major challenges like financial challenge, technical challenge, security Issue, downtime fear of the network and quality of service degradation. These challenges slow down deployment of fully SDN and cause the birth of Hybrid Software Define Network (hSDN) which legacy devices coexist with SDN devices and take advantage of both legacy and Software Define Network robustness and programmability. In this paper we explore a review of Quality of services in Hybrid SDN in an approach which Quality of Service can benefit the most from the concept of Hybrid Software Define Network. © 2022 IEEE.

关键词： Quality of service

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