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Suspect Face Retrieval via Multicriteria Decision Process 9

Suspect Face Retrieval via Multicriteria Decision Process

引用

9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Sharma, Dilip Kumar Jalal, Anand Singh Sikander, Bilal Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9789380544441

Using human definable face attributes, this paper presents suspect face identification approach for criminal investigations. To facilitate this approach, Fuzzy Analytic Hierarchy Process (FAHP) weights were created to produce the salience of facial attributes. Facial Attributes, such as short, medium, and long, are given manually assigned weights based on membership grades. The accuracy of face retrieval from a face repository is evaluated through experiments. Along with eliminating the need for expert artists, the proposed method complements sketch-based face recognition by enabling investigators to search face repositories immediately without having to wait for sketches to be generated first. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Face recognition

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Advances in Deep Learning for Dermatology: Convolutional Neural Networks for Automated Skin Disease Detection and Diagnosis 14

Advances in Deep Learning for Dermatology: Convolutional Neu...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Vasishtamol, P.K. Soni, P. Government Engineering College Dept. of Computer Science and Engineering Kerala Thrissur India

ISBN: (纸本)9798350335095

Skin conditions provide serious health risks and need a precise and prompt diagnosis in order to be effectively treated. Convolutional neural networks (CNNs), in particular, have demonstrated encouraging results in automated skin disease identification in recent years. In this paper, we describe a unique method for identifying seven distinct categories of skin illnesses using the HAM10000 image dataset, with a CNN-based model, and a 95 percent accuracy *** HAM10000 dataset, which comprises of dermatoscopic images, is preprocessed into seven classes: Benign keratosis,actinic keratoses,dermatofibroma , melanoma ,vascular lesions, melanocytic nevi and basal cell carcinoma. It reach a 95 percent overall accuracy on the testing set after much experimentation and *** results demonstrate that utilising the HAM10000 image dataset, the proposed CNN-based technique can successfully diagnose a wide range of skin diseases. The accurate and automated diagnosis of skin problems using deep learning techniques can greatly aid dermatologists and other healthcare professionals in offering quick and effective treatments. This study makes a significant contribution to the expanding area of dermatological computer-aided diagnosis and provides insightful information for future developments in skin disease detection. © 2023 IEEE.

关键词： Dermatology

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An Improved Leukemia Detection Model using CNN

An Improved Leukemia Detection Model using CNN

引用

2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Aditya, Y. Durga, M. Venkata Siva Naga Sai Teja, K. Hari Priya, J. Eswar Datta, K. Sr Gudlavalleru Engineering College Dept. of Computer Science and Engineering Gudlavalleru India

ISBN: (纸本)9798350397208

Leukemia is a kind of cancer in which a significant number of immature lymphocyte blood cells form, interfering with the formation of other blood cells. Normally, Leukemia can be identified by using Complete Blood Count(CBC) test. If there is too few or too many of one type of blood cells are presents in CBC then that indicates Leukemia. Even though leukaemia affects children 80% of the time, it can be fatal when it affects adults. So Early detection of Leukemia is very important to save lives. computer technology can help in quick Leukemia diagnosis. This study uses Convolution Neural Network (CNN) model for classifying microscopic blood cells images to predict whether the person is having Leukemia or not. © 2023 IEEE.

关键词： Diseases

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Exploring Data Visualization Techniques for Large Datasets 1

Exploring Data Visualization Techniques for Large Datasets

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sikarwar, Shailendra Singh Kumawate, Bhawesh Vaishnav, Arun Kumar, Love Bushra, Seema Devi, S. Teitsana PGV College Dept. of Computer Science Gwalior India Madhav University Dept. of CSE Pindwara India Mangalayatan University Dept. of Computer Eng. and Applications Beswan India Deen Academics Lingayas Vidyapeeth Faridabad India Dhanalakshmi Srinivasan University Dept. of CSE Coimbatore India

ISBN: (纸本)9798350356816

In a world of Big Data, being able to easily visualize large data sets can make the difference between understanding & wisely judging that data - or not. Big Data is the word with which not many people are aware of, but these success stories make sure that organizations sit back & take notice to what this large data can show then when it collated together over time from different sources. The methods traditionally used for visualization rarely fit into the Big-Data world because they do not have scalability, given that volume (huge amounts), velocity (high speed), or variety (complex details) are now more prevalent in big-data processing. This paper then explores data visualization techniques that can be used to manage very large datasets, including streaming data visualization, dimensionality reduction (e.g. T-SNE), & hierarchical clustering among other Node XL analyses. In this paper, based on accumulated observations & implementations of these techniques over the years, an extensive review is provided for researchers or practitioners to select suitable visualization methodologies that not only suit the scale but also discover patterns in their data. The results highlight the need for enabling tools, methods, & practices to make sense of big data in an interactive visual context that enables better orchestration between complex systems using diverse hardware/software stacks. Additionally, the pros & cons of each method are explained, with real-world use cases & case studies demonstrating their practicality. © 2024 IEEE.

关键词： Data assimilation

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An Automated Floor Cleaner Robot for Domestic and Industrial applications

An Automated Floor Cleaner Robot for Domestic and Industrial...

引用

2024 International Conference on Decision Aid sciences and applications, DASA 2024

作者： Hussein, Molla Rashied Shaikat, Abu Salman Tasnim, Rumana Zonayed, Md Amin, Abdullah Al Rahman, Shahed Jhara, Sayma Sultana University of Asia Pacific Dept. of Computer Science & Engineering Dhaka Bangladesh Laurentian University Bharti School of Engineering and Computer Science Canada World University of Bangladesh Dept. of Mechatronics Engineering Dhaka Bangladesh Anglia Ruskin University Dept of Mechanical Engineering Essex United Kingdom

ISBN: (纸本)9798350369106

Smart home technologies have significantly enhanced comfort and convenience in our daily lives by facilitating several domestic tasks. Household robots utilized in smart homes are designed to maintain clean floor spaces. The manually operated floor cleaning machines are exceedingly labor-intensive and time-consuming. Furthermore, fuel-operated machinery is prohibitively expensive as well as detrimental to the environment. This paper presents the design and development of an automated floor cleaning robot that eliminates previous limitations, thereby conserving both time and financial resources by reducing human labor costs. Recently, several household robots, particularly floor-cleaning robots, have been developed. Nonetheless, only a limited number of advanced robots were dedicated to the wet cleaning of floors. This study presents an efficient automated vacuum cleaning robot that can perform both vacuuming and wet cleaning of floor surfaces while navigating the area. This eliminates the labor and necessity of designating separate time for cleaning the premises. The automated robot was initially designed, followed by the construction of an effective chassis. Subsequently, IR sensors, an Arduino Nano, motor drivers, and a motor shield were incorporated into the system. The economical and user-friendly robot is designed for autonomous cleaning of various surfaces in both household and industrial settings. © 2024 IEEE.

关键词： Wages

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Comparison of CNN and YOLOv5 For Melanoma Detection 14

Comparison of CNN and YOLOv5 For Melanoma Detection

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Antony, Divya Naseer, C. Government Engineering College Dept. of Computer Science and Engineering Kerala Thrissur India

ISBN: (纸本)9798350335095

MELANOMA is one of the most dangerous forms of skin cancer that results from melanocytes, which produce the brown pigment that gives skin its colour. Sometimes it forms multi-colours based on its stage. The survival of melanoma patients depends on the early identification of the disease. But in the early stage, it becomes very small and does not meet the dermoscopic standards for cancer detection such as irregular shape, network, colour of pigments and also it is difficult to differentiate lesion to benign and melanoma. So early detection of melanoma is difficult. Therefore, we need an accurate melanoma classifier that classifies lesions at begin of the stage. In this paper, a comprehensive analysis of cnn, Alexnet and yolov5 for melanoma detection is performed using Accuracy, Precision, Recall and F1 Score © 2023 IEEE.

关键词： Genetic programming

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Comparative Analysis of Decorrelation and Contrast Stretching for Multispectral Color Image Enhancement 10

Comparative Analysis of Decorrelation and Contrast Stretchin...

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Ganesan, P. Leo Joseph, L.M.I. Sajiv, G. Chandrasekaran, B. Manigandan, E. Janakiraman, V. Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Sr University Dept. of ECE Telangana Warangal India Simats Saveetha School of Engineering Tamilnadu Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350353068

The proposed work explains the comparative detailed study of multispectral image enhancement by means of contrast and decorrelation stretching. It is challenging to extract all necessary information from multispectral image which is a huge collection of information. Multispectral images are taken using different filters (sensors) that can recognize different spectral bands, giving us important details about the scene we're looking at. Most of the data is intensified inside a narrow portion of the accessible dynamic range. So the true color composite image looks monotonous. In addition, in multispectral images the visible bands are highly correlated with each other. This is the reason they looks so dull. So it is obligatory to decorrelate the bands and enhance the contrast of the input data. In this study, the efficiency of the contrast and decorrelation stretching transformation on test images is compared. The experimental outcomes undoubtedly demonstrate that decorrelation stretching outperformed the contrast stretching in this context. © 2024 IEEE.

关键词： Image enhancement

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Utilizing Word Embedding and Linguistic Features for Fake News Detection 9

Utilizing Word Embedding and Linguistic Features for Fake Ne...

引用

9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Sharma, Dilip Kumar Shrivastava, Priya Garg, Sonal Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9789380544441

Fake news detection includes the identification of news consisting of deliberate misinformation propagated by social media or by traditional news media. In this paper, we used features of news text for better news classification. Three categories of linguistic features including writing pattern, quantity, and psycho-linguistic features are considered. These features are combined with news text for final machine-learning classification. Three different feature extraction techniques named Tf-idf, hash-vectorizer, and count-vectorizer are adopted. For experimental analysis of the research, a popular LIAR dataset is used. The proposed method achieved the highest of 72.8% accuracy using a random forest classifier and applying the Tf-idf algorithm. Our proposed method using word-embedding over linguistic features achieved better results in comparison to state-of-art methods. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Decision trees

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Hybrid Feature Selection Method with Multi-objective Grey Wolf Optimizer for High Dimension Data 9

Hybrid Feature Selection Method with Multi-objective Grey Wo...

引用

9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Sharma, Dilip Kumar Varshney, Ravi Prakash Garg, Abhishek Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9789380544441

Feature selection is an indispensable activity in machine learning, which aims at identifying the relevant predictors from a very high dimensional feature space to improve the performance and reduce the learning time of the model. However, a stupendous surge in the feature dimension space possesses a significant challenge to feature selection methods. This study is performed with an aim to cater to this challenge, and we propose a hybrid feature selection method composed of a metaheuristic optimization method and a filter-based feature selection. The proposed strategy cherry picks the advantages of both strategies while minimizing the overall drawbacks. We have examined and evaluated the proposed model over three benchmark datasets provided by University of California, Irvine Open-Source Repository. We benchmarked the performance and the accuracy of the proposed work against the standard feature selection methods and the existing hybrid models proposed in preceding studies. Adaptive boosting is used to assess the classification accuracy of the model. The set of standard classification metrics, such as, Accuracy, F1-Score, Recall and Precision are evaluated across all the three datasets for different feature selection strategies. The study result unanimously concludes that the proposed model outclasses the present standard and other state-of-art feature selection methods. The proposed model delivers accuracy of 96.60% over 95.68% for Ionosphere dataset. For the Sonar dataset, our model gives accuracy of 85.71% over 81.11%. For the Clean dataset, the proposed model's precision and recall are 96.31% and 95.10% over 95.01% and 94.93% respectively. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Adaptive boosting

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An Efficient Lightweight Cryptosystem Using Substitution-Shuffling Based Key Generation 26

An Efficient Lightweight Cryptosystem Using Substitution-Shu...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Provat, Al-Anher Haque, Md. Mokammel Chittagong University of Engineering and Technology Dept. of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9798350359015

In recent years, there has been a meteoric rise in the use of small computing devices, including RFID (Radio Frequency Identification) tags, wireless sensors, embedded electronics, and IoT (Internet of Things) gadgets. Given that these gadgets handle highly sensitive data, their protection should be a top priority. Therefore, it is crucial to implement a secure encryption system on susceptible devices. Standard encryption algorithms like RSA and AES are resource-intensive, require substantial memory, and can slow down devices. In this work, we introduce a lightweight cryptosystem that is suitable for low-powered devices without compromising security. This system utilizes Substitution-Permutation network, incorporating a variation of the Feistel design, to establish a secure symmetric key block cipher. As an enhancement over the standard technique, the proposed algorithm requires less run-time while still providing sufficient protection to maintain an avalanche effect of nearly 50%. © 2023 IEEE.

关键词： Block ciphers GFN IoT Knuth shuffle MATLAB S-box SPN

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