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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Human learning of contextual priors for object search: Where does the time go?

Human learning of contextual priors for object search: Where...

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2005 IEEE computer Society Conference on computer Vision and Pattern Recognition, CVPR 2005 - Workshops

作者： Hidalgo-Sotelo, Barbara Oliva, Aude Torralba, Antonio Department of Brain and Cognitive Sciences MIT CambridgeMA02139 United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA02139 United States

ISBN: (纸本)0769526608

Attention allocation in visual search is known to be influenced by low-level image features, visual scene context and top down task constraints. Here, we investigate the role of Contextual priors in guiding visual search by monitoring eye movements as participants search very familiar scenes for a target object. The goal of the study is to identify which stage of the visual search benefits from contextual priors. Two groups of participants differed in the expectation of target presence associated with a scene. Stronger priors are established when a scene exemplar is always associated with the presence of the target than when the scene is periodically observed with and without the target. In both cases, overall search performance improves over repeated presentations of scenes. An analytic decomposition of the time course of the effect of contextual priors shows a time benefit to the exploration stage of search (scan time) and a decrease in gaze duration on the target. The strength of the contextual relationship modulates the magnitude of gaze duration gain, while the scan time gain constitutes one half of the overall search performance benefit regardless of the probability (50% or 100%) of target presence. These data are discussed in terms of the implications of context-dependent scene processing and its putative role in various stages of visual search. © 2005 IEEE computer Society. All rights reserved.

关键词： Eye movements

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Systematic literature review of the techniques for household electrical appliance anomaly detections and knowledge extractions

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Journal of Electrical Systems and Information Technology 2023年第1期10卷 1-19页

作者： Rauf, Seidu Agbor Abdul Adekoya, Adebayo F. Department of Computer Science and Informatics University of Energy and Natural Resource Sunyani Ghana

The demand for electricity at home has increased in recent times globally, this high demand for continuous, stable and affordable power can be attributed to the demand for comfortable lifestyle of consumers but the quality and efficiency of the appliances being used remain questionable. Malfunctioning appliances usually show a power signature statistically different from their normal behavior, which can lead to higher energy consumption or more serious damages. As a result, numerous studies in recent times have been conducted on the household electrical appliance anomaly behaviors to find the root-cause of these anomalies using machine learning techniques and algorithms. This study attempted to undertake a systematic and critical review of ninety-two (92) research works reported in academic journals over fifteen (15) years (2006–2021) in the area of household electrical appliance anomaly detections and knowledge extraction using machine learning. The various techniques used in these reports were clustered based on machine learning-based techniques, statistical techniques and physical based approach techniques and the parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. This clustering was done based on the following criteria: the nature of a dataset and the number of data sources used, the data timeframe, the machine learning algorithms used, machine learning task, used accuracy and error metrics and software packages used for modeling. For the number of data source used, the results revealed that 81.2% of documents reviewed used single sources and Autoregressive integrated moving average (ARIMA) was the highest implemented regression model (60.9%), the probability model that was mostly implemented was the Bayesian network. Furthermore, the study revealed that, root-mean-square error (RMSE) accounted 35% was the most used error metric among household appliance

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Foresighted Heat Ring: Tracking the Productivity and Collaborativeness of Researchers in Dynamic Networks

Foresighted Heat Ring: Tracking the Productivity and Collabo...

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European Network Intelligence Conference (ENIC)

作者： Alfredo Ramos Tilman Göhnert Sam Zeini Ulrich Hoppe Department of Computer Science and Applied Cognitive Science University Of Duisburg-Essen Duisburg Germany

In this paper, we present the design, implementation and an evaluation of the "Foresighted Heat Ring", a technique for visualizing the productivity and collaborative ness of researchers in dynamic networks. Our approach combines a heat map and a foresighted radial layout with physiological theories of visual perception to achieve visual stability on the drawing. The technique has been embedded into a network analysis workbench and applied to a time-sliced nanotechnology publication network from Web of science. Based on this scenario, an evaluation with twenty participants was conducted. The results suggest that there are three factors that allow a visualization technique to be perceived as stable.

关键词： Layout Visualization Productivity Heating Data visualization Collaboration Navigation

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Affective structure modeling of speech using probabilistic context free grammar for emotion recognition 40

Affective structure modeling of speech using probabilistic c...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Huang, Kun-Yi Lin, Jia-Kuan Chiu, Yu-Hsien Wu, Chung-Hsien Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan

ISBN: (纸本)9781467369978

A complete emotional expression typically contains a complex temporal course in a natural conversation. Related research on utterance-level and segment-level processing lacks understanding of the underlying structure of emotional speech. In this study, a hierarchical affective structure of an emotional utterance characterized by the probabilistic context free grammars (PCFGs) is proposed for emotion modeling. SVM-based emotion profiles are obtained and employed to segment the utterance into emotionally consistent segments. Vector quantization is applied to convert the emotion profile of each segment into codewords. A binary tree in which each node represents a codeword is constructed to characterize the affective structure of the utterance modeled by PCFG. Given an input utterance, the output emotion is determined according to the PCFG-based emotion model with the highest likelihood of the speech segments along with the score of the affective structure. For evaluation, the EMO-DB database and its expansion in utterance length were conducted. Experimental results show that the proposed method achieved emotion recognition accuracy of 87.22% for long utterances and outperformed the SVM-based method. © 2015 IEEE.

关键词： affective structure model probabilistic context free grammar Speech emotion recognition

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The unsatisfiability threshold revisited

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Electronic Notes in Discrete Mathematics 2001年 9卷 81-95页

作者： Kaporis, Alexis C. Kirousis, Lefteris M. Stamatiou, Yannis C. Vamvakari, Malvina Zito, Michele University of Patras Department of Computer Engineering and Informatics University Campus GR-265 04 Patras Greece Computer Technology Institute GR-262 21 Patras 61 Riga Feraiou Str. Greece Department of Computer Science University of Liverpool United Kingdom

The problem of determining the unsatisfiability threshold for random 3-SAT formulas consists in determining the clause to variable ratio that marks the experimentally observed abrupt change from almost surely satisfiable formulas to almost surely unsatisfiable. Up to now, there have been rigorously established increasingly better lower and upper bounds to the actual threshold value. In this paper, we consider the problem of bounding the threshold value from above using methods that, we believe, are of interest on their own right. More specifically, we show how the method of local maximum satisfying truth assignments can be combined with results for the occupancy problem in random allocation schemes of balls into bins in order to achieve an upper bound for the unsatisfiability threshold less than 4.571. Thus we improve over the best, with an available complete proof, previous upper bound, which was 4.596. In order to obtain this value, we also establish a bound on the q-binomial coefficients (a generalization of the binomial coefficients) which, we believe, is of independent interest.

关键词： Complexity Phase transition Probabilistic analysis Satisfiability

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Computing Palindromes on a Trie in Linear Time 33

Computing Palindromes on a Trie in Linear Time

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33rd International Symposium on Algorithms and Computation, ISAAC 2022

作者： Mieno, Takuya Funakoshi, Mitsuru Inenaga, Shunsuke Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan Department of Informatics Kyushu University Fukuoka Japan Japan Society for the Promotion of Science Tokyo Japan PRESTO Japan Science and Technology Agency Tokyo Japan

ISBN: (纸本)9783959772587

A trie T is a rooted tree such that each edge is labeled by a single character from the alphabet, and the labels of out-going edges from the same node are mutually distinct. Given a trie T with n edges, we show how to compute all distinct palindromes and all maximal palindromes on T in O(n) time, in the case of integer alphabets of size polynomial in n. This improves the state-of-the-art O(n log h)-time algorithms by Funakoshi et al. [PSC 2019], where h is the height of T . Using our new algorithms, the eertree with suffix links for a given trie T can readily be obtained in O(n) time. Further, our trie-based O(n)-space data structure allows us to report all distinct palindromes and maximal palindromes in a query string represented in the trie T , in output optimal time. This is an improvement over an existing (naïve) solution that precomputes and stores all distinct palindromes and maximal palindromes for each and every string in the trie T separately, using a total O(n2) preprocessing time and space, and reports them in output optimal time upon query. © Takuya Mieno, Mitsuru Funakoshi, and Shunsuke Inenaga.

关键词： Linguistics

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A scoping review of deep neural networks for electric load forecasting

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Energy informatics 2021年第2期4卷 1-13页

作者： Vanting, Nicolai Bo Ma, Zheng Jørgensen, Bo Nørregaard Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Center for Health Informatics the Maersk Mc-Kinney Moller Institute University of Southern Denmark Odense5230 Denmark Center for Energy Informatics the Maersk Mc-Kinney Moller Institute University of Southern Denmark Odense5230 Denmark

The increasing dependency on electricity and demand for renewable energy sources means that distributed system operators face new challenges in their grid. Accurate forecasts of electric load can solve these challenges. In recent years deep neural networks have become increasingly popular in research, and researchers have carried out many experiments to create the most accurate deep learning models. Players in the energy sector can exploit the increasing amount of energy-related data collected from smart meters to improve the grid’s operating quality. This review investigates state-of-the-art methodologies relating to energy load forecasting using deep neural networks. A thorough literature search is conducted, which outlines and analyses essential aspects regarding deep learning load forecasts in the energy domain. The literature suggests two main perspectives: demand-side management and grid control on the supply side. Each perspective has multiple applications with its challenges to achieve accurate forecasts;households, buildings, and grids. This paper recommends using a hybrid deep learning multivariate model consisting of a convolutional and recurrent neural network based on the scoping review. The suggested input variables should be historical consumption, weather, and day features. Combining the convolutional and recurrent networks ensures that the model learns as many repeating patterns and features in the data as possible. © 2021, The Author(s).

关键词： Convolution

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Data Temperature Informed Streaming for Optimising Large-Scale Multi-Tiered Storage

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Big Data Mining and Analytics 2024年第2期7卷 371-398页

作者： Dominic Davies-Tagg Ashiq Anjum Ali Zahir Lu Liu Muhammad Usman Yaseen Nick Antonopoulos Department of Computing University of DerbyDerbyDE221GBUK Department of Informatics University of LeicesterLeicesterLE17RHUK Department of Computer Science COMSATS University IslamabadIslamabad 45550Pakistan Edinburgh Napier University EdinburghEH114BNUK

Data temperature is a response to the ever-growing amount of *** data have to be stored,but they have been observed that only a small portion of the data are accessed more frequently at any one *** leads to the concept of hot and cold *** data can be migrated away from high-performance nodes to free up performance for higher priority *** studies classify hot and cold data primarily on the basis of data age and usage *** present this as a limitation in the current implementation of data *** is due to the fact that age automatically assumes that all new data have priority and that usage is purely *** propose new variables and conditions that influence smarter decision-making on what are hot or cold data and allow greater user control over data location and their *** identify new metadata variables and user-defined variables to extend the current data temperature *** further establish rules and conditions for limiting unnecessary movement of the data,which helps to prevent wasted input output(I/O)*** also propose a hybrid algorithm that combines existing variables and new variables and conditions into a single data *** proposed system provides higher accuracy,increases performance,and gives greater user control for optimal positioning of data within multi-tiered storage solutions.

关键词： data temperature hot and cold data multi-tiered storage metadata variable multi-temperature system

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Reversible watermarking with digital signature chaining for privacy protection of optical contactless captured biometric fingerprints-a capacity study for forensic approaches

Reversible watermarking with digital signature chaining for ...

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International Conference on Digital Signal Processing

作者： Merkel, Ronny Kraetzer, Christian Dittmann, Jana Vielhauer, Claus Department of Computer Science Research Group Multimedia and Security Otto-von-Guericke-University of Magdeburg Germany Department of Informatics and Media Brandenburg University of Applied Sciences Germany

ISBN: (纸本)9781457702747

The FRT-MicroProf 200 CWL (Chromatic White Light) sensor, typically used for the measurement of surface properties for quality assurance purposes, is adapted and used for the contactless, non-invasive lifting of latent fingerprint traces. To the resulting high-resolution biometric data, invertible fragile image watermarking is applied, to assure authenticity and integrity of the data in a forensic investigation. Privacy protection is added by encryption and substitution of the fingerprint area. The scheme is extended to be used in a digital signature chain scenario with hierarchical access for further forensic investigation and the generation and preservation of a chain of custody. The capacity of the introduced watermarking protocol is evaluated in theory and in practical tests using 90 image samples (16bit grayscale, 30 intensity fingerprint images: 6.5bpp, 30 3d-topography fingerprint images: 11.2bpp and 30 standard images: 1.2bpp, different sizes and embedding areas) showing very high capacities especially for fingerprint images. © 2011 IEEE.

关键词： Chains

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