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Hybrid Color Texture Features Classification Through ANN for Melanoma

Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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A Recent Survey on Bone Cancer Detection using Deep Learning Techniques 2

A Recent Survey on Bone Cancer Detection using Deep Learning...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Murugan, S. Patil, Shravani S. Shree Deeksha, V. Reddy, Rohith K. Shravanth Kumar, K.N. Dept. of Computer Science and Engineering Sri Venkateshwara College of Engineering Bengaluru562157 India Dept. of Computer Science and Engineering Sri Venkateshwara College of Engineering Bengaluru56215 India

ISBN: (纸本)9798350383867

The emergence of deep learning methods in medical image analysis has transformed the detection and categorization of bone tumors, presenting encouraging pathways for enhanced diagnosis and treatment strategizing. This literature review synthesizes recent advancements in utilizing deep learning architectures, particularly convolutional neural networks (CNNs), for the automatic interpretation of various imaging modalities including X-rays, CT scans, MRI scans, and bone scintigrams in the context of bone cancer detection and classification. Each study discussed presents unique methodologies combining traditional image processing techniques also the augmentation techniques with deep learning models to accurately identify, categorize, and segment bone lesions. Despite the encouraging results in terms of accuracy, sensitivity, and specificity, several common limitations are identified across the studies, including small sample sizes, potential biases, limited validation on diverse datasets, and a lack of thorough comparisons with existing techniques or expert interpretations. Addressing these limitations through larger and more diverse datasets, rigorous validation protocols, uncertainty quantification, and consideration of clinical outcomes would enhance the reliability, generalizability, and clinical utility of the proposed deep learning-based methodologies. Overall, these studies underscore the immense potential of deep learning approaches in advancing the field of bone cancer diagnosis and management, paving the way for more robust and clinically applicable tools to assist medical professionals. ©2024 IEEE.

关键词： Diseases

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A top-down spatial scenario approach for identifying the locations of rainwater harvesting sites in an urban region

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Environmental science and Pollution Research 2024年第49期31卷 59430-59445页

作者： Kumar, Gaurav Patil, Deshbhushan Kumar, Akshay Gupta, Rajiv Department of Computer Science and Engineering SCSE Galgotias University Greater Noida India Department of Civil Engineering Walchand College of Engineering Sangli India Civil Engineering Department Birla Institute of Technology and Science Pilani Rajasthan Jhunjhunu333031 India

Alternative water sources are necessary in developing nations because surface water is not always accessible, and groundwater is depleted. In such situations, rainwater harvesting is considered a promising sustainable water resource management solution. Numerous studies have been conducted to determine suitable locations for rainwater harvesting (RWH) using bottom-up approaches applied to large watersheds. The bottom-up methods begin with various geographic criteria and end with regions suitable for RWH intervention, even considering the distance from settlements to be one of the criteria, excluding urban areas from RWH site identification. This study developed a top-down methodology that began with the distributed pinpoint locations of potential RWH sites, as determined by distributed flow accumulation values produced from a digital elevation model (DEM), and then filtered out the sites based on various criteria in the context of urban areas. The flow accumulation values were apportioned according to the flow-contributing area of each RWH site. Five flow-contributing areal scenarios corresponding to 1 km2, 2.5 km2, 5 km2, 7.5 km2, and 10 km2 were considered in this study, as it is challenging to choose a suitable location for RWH sites in urban zones for efficient water storage owing to a variety of land uses. Based on this technique, a case study was conducted in Jaipur, Rajasthan, India, where it was found that the volumetric potential of rainwater storage is maximum (403,679,424.9 cu. m) for 1 km2 and minimum (169,951,322 cu. m) for 10 km2 flow contributing areal distribution per RWH site. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Surface waters

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Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

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computer Systems science & engineering 2022年第5期41卷 511-524页

作者： P.Rajeswari K.Jayashree Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai602105India

Recent advancements in cloud computing(CC)technologies signified that several distinct web services are presently developed and exist at the cloud data ***,web service composition gains maximum attention among researchers due to its significance in real-time *** of Service(QoS)aware service composition concerned regarding the election of candidate services with the maximization of the whole *** these models have failed to handle the uncertainties of *** resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by *** the other hand,trip planning is an essential technique in supporting digital map *** aims to determine a set of location based services(LBS)which cover all client intended activities quantified in the *** the available web service composition solutions do not consider the complicated spatio-temporal *** resolving this issue,this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model(F3L-WSCM)in a cloud environment for location *** presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking,hotels,car rentals,*** the next stage,the firefly algorithm is applied to generate composition plans to minimize the number of composition *** by,the fuzzy subtractive clustering(FSC)will select the best composition plan from the available composite ***,the presented F3L-WSCM model involves four input QoS parameters namely service cost,service availability,service response time,and user *** extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy,execution time,and efficiency.

关键词： Web service composition trip planning hybrid models firefly algorithm QoS aware services fuzzy logic

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A Comprehensive Analysis of Network Intrusion Detection in Internet of Things and Wireless Networks 2

A Comprehensive Analysis of Network Intrusion Detection in I...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Swathi, Ch. Kare, Suresh Babu Jntuh University College of Engineering Science & Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350373110

Network Intrusion Detection System (NIDS) is most momentous safety techniques in mobile networks. Intrusion detection (ID) is significant approach aimed at identifying attacks then applying it on security procedures to networks. However, ID is limited by using a singular learning algorithm and is constrained by statistical, computational, and representational limitations. In this survey, various platforms for intrusion detection are analyzed to improve the detection of intrusions. This survey primarily discusses the significant expectations, advantages, and limitations analyzed in framework of Internet of Things (IoT) with Wireless Networks for ID systems. The performance of existing approach was considered by numerous performance metrics: precision, detection rate, false alarm rate (FAR), f1-score recall, accuracy, Mathews Correlation Coefficient (MCC), and missing alarm rate (MAR). This study concludes that the network intrusion detection overcomes the drawbacks of decreasing the detection accuracy and increase efficiency. © 2024 IEEE.

关键词： Network intrusion

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Sustainable Energy Generation from Speed Breakers for Smart Street Lighting Application 2

Sustainable Energy Generation from Speed Breakers for Smart ...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vijayakumar, P. Pyingkodi, M. Karuppusamy, S. Prasaath, J.M. Dhyaneshvar Jeysaravanan, S. Manojkumar, V. Kongu Engineering College Department of Computer Applications Perundurai Erode India Nandha Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350368413

This research explores the potential of integrating rack and pinion mechanisms with electromagnetic induction to generate electricity for street light control systems. By converting mechanical energy into electrical energy, this approach offers a sustainable and environmentally friendly solution. The proposed system utilizes Light Dependent Resistors (LDRs) and a Raspberry Pi microcontroller to dynamically control street lights based on ambient light conditions. This intelligent control strategy optimizes energy consumption and reduces operational costs. This research study analyzes the theoretical foundations, design considerations, and implementation methodologies of the proposed system. Experimental validation demonstrates the feasibility and effectiveness of the approach. By promoting the use of renewable energy sources and reducing reliance on conventional power grids, this innovative solution contributes to the development of sustainable and resilient urban infrastructure. © 2024 IEEE.

关键词： Proportional control systems

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A Comparative Analysis of Image Partitioning and Compression Mechanisms 15

A Comparative Analysis of Image Partitioning and Compression...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chaudhari, Sampada P. Thakur, Nileshsingh V. Dept. of Computer Science and Engineering Prof Ram Meghe College of Engineering & Management Badnera India Dept. of Computer Science and Engineering Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9798331300579

This paper presents a comprehensive comparative analysis of image partitioning and compression mechanisms, two fundamental techniques in image processing and data compression. Image partitioning involves dividing an image into smaller components, while compression aims to reduce the size of digital images while preserving visual quality. The analysis encompasses various aspects of both techniques, including methodologies, algorithms, and performance metrics. For image partitioning, segmentation methods such as block-based, region-based, and hierarchical partitioning are examined, along with their respective advantages and limitations. Additionally, the paper explores compression mechanisms such as transform coding, quantization, entropy coding, and predictive coding, highlighting their roles in achieving efficient compression. Furthermore, the comparative analysis evaluates the tradeoffs between different partitioning and compression techniques in terms of compression ratio, image quality, computational complexity, and applicability across different types of images and applications. Various compression standards such as JPEG and PNG are also reviewed to provide insights into real-world implementations. Finally, the paper discusses future research directions and emerging trends in image partitioning and compression, including the integration of machine learning techniques for improved segmentation and compression performance. The findings presented in this comparative analysis can serve as a valuable resource for researchers, practitioners, and developers working in the fields of image processing, data compression, and multimedia systems. © Grenze Scientific Society, 2024.

关键词： Image compression

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Detecting Misinformation in COVID-19 Content: A Machine Learning and Deep Learning Approach with Word Embeddings

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SN computer science 2025年第1期6卷 1-18页

作者： Chabukswar, Arati Shenoy, P. Deepa Venugopal, K.R. Department of Computer Science & Engineering University Visvesvaraya College of Engineering Bangalore University Karnataka Bengaluru 560001 India

Inaccurate information that is purposefully spread for a certain goal known as fake news related to COVID-19 tweets posing threats to public health, harm to the society. But creating a reliable method to identify news of this kind is difficult, particularly when the news is released combining factual and fictitious material. The identification of fake news with COVID-19 is challenging task in Natural Language Processing (NLP). This study uses Machine Learning (ML) models such as Support Vector Machine (SVM), Random Forest (RF), Gaussian Naıve Bayes (NB), Logistic Regression (LR) and Deep Learning (DL) models with several architectures such as Long-short Term memory (LSTM), Bidirectional LSTM with one-hot encoding representation and pre-trained word embeddings such as Word2Vec, GloVe and FastText, trained using 14,571 records on DataCOVID19 dataset obtained by combining datasets from three different sources available in Kaggle and GitHub repository. The tests on a real-world COVID-19 news dataset show that Bi-LSTM model improves performance when built over various embedding techniques such as Word2Vec, GloVe, FastText and One-hot encoding representation achieving highest accuracy score. The findings emphasise the potential of deep learning methods for identifying false news and have important ramifications for curbing the dissemination of false information about COVID-19 tweets known as Fake News Detection (FND). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19 Deep learning Fake news Machine learning Natural language processing One-hot encoding Social media Word embeddings (Word2Vec, GloVe, FastText)

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Revolutionizing Hearing Health: Mobile-based Audiometry Assessment Enhanced by Machine Learning Integration 8

Revolutionizing Hearing Health: Mobile-based Audiometry Asse...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Kanimozhi, P. Jebasanthiya, P. Kumar, T.Ananth Hussain, Mohamed Inamul Ananth, Christo Preethi, E. Ifet College of Engineering Dept of Computer Science Engineering Villupuram India Holy Cross Engineering College Dept of Computer Science Engineering Tamilnadu Thoothukudi India Samarkand State University Uzbekistan

ISBN: (纸本)9798350386578

The Mobile-Based Audiometry Test Assessment with Machine Learning Integration is a user-friendly application for convenient and accurate hearing health assessment. This innovative app incorporates advanced machine learning algorithms to enhance the precision of hearing tests, allowing for early detection of hearing impairments. Users can effortlessly create profiles, securely store their test results, and access their hearing history. The application ensures the calibration of user hardware for accurate tests, closely mirroring clinical audiometry standards with a wide range of frequencies and decibel levels. Machine learning, particularly decision tree classification, is seamlessly integrated to improve the accuracy of audiogram analysis and classification, enabling the identification of varying degrees of hearing loss. The user-centric design and educational resources encourage awareness and proactive management of hearing health. This Mobile-Based Audiometry Test Assessment with Machine Learning Integration offers an accessible, technologically advanced solution for individuals to take charge of their auditory health and make informed decisions about their well-being. © 2024 IEEE.

关键词： Audiometers

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Deformable Dual Graph Aggregation Transformer Convolutional Networks with Spider Wasp Optimizer for Ovarian Tumor Classification Using Magnetic Resonance Imaging

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Biomedical Materials and Devices 2025年 1-19页

作者： Shanmugaveni, V. Jotheeswari, M. Abarnaswara, R. VijayaKumar, M. Manojanani, A. Department of Computer Science and Engineering (Cyber Security) Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Artificial Intelligence & Data Science Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering RVS Technical Campus Tamil Nadu Coimbatore India

The diagnosis of Ovarian Tumor (OT) remains a significant challenge as there is presently no practical non-invasive technique to determine true benign or malignant lesions before treatment. This study proposes a unique Deformable Dual Graph Aggregation Transformer Convolutional Network (DeDGATCNet) with Spider Wasp Optimizer (SWO) for the classification of ovarian tumors acquired from magnetic resonance imaging (MRI). In contrast to traditional deep learning models, DeDGATCNet + SWO directly integrates bending graph-based forming transformer convolutional learning and improves representation for better classification. The SWO is combined to optimize the parameters of the network, thereby improving its performance. The procedures include pre-processing the MRI data from the study of ovarian tumor TCGA-OV datasets with an Adaptive Bilateral Texture Filter (ABTF) for removing artifacts and normalization of intensity, accompanied by a segment using a Masked-Attention Mask Transformer (MAMT). Feature extraction is achieved through a One-Dimensional Quaternion Discrete Fourier Transform (1D-QDFT), which captures features from both the spatial and frequency domains. DeDGATCNet + SWO performance is way ahead and sets a new accuracy record of 99.9% for state-of-the-art models. This research introduces a new type of hybridization between deformable graph transformers, quaternion-based spectral analysis, and nature-inspired optimization techniques, setting non-invasive OT at a new height for classification and improving early diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive bilateral texture filter Deformable dual graph aggregation transformer convolutional networks Masked-attention mask transformer One-dimensional quaternion discrete fourier transform

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