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Classification of Skin Cancer Images using Lightweight Convolutional Neural Network

Classification of Skin Cancer Images using Lightweight Convo...

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Emerging technology (INCET), International Conference for

作者： Talapally Sandeep Kumar B Annappa Shubham Dodia Dept. of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangalore India

Skin is the most powerful shield human organ that protects the internal organs of the human body from external attacks. This important organ is attacked by a diverse range of microbes such as viruses, fungi, and bacteria causing a lot of damage to the skin. Apart from these microbes, even dust plays important role in damaging skin. Every year several people in the world are suffering from skin diseases. These skin diseases are contagious and spread very fast. There are varieties of skin diseases. Thus it requires a lot of practice to distinguish the skin disease by the doctor and provide treatment. In order to automate this process several deep learning models are used in recent past years. This paper demonstrates an efficient and lightweight modified SqueezeNet deep learning model on the HAM10000 dataset for skin cancer classification. This model has outperformed state-of-the-art models with fewer parameters. As compared to existing deep learning models, this SqueezeNet variant has achieved 99.7%, 97.7%, and 97.04% as train, validation, and test accuracies respectively using only 0.13 million parameters.

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Deep Learning in Autonomous Robotics

Deep Learning in Autonomous Robotics

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Anand Singh Bisen Sami Anand S. Sujatha Tholkapiyan. M Yogendra Kumar Madhuri Sharma Dept. of CSE & IT Vikrant University Gwalior India Dept. of Computer Science and Engineering Lovely Professional University Punjab India Dept. of Civil Engineering K.Ramakrishnan College Trichy India Dept. of Civil Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Computer Eng. & Applications GLA University Mathura India Dept. of Life Sciences CHRI Campus Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Deep learning has become a cornerstone in the development of autonomous robotics, enabling machines to detect, understand, and interact with their environment with precision and efficiency. By leveraging advanced deep neural networks, autonomous robots can process and interpret a wide range of sensory data, including visual, auditory, and tactile inputs. This ability is crucial for tasks like object recognition, scene understanding, and decision-making in complex, dynamic environments. Deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), provide visual perception, allowing robots to navigate and operate in sophisticated settings. Additionally, reinforcement learning techniques teach robots to make real-time decisions and adjust their actions accordingly. These technological advancements have led to significant progress in applications such as autonomous vehicles, surgical robots, industrial automation, and unmanned aerial vehicles (UAVs). This abstract highlight the various applications and benefits of deep learning in autonomous robotics, emphasizing its role in enhancing the performance and adaptability of robotic systems across diverse fields.

关键词： Deep learning Visualization Technological innovation Service robots Robot sensing systems Sensors Convolutional neural networks Vehicle dynamics Robots Visual perception

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ESAD: Expert System for Apnea Detection Using Enhanced DWT Feature Extraction and Machine Learning Algorithms

ESAD: Expert System for Apnea Detection Using Enhanced DWT F...

引用

International Conference on Computing and Networking technology (ICCNT)

作者： Atiya Khan Saroj K. Biswas Chukhu Chunka Dept. of Computer Science & Engg. National Institute of Technology Silchar Silchar India

Sleep apnea is a sleep disorder with some potentially severe consequences, characterized by recurring disturbance in breathing during sleep. This happens when the muscles in the back of the throat cannot maintain an open airway, leading to brief awakenings to take a breath. Since the last decades, several expert systems have been proposed for apnea detection, but only a few have analyzed the data exhaustively before classification. This paper proposes a model for apnea detection named Expert System for Apnea Detection (ESAD) that uses Discrete Wavelet Transform (DWT) for EEG signal decomposition and for features extraction, namely Standard Deviation(SD), Power, Mean, Energy, Kurtosis, Max, Min, Variance and Root Mean Square (RMS). The proposed ESAD uses Isolation Forest (IF) for outlier detection as the used medical data can have noise and anomalies. Finally, the SVM with Radial Basis Function (RBF) kernel is incorporated for detecting and classifying sleep apnea disorder accurately. The model’s performance is evaluated by incorporating key performance metrics like - Precision, Accuracy, F1-Score and Recall and ROC-AUC curve. On comparing ESAD with other machine learning models, it outperforms other incorporated shallow learning models like - Decision Tree, KNN, and Naive Bayes with superior performance, demonstrating higher accuracy and precision in detecting apnea events.

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Optimized Approach for Video Summarization using Transfer Learning and LSTM

Optimized Approach for Video Summarization using Transfer Le...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Abhishek Dhiman Maroti Deshmukh Dept. of Computer Science and Engineering National Institute of Technology Uttarakhand Srinagar Garhwal India

Video content has been increasing at a very high pace, so the need for summarizing videos is of urgent need. Video summarization emphasizes on quick go-through of video content. In the last few decades, the field of video summarization has inspired a lot of research. Many of the techniques have been proposed by different researchers, but most of them are not able to create summaries of general videos, i.e., they are generally suitable for a set of categories of videos. To overcome this problem, we have introduced a general framework for video summarization, where we have used a pre-trained VGG16 model for feature extraction, and based on the features, we found the frame level importance using a Long Short Term memory (LSTM) network. After that, we selected the frames with high frame-level scores and finally combined them to form the video summary. Experimenting with the different types of videos taken from the different datasets shows that the proposed methodology outperforms the existing state-of-the-art methods.

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Synchrophasor Measurement Prediction Using Modified Time-Series Mixer Model Under Cyber Attacks

Synchrophasor Measurement Prediction Using Modified Time-Ser...

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National Power Systems Conference (NPSC)

作者： Purna Kukadiya Avinash Kumar Pandey Trapti Jain Neminath Hubballi Shailja Shukla Dept. of Electrical Engineering Indian Institute of Technology Indore Indore India Dept. of Electrical Engineering Jabalpur Engineering College Jabalpur India Dept. of Computer Science Engineering Indian Institute of Technology Indore Indore India

ISBN: (数字)9798331519568

ISBN: (纸本)9798331519575

Power systems are critical infrastructures that require robust monitoring and control mechanisms to ensure reliability, stability, and efficiency. It utilizes the data from Phasor Measurement Units (PMUs) and other monitoring devices, which are sent over communication links. Hence, they are subjected to cyber attacks leading to missing or corrupted values. This can cause unreliable operation of the power system. To address the issue, this paper proposes a modified Time-Series Mixer model to accurately predict multivariate measurements under continuous unavailability of data. The proposed model utilizes the time-mixing and feature-mixing layers, which help to capture temporal and spatial correlation to predict the measurements. Further, it can be utilized in real-time applications in case of attack on any communication channel/PMU measurements, as the single model predicts all the PMU measurements. The performance of the proposed model is validated using data generated for the IEEE 14 bus system via RTDS. Numerical results validate the effectiveness of the proposed method, as the errors are significantly smaller than those obtained by the LSTM model-based consecutive PMU measurement prediction technique.

关键词： Power measurement Time series analysis Predictive models Power system stability Phasor measurement units Loss measurement Numerical models Cyberattack Monitoring Mixers

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Recognition System for Libyan Vehicle License Plate 21

Recognition System for Libyan Vehicle License Plate

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7th International Conference on Engineering and MIS, ICEMIS 2021

作者： Almabruk, Tahani A.A. Almaghairbe, Rafig Bukewitin, Talal Roper, Marc Dept. Computer Science Faculty of Science Omar Al-Mukhtar University Bayda Libya Dept. Computer Science Faculty of Arts and Sciences Derna University Derna Libya Dept. Computer Science Higher Institute for Science and Technology Bayda Libya Dept. Computer and Information Sciences Faculty of Science Strathclyde University Glasgow United Kingdom

ISBN: (纸本)9781450390446

Automatic license plate recognition system plays an essential role in real life applications, especially those related to security and traffic managements. It essentially extracts and recognizes number plate information from videos or captured images of the targeted vehicle. Vehicle license plates differ from one country to another and because of this the effectiveness of implementing any particular method or system varies based on the plate type. In this study, we present an automatic detection, segmentation and recognition system for Libyan vehicle license plates. The main challenge in this work is our determination to use images of real vehicle plates in Libya, and the majority of these plates are not in a good condition because of poor vehicle maintenance. Three different approaches were used in the proposed system as follows: (1) Projection histogram based approach is used to locate the authorized plate license;(2) Connected component analysis based approach is used to segment the plate characters;(3) The template matching based approach is used to recognise the extracted characters. The proposed system was tested on 200 vehicle images varying in illumination conditions and backgrounds. The detection accuracy of the implemented system was 87%, the segmentation accuracy was 90% and the recognition accuracy was 86%. © 2021 ACM.

关键词： Template matching

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A Smart Manufacturing Ecosystem for Industry 5.0 using Cloud-based Collaborative Learning at the Edge

A Smart Manufacturing Ecosystem for Industry 5.0 using Cloud...

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IEEE Symposium on Network Operations and Management

作者： Salman Javed Saleha Javed Jan van Deventer Hamam Mokayed Jerker Delsing Dept. of Computer Science Electrical and Space Engineering Lulea University of Technology Luleå Sweden

In the modern manufacturing industry, collaborative architectures are growing in popularity. We propose an Industry 5.0 value-driven manufacturing process automation ecosystem in which each edge automation system is based on a local cloud and has a service-oriented architecture. Additionally, we integrate cloud-based collaborative learning (CCL) across building energy management, logistic robot management, production line management, and human worker Aide local clouds to facilitate shared learning and collaborate in generating manufacturing workflows. Consequently, the workflow management system generates the most effective and Industry 5.0-driven workflow recipes. In addition to managing energy for a sustainable climate and executing a cost-effective, optimized, and resilient manufacturing process, this work ensures the well-being of human workers. This work has significant implications for future work, as the ecosystem can be deployed and tested for any industrial use case.

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An Ensemble Learning Approach to Detect the Presence of Diabetic Retinopathy based on Inception Module

An Ensemble Learning Approach to Detect the Presence of Diab...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： T. Aswini Devi Sathwik Yamana Harshankar Reddy G Vedh Sivaji Kumar Sangita Swapneel Kulkarni Dept. of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

In recent times, Diabetic Retinopathy (DR) has become a complication of major degree for diabetic patients, in which the blood vessels present in the eye retina are severely damaged. This in most cases has lead to a loss of vision as observed from records of DR patients, and if left untreated, can cause blindness. As per the estimates of the World Health Organization, DR will impact around 224 million people by 2040. This research study presents a convolutional neural network (CNN) model, followed by employing ensemble learning over various ML algorithms through max voting, for the task of image classification for detecting Diabetic Retinopathy using color fundus images, with the goal of improving the accuracy and efficiency of the diagnostic process. The proposed model is designed to address the challenges of real-world data, such as variability in the appearance of images that belong to the same class and class imbalance. The dataset used for this study is EYEPACS, which is widely used for training and testing models for detecting Diabetic Retinopathy. The Metrics that are used for measuring the performance of the proposed model are accuracy, precision, and F1-S core. This study obtains an accuracy of 95.4 percent.

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Free Verse Figure Of Speech Calculator: An Aid To Poet Style Determination

Free Verse Figure Of Speech Calculator: An Aid To Poet Style...

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International Conference on Computing and Networking technology (ICCNT)

作者： Komal Naaz Niraj Kumar Singh Dept. of Computer Science & Engg. Birla Institute of Technology Mesra Ranchi India

Poetry writing and analysis are both qualitative subjects. To get over any possible biases of human perspective it is necessary to map these poetic features on a scale of real numbers. Free verse is a form of poetry that is unrestricted by the requirements of metre and rhyme. This article is focused on free verse compositions viewed through the lens of rhetorical properties or figures of speech. The state-of-the-art model FoSCal cannot handle free verse compositions. On the other hand, FVRCal is a state-of-the-art tool for free verse that computes rhyme (not FoS) on a numerical scale. The proposed tool, FVFoSCal, covers a broader section of alankāra than the state-of-the-art model. It can measure the alankāra score for any free verse composition. The scores generated by the proffered tool form a dataset, FoSSset, which is later used to determinethestyleofthepoetusingsuitablestatistical tests. The statistical analysis performed in this experiment establishes commonality and diversity among the four renowned poets of Hindi literature, namely; Bachchan, Dushyant Kumar, Nirala, and Agyey.

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Campus Network Configuration, Monitoring and Data Flow Simulation using Cisco Packet Tracer

Campus Network Configuration, Monitoring and Data Flow Simul...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Shahadat Hoshen Moz Md Apu Hosen Nice Fatema Islam Tanny Dept. of Computer Science and Engineering Northern University of Business and Technology Khulna Khulna Bangladesh

The key to communication is being digitalized every day, in order to keep up with technology everyone needs to update their skillset with it. A campus network is connected to us more because all are often a part of it. Everyone uses it most of the time for their daily activities on campus and also for educational purposes, depending on their needs. To ensure that data cannot be corrupted, it is crucial to make specific alterations to it. Integrity and reliability are key issues in all information switch difficulties in order to ensure safe and simple transfers among customers. As a result, this research has developed a secure campus network and a stable campus community for sending and getting data among end users with high levels of security. Using a Cisco packet tracer, this study proposed a topology for a campus of multiple networks and virtual local area networks (VLANs), as well as the most crucial security configurations for the networking used in this design. There used a massive variety of protocols to defend and accommodate the customers of the secure campus network scheme and also added switches, routers, firewalls, Wi-Fi routers, IP routers and printers to this network in order to make communication smoother and more efficient. Digital campus is the place where the next generation goes to gain education and knowledge about the world makes the campus network more secure and tech-focused. The students and other personnel can learn and even get familiar with them all and also help everyone.

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