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Skin Cancer Prediction by Incorporating Bio-inspired Optimization in Deep Neural Network

SN computer science 2024年第8期5卷 1-20页

作者： Mundada, Monica R. Sowmya, B.J. Supreeth, S. Prabhu, Smitha G. Mahesh, K. Vishwanath, Y. Rohith, S. Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bengaluru 560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru 560054 India School of Computer Science and Engineering REVA University Bengaluru 560064 India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering Sir M Visvesvaraya Institute of Technology Bengaluru India School of Computer Science & Engineering Presidency University Bengaluru 560064 India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Bengaluru 562164 India

This study offers a unified surgical conception of skin neoplasms using deep learning and genetic optimization techniques to improve classification values and speed of diagnosis. In this case, the architecture of Convolutional Neural Networks such as MobileNet, Xception and Inception is enhanced on the basis of the creative algorithms of Particle Swarm Optimization and Bat Algorithm to adjust the hyperparameters. The Heroku hosted cloud solution permits registered users to upload pictures of skin lesions via the web interface, where the picture is then processed through a Deep Neural Network (DNN) model that classifies the lesion as either melanoma or non-melanoma. The Convolution Neural Network (CNN) models were trained and evaluated on large publicly available datasets of skin lesions like ISIC, HAM10000 and the model performance was evaluated based on the effect of varying optimizers. Among all the architectures, Xception with Bat Algorithm (BA) performed best because of the more complex and deeper network architecture and extreme inception modules, which made it possible to extract and classify more complicated features. As a cloud-based solution, this system provides dermatologists and health practitioners with a more practical and workable solution for the detection of skin cancers. Furthermore, it can be utilized in other fields, which implies that this approach could extend its applications beyond dermatological imaging or imaging of patients at risk for other diseases, thus creating an adaptable and effective tool for clinical diagnosis in many areas of medicine. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bio-inspired optimization Deep neural network (DNN) Skin cancer prediction

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Visualisation and Classification of Coffee Leaves via the Use of a Sequential CNN Model Based on Deep Learning 4

Visualisation and Classification of Coffee Leaves via the Us...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Singla, Muskan Singh Gill, Kanwarpartap Upadhyay, Deepak Singh, Vijay Kumar, Gotte Ranjith Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350307757

The coffee industry plays a crucial role in global agriculture and economy. Monitoring the health and classification of coffee plants is vital for optimizing yield and ensuring sustainable production. Coffee plants are very vulnerable to several diseases and pests. The long-term effects of excessive pesticide usage may enhance disease resistance, severely limiting coffee plants' ability to fend off infections. The goal of this project is to create a sophisticated system that employs a deep learning-based Sequential Convolutional Neural Network (CNN) model to visualise and categorise coffee leaves. This study provides a unique method for visualising and categorising coffee leaves using a deep learning-based Sequential CNN model. Coffee plant growers may be able to spot infections more promptly with the aforementioned approach, enhancing India's coffee crop output. The suggested method proposing an accuracy of 97% was created to aid farmers and the agriculture industry. Hence, the created model shows promising accuracy and interpretability outcomes, leading to the growth of precision agriculture in the coffee business. © 2024 IEEE.

关键词： Convolutional neural networks

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The Quantum Threat: Implications for Data Security and the Rise of Post-Quantum Cryptography 9

The Quantum Threat: Implications for Data Security and the R...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Tiwari, Amritansh Chauhan, Rahul Joshi, Nischay Devliyal, Swati Aluvala, Srinivas Kumar, Amit Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

Securing our data is critical in today's digital environment. This study investigates the possible influence of quantum computers on data security. Research has been initiated by learning about quantum computers and their distinct qualities. These computers, which are driven by quantum bits (qubits), can do numerous jobs at the same time, but they also confront unique problems. The RSA Algorithm has been proposed here which is a key component of modern encryption. To protect our data from normal computers, one can use huge prime *** quest then takes to Shor's Algorithm, a quantum innovation designed to decipher complicated codes effectively. We explain how it works and what it means for traditional computing. Author delves into the area of post-quantum cryptography, introducing approaches like lattice cryptography, code-based cryptography, multivariate cryptography and hash-based signatures. These solutions exploit quantum features to guard against quantum computer attacks, albeit they may come with certain trade-offs. This research study emphasizes the constantly changing environment of digital security, emphasizing the importance of adaptive cryptographic systems in an increasingly linked and vulnerable world. © 2024 IEEE.

关键词： Economic and social effects

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Biometric-Based Key Handling Using Variation to Scale Invariant Feature Transform amid COVID-19 Pandemic 7th

Biometric-Based Key Handling Using Variation to Scale Invar...

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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Kaur, Prabhjot Kumar, Nitin Singh, Maheep Department of Computer Science and Engineering National Institute of Technology Uttarakhand Uttarakhand Srinagar India School of Computing DIT University Uttarakhand Dehradun India Department of Computer Science and Engineering PEC Deemed-to-be University Chandigarh India Department of Computer Science and Engineering Doon University Uttarakhand Dehradun India

ISBN: (纸本)9789819950140

The outbreak of COVID-19 pandemic has hardened the biometric recognition, authentication, and cryptographic process based on facial features due to masks coverage. The Biometric cryptosystem (BCS) consisting of touch sensing biometric, such as fingerprint, palmprint, vein, is no longer in use in this scenario. In this context, other key-based biometric cryptosystems based on facial biometric may underperform. Thus, there is a need to simulate existing and implement novel biometric cryptosystems based on other biometric cues such as Ear, Face, Iris, Gait. This paper introduces a novel method for biometric key generation based on Scale Invariant Feature Transform (SIFT). The obtained key is used to encrypt/decrypt the data. At enrollment stage, the biometric key is obtained which subsequently encrypts the data and authentication phase decrypts the secret upon successful biometric match. The proposed method is compared with the state-of-art methods on three datasets namely AMI (ear), FACES (face), and UBIPr (iris). The metrics used to evaluate the performance of proposed method include: NPCR, UACI, PSNR, Correlation, MSE, MAE, SSIM, NRMSE. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics

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Advancing Vehicular Ad-Hoc Network Solutions in Emerging Economies: A Comparative Analysis of V2V Protocols Through Simulation Studies

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SN computer science 2024年第8期5卷 1-13页

作者： Raj, Dharm Ather, Danish Sagar, Anil Kumar Department of Computer Science & amp Engineering Sharda School of Engineering & amp Technology Sharda University Greater Noida India Amity University in Tashkent Tashkent Uzbekistan

This paper aims at moving ahead with Vehicular Ad-Hoc Network (VANET) in the emerging economies via protocol analysis of Vehicle-to-Vehicle (V2V) communication through simulation study. Given the distinct challenges in developing regions and the lack of the infrastructure and resources, the research aims to find out the feasible V2V communication protocols, applicable in VANET settings and profitable. Multiple network simulators that are widely accepted were employed in the network to perform an extensive evaluation of the primary V2V protocols, considering their performance, reliability, and scalability under various conditions that are common in emerging economies. This paper ends up with a different outcome of each protocol and some of them show a better resistance compared to the others in high-density traffic scenarios while others are more suitable to the areas with sparse, low-connectivity. The research allows for a complete evaluation of existing V2V protocols for VANET implementations in these regions with guidelines on critical points to focus on in the future deployment of VANET systems in emerging economies. This research is in line with the general efforts of improving Intelligent Transportation Systems for the developing world, which aims to realize coordinated and intelligent traffic management, safer roads and better overall traffic efficiency. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Communication protocols Intelligent Transportation Systems (ITS) Network simulation Vehicle-to-Vehicle (V2V) communication Vehicular Ad-Hoc Networks (VANETs)

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Revolutionizing Kidney Disease Diagnosis: A Comprehensive CNN-Based Framework for Multi-Class CT Classification

Revolutionizing Kidney Disease Diagnosis: A Comprehensive CN...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Sharma, Gunjan Anand, Vatsala Chauhan, Rahul Pokhariya, Hemant Singh Gupta, Sheifali Sunil, G. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350382693

A major global health concern, kidney infections are linked to rising death rates, especially when they worsen and cause the breakdown of the kidneys. Kidney function is seriously threatened by common kidney disorders such as nephrolithiasis, kidney tumors, and cyst development. Kidney failure, which can be brought on by conditions including tumors, stones, and cysts, can be avoided with prompt diagnosis and treatment. computer-aided diagnostics are essential due to the rising incidence of chronic renal illness, the lack of specialists, and the increased need for evaluation and monitoring. Though artificial intelligence (AI) methods, such as machine and deep learning, have been investigated for the identification of renal illness, their effectiveness is still lacking. In order to fill this gap, this study implements a deep learning-based Convolutional Neural Network (CNN) model for kidney illness prognosis and classification using a benchmark kidney dataset from Computed Tomography. CNN uses data reprocessing to extract features from the CT images. The results show how effective the suggested method is in correctly classifying renal illness, with a noteworthy accuracy of 99.88%, precision of 99.8%, recall of 99.7%, and an F1-score of 0.98. This work represents a potential development in the field of computer-assisted renal health diagnostics by supporting the use of the refined CNN model as a trustworthy instrument for kidney illness identification. This research can be applied in the medical field for the diagnosis of kidney diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Capacitor voltage unbalance minimization for three-phase five-level diode-clamped inverter using hexagonal hysteresis space vector modulation

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 661-671页

作者： Ramkumar, R. Singh, Manmohan Ramkumar, R. Usha, S. Thentral, T. M. Thamizh Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computer Engineering and Application GLA University Mathura India Department of Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Renewable Energy University of Maroua National Advanced School of Engineering of Maroua Maroua Cameroon

This article suggests a method for diminishing the voltage unbalance in a three-phase five-level diode-clamped inverter (DCI) through the use of hexagonal hysteresis space vector modulation (HHSVM). Capacitor voltage balancing leads to enhanced system efficiency, reduced stress on components, enhanced performance, abridged electromagnetic interference, and reduced total harmonic distortion. The proposed modulation technique and its implementation are thoroughly examined in this study, along with modeling and experiment data that show how efficient the method is at lowering the capacitor voltage unbalance in the proposed five-level DCI. Capacitor voltage unbalance is reduced with the use of this HHSVM approach to 0.95%, which is a superior reduction compared to traditional PWM methods. The paper also discusses the advantages of the proposed method over other existing methods, making it a promising solution for practical applications in power electronics systems.

关键词： Diode-clamped inverter (DCI) Capacitor voltage balancing Hexagonal hysteresis space vector modulation (HHSVM) Total harmonic distortion

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Providing an Intelligent Hybrid Routing Method in Wireless Sensor Networks 23th

Providing an Intelligent Hybrid Routing Method in Wireless S...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Hosseinabadi, Ali Asghar Rahmani Mirkamali, Seyedsaeid Hajiabadi, Mahdi Rohani Abraham, Ajith Department of Computer Science University of Regina Regina Canada Department of Computer Engineering and IT Payame Noor University Tehran Iran Faculty of Computer and Information Technology Engineering Islamic Azad University Qazvin Qazvin Iran School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (纸本)9783031647758

Optimal energy consumption in Wireless Sensor Networks (WSNs) is important. Previous research has shown that by organizing network nodes into a number of clusters, one can achieve greater energy efficiency leading to longer network life. The WSN is made up of a very large number of sensor nodes that communicate with each other to perform specific tasks. These nodes have limited energy, processing power, and memory. Because the lifetime of the network depends on the brightness of the nodes, the energy consumption of each node is very important. Therefore, we need to find solutions to reduce the energy consumption of the nodes. One way to reduce energy consumption is clustering, many algorithms have been introduced in this field. In this paper, we optimize Particle Swarm Optimization (PSO) algorithm based on fuzzy logic and PSO algorithm. The fitting criterion will be based on the minimum energy consumed by the nodes of the network during each operation period, It leads to a balance in the energy consumption of the headers and, as a consequence, a lengthening of the life of the network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fuzzy logic

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SCTrans: Multi-scale scRNA-seq Sub-vector Completion Transformer for Gene-selective Cell Type Annotation 33

SCTrans: Multi-scale scRNA-seq Sub-vector Completion Transfo...

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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Lin, Lu Xue, Wen Wei, Xindian Shen, Wenjun Liu, Cheng Wu, Si Wong, Hau San School of Computer Science and Engineering South China University of Technology China Department of Computer Science City University of Hong Kong Hong Kong Department of Bioinformatics Shantou University Medical College China Department of Computer Science Shantou University China

ISBN: (纸本)9781956792041

Cell type annotation is pivotal to single-cell RNA sequencing data (scRNA-seq)-based biological and medical analysis, e.g., identifying biomarkers, exploring cellular heterogeneity, and understanding disease mechanisms. The previous annotation methods typically learn a nonlinear mapping to infer cell type from gene expression vectors, and thus fall short in discovering and associating salient genes with specific cell types. To address this issue, we propose a multi-scale scRNA-seq Sub-vector Completion Transformer, and our model is referred to as SCTrans. Considering that the expressiveness of gene sub-vectors is richer than that of individual genes, we perform multi-scale partitioning on gene vectors followed by masked sub-vector completion, conditioned on unmasked ones. Toward this end, the multi-scale sub-vectors are tokenized, and the intrinsic contextual relationships are modeled via self-attention computation and conditional contrastive regularization imposed on an encoding transformer. By performing mutual learning between the encoder and an additional lightweight counterpart, the salient tokens can be distinguished from the others. As a result, we can perform gene-selective cell type annotation, which contributes to our superior performance over state-of-the-art annotation methods. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

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SMAKAP: Secure Mutual Authentication and Key Agreement Protocol for RFID Systems 17

SMAKAP: Secure Mutual Authentication and Key Agreement Proto...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Naziri, Shayesta Wang, Xu Yu, Guangsheng Xu, Jian Shrestha, Sudhir Liang, Christy Jie School of Electrical and Data Engineering University of Technology SydneyNSW2007 Australia School of Computer Science University of Technology SydneyNSW2007 Australia

ISBN: (纸本)9798331509736

Radio Frequency Identification (RFID) is a crucial technology in the Internet of Things (IoT), enabling seamless wireless communication and data exchange. However, these technologies can pose significant security chal-lenges if not implemented with proper attention to security protocols-especially in communication, where pre-shared keys are not used between active tags and readers for device authentication. Some recent authentication protocols rely solely on a hash function, nonce, and single public kay agreement, which can lead to failure to implement robust security and proper authentication or ineffective for high security application environments. To effectively address these challenges this paper proposes a secure Elliptic Curve Cryptography (ECC) based lightweight mutual authentication protocol utilizing a hybrid key agreement protocol between active tag and reader for secure communication in RFID-enabled devices in the IoT environments. The informal analysis demonstrates a secure communication environment for data privacy and flexibility through effective key management. This protocol is adaptable to various applications by addressing specific requirements and limitations. © 2024 IEEE.

关键词： Authentication Protocol

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