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Early-Stage Cervical Cancer Detection via Ensemble Learning and Image Feature Integration
Cervical cancer ranks as the fourth most common malignancy worldwide and poses a significant threat, particularly in resource-constrained regions. Automated diagnostic approaches, leveraging colposcope image analysis, hold great promise in curbing the impact of this disease. In this study, we introduce an ensemble of machine learning and deep learning models, including DenseNet 121, ResNet 50, and XGBoost to classify the cervical intraepithelial neoplasia. A novel feature integration is proposed which ensembles the results of the individual models in five fold validation process. Our methodology is deployed on a dataset sourced from the International Agency for Cancer Research. The results from the proposed framework have shown to be accurate, robust and dependable. This method can be utilized for achieving automatic identification of cervical cancer in early stages so it can be treated appropriately. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Comparative Performance Analysis of Deep Learning Models in Cervical Cancer Detection
Cervical cancer one of the four most common malignancies worldwide and poses a significant threat, particularly in resource-constrained regions. Automated diagnostic approaches, leveraging colposcope image analysis, hold great promise in curbing the impact of this disease. In this paper, we deploy a range of deep learning methods, including DenseNet 121, ResNet 50, AlexNet and VGG 16 to classify the cervical intraepithelial neoplasia. Our methodology is deployed on a dataset sourced from a Cancer Research institute in India. The current experiment aims to establish the execution of the state-of-the-art pretrained frameworks in deep learning. This will be a baseline experiment for researcher who aim to develop further deep learning models for cervical cancer diagnosis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
CeLaTis: A Large Scale Multimodal Dataset with Deep Region Network to Diagnose Cervical Cancer
Cervical cancer is a leading cause of mortality in third world countries. Although there are multiple ways of screening cervical cancer, colposcope image analysis is considered to be standard routine method of diagnosis. Due to factors like lack of skilled personnel and interobserver variability, there is a need for automated diagnostic support for cervical cancer. However, artificial intelligence solutions for medical image analysis done through deep and machine learning models require high quality, non-erroneous and sufficient amount of data. Owing to the lack of such established benchmark datasets for the colposcope images, this work aims at establishing a standard benchmark multi state colposcope image dataset that also contains clinical findings pertaining to each case. In order to establish the quality of the images, mask R-CNN method is used for segmenting the images. Subsequently, a series of IMAGENet pretrained deep learning models are deployed on the dataset to evaluate the performance. The dataset will be made available upon request for strictly research purposes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Advanced Cervical Lesion Detection using Deep Learning Techniques
Cervical cancer has been one of the common causes for mortality by cancer in women across the world. But there are currently not enough skilled colposcopists, and the training process is drawn out. This implicates that there is a significant scope for artificial intelligence based computational models for segmentation of colposcope images. This paper proposes a segmentation network to accurately segment the cervix region and acetowhite lesions in a cervigram. This research can lay a foundation for research aiming to classify the cervix malignancy using AI. The method performed with a precision of 0.73870.1541, accuracy of 0.9291, recall of 0.79120.1439, a dice score of 0.74310.1506 and specificity of 0.95890.0131. The results prove that the model is reliable and robust. 2024 IEEE. -
A Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer
Latest innovations in technology and computer science have opened up ample scope for tremendous advances in the healthcare field. Automated diagnosis of various medical problems has benefitted from advances in machine learning and deep learning models. Cancer diagnosis, prognosis prediction and classification have been the focus of an immense amount of research and development in intelligent systems. One of the major concerns of health and the reason for mortality in women is cervical cancer. It is the fourth most common cancer in women, as well as one of the top reasons of mortality in developing countries. Cervical cancer can be treated completely if it is diagnosed in its early stages. The acetowhite lesions are the critical informative features of the cervix. The current study proposes a novel feature engineering strategy called lesion feature extraction (LFE) followed by a lesion recognition algorithm (LRA) developed using a deep learning strategy embedded with a Gaussian mixture model with expectation maximum (EM) algorithm. The model performed with an accuracy of 0.943, sensitivity of 0.921 and specificity of 0.891. The proposed method will enable early, accurate diagnosis of cervical cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Multimodal Early Fusion Strategy Based on Deep Learning Methods for Cervical Cancer Identification
It is essential to enhance the accuracy of automatic cervical cancer diagnosis by combining multiple forms of information obtained from a patients primary examination. However, existing multimodal systems are not very effective in detecting correlations between different types of data, leading to low sensitivity but high specificity. This study introduces a deep learning system for automatic diagnosis of cervical cancer by incorporating multiple sources of data. First, a convolutional neural network (CNN) to transform the image database to a vector that can be combined with non-image datasets is used. Subsequently, an investigation of jointly the nonlinear connections between all image and non-image data in a deep neural network is performed. Proposed deep learning-based method creates a unified system that takes advantage of both image and non-image data. It achieves an impressive 89.32% sensitivity at 91.6% specificity when diagnosing cervical intraepithelial neoplasia on a wide-ranging dataset. This result is far superior to any single-source system or prior multimodal approaches. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Specular Reflection Removal Techniques in Cervix Image: A Comprehensive Review
Cancer detection through medical image segmentation and classification is possible owing to the advancement in image processing techniques. Segmentation and classification tasks carried out to predict and classify diseases need to be dependable and precise. Specular reflections are the high-intensity and low-saturation areas that reflect the light from the probing devices that capture the picture of the organ surface. These areas sometimes mimic the features that are key identifying factors for cancers like acetowhite lesions. This review article examines the various methods proposed for removing specular reflections from medical images, especially those captured by colposcope. The fundamentals of specular reflection removal and its associated challenges are discussed. The paper reviews several state-of-the-art approaches for specular reflection removal. The comprehensive review can be a strong foundation for researchers looking to decide on appropriate techniques to employ in their respective research approaches. 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Specular Reflection Removal Technique in Cervigrams
Cancer detection through medical image segmentation and classification is possible owing to the advancement in image processing techniques. Segmentation and classification tasks carried out to predict and classify diseases need to be dependable and precise. Specular reflections are the high-intensity and low-saturation areas that reflect the light from the probing devices that capture the picture of the organ surface. These areas sometimes mimic the features that are key identifying factors for cancers like acetowhite lesions. This review article examines the various methods proposed for removing specular reflections from medical images, especially those captured by colposcopes. The fundamentals of specular reflection removal and its associated challenges are discussed. The paper reviews several prominent approaches for removal of specular reflections proposes a novel method to remove the specular reflections. The comprehensive review can be a strong foundation for researchers looking to decide on appropriate techniques to employ in their respective research approaches. 2023 IEEE. -
Enhancing Mobility: A Smart Cane with Integrated Navigation System and Voice-Assisted Guidance for the Visually Impaired
Blindness is a condition which affects many people, and for the affected people, quality of life can take a big hit. Most blind people already use walking sticks to feel the terrain in front of them as they move around and navigate using touch and sound. However, they cannot judge distances to objects until the cane actually hits the object. In some cases, the contact with the cane may damage the object. Hence, it may be better to have some early warning system so that there is less likelihood of causing damage. This paper presents the design and development of a 'Smart Cane' aimed at enhancing mobility and safety for visually impaired individuals. The cane incorporates ultrasonic sensors to detect objects in the user's surroundings. When an object is detected within a specified distance range, the cane provides haptic feedback through a bidirectional vibration motor, alerting the user to its presence. The microcontroller-based system processes data from both sensors and efficiently manages power consumption to ensure extended battery life. The device's design includes user-friendly controls and an ergonomic enclosure to offer ease of use and protection for the electronic components. Further, there is built-in navigation via online Map API. With the convenience of navigating oneself without external assistance, the 'Smart Cane' demonstrates great potential to improve the independence and confidence of visually impaired individuals in navigating their environments safely. 2024 IEEE. -
Restrained geodetic domination in the power of a graph
For a graph G = (V,E), S ? V(G) is a restrained geodetic dominating set, if S is a geodetic dominating (gd) set and never consists an isolated vertex. The least cardinality of such a set is known as the restrained geodetic domination (rgd) number. The power of a graph G is denoted as Gk and is obtained from G by making adjacency between the vertices provided the distance between those vertices must be at most k. In this study, we discussed geodetic number and rgd number of Gk. 2024 Author(s). -
User Authentication with Graphical Passwords using Hybrid Images and Hash Function
As per human psychology, people remember visual objects more than texts. Although many user authentication mechanisms are based on text passwords, biometric characteristics, tokens, etc., image passwords have proven to be a substitute due to its ease of use and reliability. The technological advancements and evolutions in authentication mechanisms brought greater convenience but increased the probability of exposing passwords through various attacks like shoulder-surfing, dictionary, key-logger, and social engineering attacks. The proposed methodology addresses these vulnerabilities and ensures to keep up the usability of graphical passwords. The system displays hybrid images that users need to recognize and type the randomly generated alphanumeric or special character values associated with each of them. A mechanism to generate One Time Password (OTP) is included for additional security. As a result, it is difficult for an attacker to capture and misuse the password. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Captcha-Based Defense Mechanism to Prevent DoS Attacks
The denial of service (DoS) attack, in the current scenario, is more vulnerable to the banking system and online transactions. Conventional mechanism of DoS attacks consumes a lot of bandwidth, and there will always be performance degradation with respect to the traffic in any of the communication networks. As there is an advent over the network bandwidth, in the current era, DoS attacks have been moved from the network to servers and API. An idea has been proposed which is CAPTCHA-based defense, a purely system-based approach. In the normal case, the protection strategy for DDoS attacks can be achieved with the help of many session schedulers. The main advantage is to efficiently avoid the DoS attacks and increase the server speed as well as to avoid congestion and data loss. This is majorly concerned in a wired network to reduce the delays and to avoid congestion during attacks. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Multilevel Security and Dual OTP System for Online Transaction Against Attacks
In the current internet technology, most of the transactions to banking system are effective through online transaction. Predominantly all these e-transactions are done through e-commerce web sites with the help of credit/debit cards, net banking and lot of other payable apps. So, every online transaction is prone to vulnerable attacks by the fraudulent websites and intruders in the network. As there are many security measures incorporated against security vulnerabilities, network thieves are smart enough to retrieve the passwords and break other security mechanisms. At present situation of digital world, we need to design a secured online transaction system for banking using multilevel encryption of blowfish and AES algorithms incorporated with dual OTP technique. The performance of the proposed methodology is analyzed with respect to number of bytes encrypted per unit time and we conclude that the multilevel encryption provides better security system with faster encryption standards than the ones that are currently in use. 2019 IEEE. -
Improving Renewable Energy Operations in Smart Grids through Machine Learning
This paper reviews the work in the areas of machine learning's role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine learning in paving the way for a greener, more efficient energy future. The Authors, published by EDP Sciences, 2024. -
A Review on Condition Monitoring of Wind Turbines Using Machine Learning Techniques
This document examines the most up-to-date research on the application of machine learning (ML) techniques in monitoring the conditions of wind turbines. The focus is on classification methods, which are used to identify different types of faults. The analysis revealed that the majority of the research utilizes Supervisory Control and Data Acquisition (SCADA) information, with neural networks, support vector machines, and decision trees being the most prevalent machine learning algorithms. The review also identifies several areas for future research, such as the development of more robust ML models that can handle noisy data and the use of ML methods for prognosis (predicting future faults). The Authors, published by EDP Sciences, 2024. -
Fusion of medical image using STSVD
The process of uniting medical images which are taken from different types of images to make them as one image is a Medical Image Fusion. This is performed to increase the image information content and also to reduce the randomness and redundancy which is used for clinical applicability. In this paper a new method called Shearlet Transform (ST) is applied on image by using the Singular Value Decomposition (SVD) to improve the information content of the images. Here two different images Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) are taken for fusing. Initially the ST is applied on the two input images, then for low frequency coefficients the SVD method is applied for fusing purpose and for high frequency coefficients different method is applied. Then fuse the low and high frequency coefficients. Then the Inverse Shearlet Transform (IST) is applied to rebuild the fused image. To carry out the experiments three benchmark images are used and are compared with the progressive techniques. The results show that the proposed method exceeds many progressive techniques. Springer Nature Singapore Pte Ltd. 2017. -
Earlier Stage Identification of Bone Cancer with Regularized ELM
A major focus of current research in the field of image processing is the application of such methods to the field of medical imaging. While dealing with biological issues like fractures, canoers, ulcers, etc., image processing facilitated pinpointing the precise cause and tailoring a remedy. In the field of tumor identification, medical imaging has set a new standard by overcoming a number of challenges. Medical imaging is the practice of generating images of the human body for diagnostic or exploratory purposes. Because of its high image quality, MRI is the method of choice for detecting tumors. This research study proposes the integration of RLM to detect tumors and presents an automatic bone cancer detection system to assist oncologists in making early diagnosis of bone malignancies, which in turn allows patients to receive treatment as soon as possible. This research work also proposes to detect bone tumors by using a combination of the RELM based M3 filtering, Canny Edge segmentation, and the Enhanced Harris corner approach. When compared to other models like CNN, ELM, and RNN, the suggested technique achieves an accuracy of around 97.55%. 2023 IEEE. -
Catalyzing Green Mobility: Consumer Preferences for Green Energy Vehicles
Due to growing urbanization and the increase of vehicles, most Indian cities endure traffic congestion and significant air pollution. As a result, alternate technology in autos, such as electric vehicles, may become necessary (EV). This study aims to identify consumer preferences toward electric vehicles in the Indian market. This research conducted a survey and analyzed the opinions of people regarding their preferences for electric vehicles, demographics, and some of the demotivation which might be stopping them to switch to electric vehicles altogether. This research will help in determining different factors influencing the perception of consumers toward electric vehicles and what they expect when they think about purchasing a new electric vehicle. It is important to understand that electric vehicles are really getting popular now because of the rising fuel prices and environmental concerns. People are thinking about electric vehicles and replacing them with their regular petrol or diesel vehicles. In this research there might be some challenges or roadblocks in switching to electric vehicles. This research found out that despite a favorable attitude toward electric vehicles, individuals are hesitant to transition to electric vehicles due to different hurdles connected with them. This research found out that mostly the preferences of the consumers are good charging infrastructure, a good range of the electric vehicle, pocket-friendly vehicles are the most common preferences of consumers buying an electric vehicle. 2023 EDP Sciences. All rights reserved. -
Efficient multipath model based cross layer routing techniques for Gauss Markov movable node management in MANET
This research unveils an innovative cross-layer routing methodology tailored for managing Gauss Markov mobile nodes within MANETs. The primary focus deceits cutting-edge inspiring network performance through the efficient utilization of resources and the steadfast maintenance of mobile node connectivity. Central to this model is the implementation of joint optimization, which takes into account both node mobility patterns and resource allocation dynamics to pinpoint the most favorable data transmission pathway. Incorporating multipath routing, the methodology enables the simultaneous exploration of multiple transmission routes, thereby fortifying the network against potential link failures and disruptions. By embracing a cross-layer approach, it seamlessly integrates functionalities across network, and steering layers, thereby amplifying the complete system efficacy. Comprehensive simulations conducted reveal the superior performance of this approach compared to existing techniques, particularly in terms of network throughput, latency reduction, and augmentation of packet delivery ratios. Such findings underscore the immense potential of this methodology across a spectrum of MANET applications that demand streamlined and dependable data transmission mechanisms. 2024 Author(s). -
HumanComputer Interactions with ArtificialIntelligence and Future Trends of HCIA Study
Artificial Intelligence, the name itself depicts the meaning that providing the knowledge of human to the machine artificially. AI is not a sense or feeling but the software or a model evolved to do complex tasks like human beings. With the invention of computer it has become so easy to do day to day jobs without much effort. HCI is all about interacting with computers. Now-a days it is possible to mesh with the computer through voice, touch, eye movement, and hand gestures. HCI has many challenges but has established in grand manner with the support of Artificial Intelligence. This study provides some important roles of Artificial Intelligence in HCI and its future development. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.