Retracted: Aspect‐based sentiment analysis employing linguistics content over social media for Web of Things
IET Communications
The above article from IET Communications, published online on 12 November 2022 in Wiley Online L... more The above article from IET Communications, published online on 12 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in ... more The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16...
Architecture for Garbage Monitoring System using Integrated Technologies with Short Literature Survey
2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)
The innovations given by rapid development of science and technology may bless the society in han... more The innovations given by rapid development of science and technology may bless the society in handling environmental issues efficiently. The main driving force behind environmental issues is population growth. As the population is increasing, the management of waste is becoming a difficult and challenging task for the concerned authorities. The utilization and deployment of advanced technologies namely Internet of Things, Cloud Computing and Image processing may aid the authorities to timely manage the garbage and keep the environment clean. In time management of waste will help the society to meet the alarming situations like inhaling of toxic gases and various health diseases. This paper discusses the state-of-art technologies that have been employed in literature for waste management. Also, the authors proposed a novel architecture for waste management that utilizes the concept of IoT and image processing. The proposed architecture acts as a surveillance system to monitor the overflow of the garbage and delivers a message to the concerned authorities to take the necessary and immediate action.
International Journal of Computer Mathematics, 2020
Nature inspired algorithms emulate the mathematical and innovative techniques for non-linear and ... more Nature inspired algorithms emulate the mathematical and innovative techniques for non-linear and real life problems worldwide. Imaging technology is emerging out as one of the most prominent and widely used domain in medical field such as cancerous cell nuclei detection, blood vessel segmentation, study of organs or structure of tissues and many more. Nature inspired algorithms emulate the mathematical and innovative techniques for non-linear and real life problems and can be applied to segment or analyse the images. An efficient image segmentation technique may help the subject expert like radiologist, pathologist for early and effective examination or diagnosis of disease. The authors proposed a firefly based segmentation technique that can be employed to segment the breast cancer image regardless the type or modality of the image. The effectiveness of the proposed technique is validated by comparing the procured results with the existing state-of-art techniques.
h i g h l i g h t s g r a p h i c a l a b s t r a c t • Review of state-of-art computer assisted ... more h i g h l i g h t s g r a p h i c a l a b s t r a c t • Review of state-of-art computer assisted diagnosis (CAD) system for breast cancer. • Explicit categorization and remarks of techniques reported over the past 10 years. • Major recent trend analysis for segmentation and classification phase. • Provides current status and future scope of CAD system in histopathology images. • Useful for clinicians to get second opinion from CAD system for early diagnosis.
The image segmentation is the basic step in the image processing involved in the processing of me... more The image segmentation is the basic step in the image processing involved in the processing of medical images. Over the past two decades, medical image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in research studies. This article surveys the techniques and their effect on chest Xray images. The objective of this work is to study the key similarities and differences among the different published methods while highlighting their strengths and weaknesses on chest X-ray images. The reason is to assist the researchers in the choice of an appropriate lung segmentation methodology. We additionally give a complete portrayal of the existing few basic methods when combined with preprocessing method that can be utilized as a part of the segmentation. A discussion and fair analysis justified with experimental results along with quantitative correlation of the outcomes on 247 images of JSRT through Dice coefficient exhibited.
As the world gradually recovers from the impacts of COVID-19, the recent global spread of Monkeyp... more As the world gradually recovers from the impacts of COVID-19, the recent global spread of Monkeypox disease has raised concerns about another potential pandemic, highlighting the urgency of early detection and intervention to curb its transmission. Deep Learning (DL)-based disease prediction presents a promising solution, offering affordable and accessible diagnostic services. In this study, we harnessed Transfer Learning (TL) techniques to tweak and assess the performance of an array of six different DL models, encompassing VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, VGG19, and Vision Transformer (ViT). Among this diverse collection, it was the modified versions of the VGG19 and MobileNetV2 models that outshone the others, boasting striking accuracy rates ranging from an impressive 93% to an astounding 99%. Our results echo the findings of recent research endeavors that similarly showcase enhanced performance when developing disease diagnostic models armed with the power of TL. To add to this, we used Local Interpretable Model Agnostic Explanations (LIME) to lend a sense of transparency to our model's predictions and identify the crucial features correlating with the onset of Monkeypox disease. These findings offer significant implications for disease prevention and control efforts, particularly in remote and resource-limited areas.
Over 6.5 million people around the world have lost their lives due to the highly contagious COVID... more Over 6.5 million people around the world have lost their lives due to the highly contagious COVID 19 virus. The virus increases the danger of fatal health effects by damaging the lungs severely. The only method to reduce mortality and contain the spread of this disease is by promptly detecting it. Recently, deep learning has become one of the most prominent approaches to CAD, helping surgeons make more informed decisions. But deep learning models are computation hungry and devices with TPUs and GPUs are needed to run these models. The current focus of machine learning research is on developing models that can be deployed on mobile and edge devices. To this end, this research aims to develop a concise convolutional neural network-based computer-aided diagnostic system for detecting the COVID 19 virus in X-ray images, which may be deployed on devices with limited processing resources, such as mobile phones and tablets. The proposed architecture aspires to use the image enhancement in first phase and data augmentation in the second phase for image preprocessing, additionally hyperparameters are also optimized to obtain the optimal parameter settings in the third phase that provide the best results. The experimental analysis has provided empirical evidence of the impact of image enhancement, data augmentation, and hyperparameter tuning on the proposed convolutional neural network model, which increased accuracy from 94 to 98%. Results from the evaluation show that the suggested method gives an accuracy of 98%, which is better than popular transfer learning models like Xception, Resnet50, and Inception. Coronavirus 1 was identified in Wuhan, China, in 2019, and it has affected more than 760 million people around the globe 2 . The virus causes respiratory diseases such as Middle East Respiratory Syndrome, Severe Acute Respiratory Syndrome (SARS) 3 , and other deadly complications. The most common symptoms are cough, sore throat, headache, fever and fatigue ( 19. who. int/). The virus is passed from person to person by droplets of breath. During past COVID 19 waves, the sudden surge in cases made it difficult for the laboratories to confirm positive or negative cases using RT-PCR as it is a time-consuming method and has high false-negative rates 4 , and is costly also. Therefore, development of real time diagnostic tools, which can be executed in mobile and edge devices is the need of the hour 5 . Since most diagnostic centers already have X-ray machines, and because acquiring an X-ray takes less time than getting the RT-PCR done, using chest X-rays of patients 6 satisfies the urgent need for a speedy diagnostic approach. Deep learning 7,8 is one of the most promising techniques that provides efficient results in the accurate diagnosis of the diseases from images and is widely used in the medical field to diagnose severe diseases at early stages 9 . It is made up of input layer, activation functions, hidden layers and also output layer. The mathematical equation in each step with feed forward and backward functions can help in finding better results 10 . An activation function is used to activate and deactivate the neurons and basically defines the output of a node. Convolutional neural networks (CNN) are deep learning neural network made up of neurons which are experienced, self-optimized and are used primarily by researchers working in the field of disease diagnosis from images. CNN's key popularity
New algorithm for finding frequent and rare itemsets
In this paper, we have proposed two new approaches Per_SPAM and Mis_SPAM. Per_SPAM is the periodi... more In this paper, we have proposed two new approaches Per_SPAM and Mis_SPAM. Per_SPAM is the periodic approach to discover frequent patterns in item sequences. Mis_SPAM is the minimum item support approach to extract rare items. The outcome of these approaches is the proposal of a new algorithm called New_SPAM which incorporates periodic and minimum item support approach. The new algorithm assigns minimum item support values for frequent as well as rare items based on their item supports. The performance of proposed algorithm and approaches is compared against frequent sequence count and running time. Experimental results on real-life datasets show that the proposed approaches and new algorithm have better efficiency over the existing algorithm CM_SPAM.
Comparative study of recent sequential pattern mining algorithms on web clickstream data
As users access the web pages of a website, sequences containing the web pages are stored in the ... more As users access the web pages of a website, sequences containing the web pages are stored in the web server logs. These web server logs can be used to analyze the behavior of website users. This paper presents a pioneering comparison study of five most important sequential pattern mining algorithms on web click stream datasets. The performance of algorithms is compared against running time and maximum memory usage. Experimental results have been evaluated and compared to find an algorithm which has better performance on some real-life datasets.
Officials in the field of public health are concerned about a new monkeypox outbreak, even though... more Officials in the field of public health are concerned about a new monkeypox outbreak, even though the world is now experiencing an epidemic of COVID-19. Similar to variola, cowpox, and vaccinia, an orthopoxvirus with two double-stranded strands causes monkeypox. The present pandemic has been propagated sexually on a massive scale, particularly among individuals who identify as gay or bisexual. In this instance, the speed with which monkeypox was diagnosed is the most important aspect. It is possible that the technology of machine learning could be of significant assistance in accurately diagnosing the monkeypox sickness before it can spread to more people. This study aims to determine a solution to the problem by developing a model for the diagnosis of monkeypox through machine learning and image processing methods. To accomplish this, data augmentation approaches have been applied to avoid the chances of the model's overfitting. Then, the transfer-learning strategy was utilized to apply the preprocessed dataset to a total of six different Deep Learning (DL) models. The model with the best precision, recall, and accuracy performance matrices was selected after those three metrics were compared to one another. A model called ''PoxNet22'' has been proposed by performing fine-tuning the model that has performed the best. PoxNet22 outperforms other methods in its classification of monkeypox, which it does with 100% precision, recall, and accuracy. The outcomes of this study will prove to be extremely helpful to clinicians in the process of classifying and diagnosing monkeypox sickness.
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