A novel deep learning-based approach for detecting attacks in social IoT.

In the innovative concept of the "Social Internet of Things" (IoT), the IoT is combined with social platforms so that inanimate devices can form their interactions with one another. Still, customers have a wary attitude toward this new standard. They worry that their privacy will be invaded and their information will be made public. IoT won't become a frontrunner technology until we have tried true techniques to improve trustworthy connections between nodes. As a result, data privacy becomes extremely difficult, further increasing the difficulty of providing high-quality services and absolute safety. Several articles have attempted to analyze this issue. To categorize safe nodes in the IoT network, they suggested many models based on various attributes and aggregation techniques. In contrast, prior works failed to provide a means of identifying fraudulent nodes or distinguishing between different forms of assaults. To identify attacks carried out by hostile nodes and separate them from the network, we propose a novel Multi-hop Convolutional Neural Network with an attention mechanism (MH-CNN-AM). To achieve the best performance in the suggested research, performance measures including accuracy, precision, recall, F1-score, and MAE are studied and compared with the of existing methodologies.

Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images.

Rice (Oryza sativa) is India's major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy.

Severe Acute Respiratory Syndrome Coronavirus-2 - A Surge of CoronaVirus Disease-2019: An Epidemiological Study in Coimbatore District.

Context: Coronavirus disease-2019 (COVID-19) is an ongoing pneumonia-like cluster syndrome which originated in Wuhan city of China and is still now on escalation, causing severe outbreaks all over the world. Being a ribonucleic acid (RNA) virus which has the low proofreading RNA-dependent RNA polymerase leads to many mutations and that serves as the major cause for the progress of the disease. As per the recent research works done, 99% of COVID-19 severe acute respiratory syndrome coronavirus (SARS-CoV-2) are due to pangolin-associated coronavirus which causes the super spreading events of coronavirus. SARS-CoV-2 was identified in the nasopharyngeal swabs received in the viral transport medium at optimum temperature. Materials and Methods: The tests were conducted for a time period of 1 year from July 2020 to June 2021. A total of 77,824 samples were tested in the laboratory as per ICMR guidelines using approved RNA extraction kits and polymerase chain reaction kits. Results: In the total of 77,824 samples tested in our laboratory, 14174 positives were identified. In that, about seven positive cases (0.004%) were identified in the month of July 2020 which increased to the maximum in September 2020 to about 865 positive cases (6%) which is the peak of first wave COVID-19 in Coimbatore district, Tamil Nadu. Out of 77,824 samples tested, the actual cumulative laboratory-confirmed positive cases of about 14174 were identified. In that, 7731 (55%) male positive cases were identified, 6171 (43%) female positive cases were identified, and 270 (2%) children who were below 12 years of age also were tested positive. Conclusions: The findings of the study indicated a high predominance of SARS-CoV-2 infection in the male gender population when compared to females and children below 12 years of age in Coimbatore district as of June 2021. The surge of cases was high in September 2020 as well as in May 2021, indicating the first and second wave of COVID-19.

IOTEML: An Internet of Things (IoT)-Based Enhanced Machine Learning Model for Tumour Investigation.

In the current age of technology, various diseases in the body are also on the rise. Tumours that cause more discomfort in the body are set to increase the discomfort of most patients. Patients experience different effects depending on the tumour size and type. Future developments in the medical field are moving towards the development of tools based on IoT devices. These advances will in the future follow special features designed based on multiple machine learning developed by artificial intelligence. In that order, an improved algorithm named Internet of Things-based enhanced machine learning is proposed in this paper. What makes it special is that it involves separate functions to diagnose each type of tumour. It analyzes and calculates things like the size, shape, and location of the tumour. Cure from cancer is determined by the stage at which we find cancer. Early detection of cancer has the potential to cure quickly. At a saturation point, the proposed Internet of Things-based enhanced machine learning model achieved 94.56% of accuracy, 94.12% of precision, 94.98% of recall, 95.12% of F1-score, and 1856 ms of execution time. The simulation is conducted to test the efficacy of the model, and the results of the simulation show that the proposed Internet of Things-based enhanced machine learning obtains a higher rate of intelligence than other methods.