Heart rate Encapsulation and Response Tool using Sentiment Analysis

Users of every system expect it to get better. Providing feedback to the owners or management was difficult but with the advent of technology, it has become handy. Users can now post their comments through online blogs, android apps and websites. Due to the enormous data piling up every second causes a problem in analyzing it. In this paper, sentiment analysis is used for analyzing comments and reviews for hospital management system are demonstrated with real time data. The tools, algorithms and methodology that could fetch accurate results is described. Experimental results indicate 90% of accuracy in proposed system. The review report generated would help the hospital management to identify the positive and negative feedback which further assists them in improving their facilities that could not only create customer satisfaction but also enhanced business processes

Lattice structural analysis on sniffing to denial of service attacks

Sniffing is one of the most prominent causes for most of the attacks in the digitized computing environment. Through various packet analyzers or sniffers available free of cost, the network packets can be captured and analyzed. The sensitive information of the victim like user credentials, passwords, a PIN which is of more considerable interest to the assailants’ can be stolen through sniffers. This is the primary reason for most of the variations of DDoS attacks in the network from a variety of its catalog of attacks. An effective and trusted framework for detecting and preventing these sniffing has greater significance in today’s computing. A counter hack method to avoid data theft is to encrypt sensitive information. This paper provides an analysis of the most prominent sniffing attacks. Moreover, this is one of the most important strides to guarantee system security. Also, a Lattice structure has been derived to prove that sniffing is the prominent activity for DoS or DDoS attacks

Internet of Things and Machine Learning Applications for Smart Precision Agriculture

Agriculture forms the major part of our Indian economy. In the current world, agriculture and irrigation are the essential and foremost sectors. It is a mandatory need to apply information and communication technology in our agricultural industries to aid agriculturalists and farmers to improve vice all stages of crop cultivation and post-harvest. It helps to enhance the country’s G.D.P. Agriculture needs to be assisted by modern automation to produce the maximum yield. The recent development in technology has a significant impact on agriculture. The evolutions of Machine Learning (ML) and the Internet of Things (IoT) have supported researchers to implement this automation in agriculture to support farmers. ML allows farmers to improve yield make use of effective land utilisation, the fruitfulness of the soil, level of water, mineral insufficiencies control pest, trim development and horticulture. Application of remote sensors like temperature, humidity, soil moisture, water level sensors and pH value will provide an idea to on active farming, which will show accuracy as well as practical agriculture to deal with challenges in the field. This advancement could empower agricultural management systems to handle farm data in an orchestrated manner and increase the agribusiness by formulating effective strategies. This paper highlights contribute to an overview of the modern technologies deployed to agriculture and suggests an outline of the current and potential applications, and discusses the challenges and possible solutions and implementations. Besides, it elucidates the problems, specific potential solutions, and future directions for the agriculture sector using Machine Learning and the Internet of things

Steel Strip Quality Assurance With YOLOV7-CSF: A Coordinate Attention and SIoU Fusion Approach

Steel strip can develop surface defects during manufacturing and processing, affecting structural integrity and usability. These defects can be caused by both internal and external factors. However, traditional manual error detection techniques do not meet today’s accuracy standards. Therefore, an improved version of the YOLOv7 algorithm for steel strip surface defect detection is proposed in this work. A lightweight and inexpensive Coordinate Attention (CA) mechanism is built into the structure of the head of YOLOv7. The SCYLLA-Intersection over Union (SIoU) loss function is used to improve detection efficiency. Furthermore, to enhance the dataset, a vertical flip augmentation technique is applied to create the optimal model:YOLOv7-CSF through fusion of CA and SIoU. It has been observed in the experimental findings that the modified YOLOv7-CSF algorithm’s mAP value in the detection is 4.09% better than that of the original YOLOv7 method, reaching 66.1% and a maximum of 96.9% accuracy in a single category of defects. The efficacy and superiority of the updated model are shown by comparing it with the recently announced YOLOv8, other steel strip datasets and other hyper-parameter tuned models, providing a novel way for daily surface defect detection on steel strips

Industry 5.0: A survey on enabling technologies and potential applications

Industry 5.0 is regarded as the next industrial evolution, its objective is to leverage the creativity of human experts in collaboration with efficient, intelligent and accurate machines, in order to obtain resource-efficient and user-preferred manufacturing solutions compared to Industry 4.0. Numerous promising technologies and applications are expected to assist Industry 5.0 in order to increase production and deliver customized products in a spontaneous manner. To provide a very first discussion of Industry 5.0, in this paper, we aim to provide a survey-based tutorial on potential applications and supporting technologies of Industry 5.0. We first introduce several new concepts and definitions of Industry 5.0 from the perspective of different industry practitioners and researchers. We then elaborately discuss the potential applications of Industry 5.0, such as intelligent healthcare, cloud manufacturing, supply chain management and manufacturing production. Subsequently, we discuss about some supporting technologies for Industry 5.0, such as edge computing, digital twins, collaborative robots, Internet of every things, blockchain, and 6G and beyond networks. Finally, we highlight several research challenges and open issues that should be further developed to realize Industry 5.0.European Commission Horizon 2020University College Dubli