This paper affords a novel suppressed segmentation framework for Hyperspectral image processing before most cancer detection. This framework integrates the most recent advances in deep learning fashions and image segmentation for the most fulfilling selection-making approximately early cancer analysis. The proposed framework facilitates the fast and correct segmentation of the tumour tissues and other aberrations within hyperspectral images. The key modules of this framework encompass input pre-processing, noisy additive analysis, random area cropping, augmented context representation, hierarchical segmentation, and submit-processing. Experiments performed on real-world datasets show that the proposed framework yields segmentation accuracy similar to other main segmentation techniques while having advanced pace and robustness. The proposed model obtained 95.32% accuracy, 92.89% sensitivity, 91.50% specificity, 94.25% precision and 92.51% F1-score. This proposed method offers an optimised workflow for fast and correct segmentation of tumour tissues in most early cancer diagnoses.

In the manufacturing industry, it is a great challenge to achieve residual stress-free products from hard metals and alloys. Optimum selection of process parameters and cutting conditions in the turning of hard metals plays a significant role in minimization of residual stress and tool wear as well as maximizing the material removal rate (MRR). The primary aim of the present study is to optimize process parameters such as cutting speed (Vc), feed rate (f), and depth of cut (d) to minimize tool wear as well as residual stresses and maximize material removal rate in turning tempered EN-36C alloy steel with the use of a tungsten carbide cutting insert (CNMG-120408-TMH-F) in a dry environment. Tests were carried out using response surface methodology (RSM) to develop statistical models for tool wear, residual stress, and MRR. Analysis of variance (ANOVA) revealed that more contribution toward cutting speed and feed rate was observed than depth of cut for minimum residual stress and tool wear, while more contribution toward cutting speed and depth of cut was observed as compared to feed rate for maximization of MRR. The predicted model and confirmatory experiment results show good unanimity with the developed statistical model at 95

Accurate forecasting for the different time-series data plays a major role in predicting future analysis that can increase economic benefits. Many research models have been attempted earlier but pose certain challenges due to the random characteristics of the original time-series data in obtaining high-precision forecasting results. To this end, we propose a hybrid convolutional-based extreme learning model named Convolutional Multi-Layer Deep Extreme Learning Machine (CMDELM) adopted with the Extended Elephant Herd Optimization Algorithm (E2HOA) that performs deeper feature extraction with faster learning of optimized features. Also, an Edge-aware Attribute-based Dynamic Graph learning (EADGL) approach is proposed to ensure that the graph-structured network incorporates edge-level information for effective future learning. The CMDELM network employs stacked multiple convolutional ELM layers that use several hidden layers with dense connections to learn high-level features for enhancing forecast accuracy. Dense connections are utilized in the proposed CMDELM task of integrating ELM with CNN to aid the network in extracting feature maps from shallow layers. Moreover, the network's performance is enhanced by adjusting the convolutional kernel sizes and integrating them into the residual unit. Following the convolutional model of a fully connected network, the deep ELM layer is linked to produce the predicted classes for the forecasts. In the forecasting stage, we adopt the E2HOA strategy to optimize the computational parameters for all learning layers and predict an accurate future forecast signal. The E2HOA algorithm improves the separation and updation phases with the consideration of a newborn calf in the elephant herd. E2HOA incorporates new clans and updates them using a threshold value to determine their inclusion, thus achieving an optimal outcome. This makes the learning network achieve the final solution with the optimal key parameters of the CMDELM network. Experimental results covering different competitive models and the evaluated results indicate that our proposed hybrid forecasting system obtains satisfactory accuracy with a Root Mean Square percentage Error (RMSPE) and Symmetric Mean absolute percentage Error (SMAPE) of below 22%, respectively, for PM2.5 concentration, electricity price, and wind speed forecasting results. Hence, the experimental outcomes show that the proposed model performs well in terms of accuracy and provides constant forecasting of time-series.

The recent research is developing in a vast speed to develop the cloud orchestration system. In cloud system the remotely managed servers are storing, finding, removing, replacing and retrieving the various services in an adaptive optimized manner. The lot of services are provided by the vast number of providers in the market with the help of approximation theory by the rough set system (RST). RST finds in helping in getting the efficient cloud resources as a service to the users. The proposed OCRS (Optimized Cost Resource System) approach is being simulated and compared with the existing cloud simulator. The simulator gives the approximate results in many parameters of cloud services. In all aspects our algorithm is performing better.

Data mining (DM) and Soft Computing (SC) are a vital computational approach that offers good competence of flexible agricultural data processing systems to solve farmer’s problems. Recently, soft computing has emerged as a powerful technique for solving and analyzing complex real-world problems. This article suggests an approach of smart crop predictions is presented through DM &SC in the field of agricultural quality crop prediction. A five-level framework is proposed namely 1) Collection of data from different repositories, 2) Pre-processing of data, 3) Appropriate Classifier Selection, 4) Prediction and Estimation 5) Draw AUC & ROC curve. Method proposed here focuses on analyzing agricultural yield, soil for crop, rainfall required based on chemical property of soil. Agricultural data analysis and cataloging is one of the best applications of new computing tools such as soft Computing, Machine Learning (ML) approach, became a burning area for the reason that of the massive development of farming data. DM& SC approaches for accomplishing applied research will give effective answers for this type of problem. ML is a working tool to study multiple learners and combine their assessments for accomplishing greater forecasting accurateness. In this investigation, we had recapitulated ML methods which can be applied as an essential tool by the farmer and agriculture scientist for timely prediction of crop production.

Computed tomography (CT) is used in medical applications to produce digital medical imaging of the human body and is acquired by the reconstruction process, where X-rays are the key component of CT imaging. The present coronavirus outbreak has spawned new medical device and technology research fields. COVID-19 most severely affects people with poor immunity; children and pregnant women are more susceptible. A CT scan will be required to assess the infection’s severity. As a result, to reduce the radiation levels significantly there is a need to minimize the CT scan noise. The quality of CT images may degrade in the form of noisy images due to low radiation levels. Hence, this study proposes a novel denoising methodology for COVID-19 CT images with a low dose, where a convolution neural network (CNN) and batch normalization were utilized for denoising. From different output metrics such as peak signal-to-noise ratio (PSNR) and image quality index (IQI), the accuracy of the resulting CT images was checked and evaluated, where IQI obtained the best results in terms of 99% accuracy. The findings were also compared with the outcomes of related recent research in the domain. After a detailed review of the findings, it was noted that the proposed algorithm in the present study performed better in comparision to the existing literature.

Out of the most entrusted technological revolutions applied for the advancement of living standards and creation of a more convenient operation in the professional world, the Internet of Things (IoT) has been progressing at the highest projected pace. The interconnectivity of gadgets and devices over the internet to produce smarter control, communication, and a swift and easy lifestyle makes it an amazing tech introduction. However, the IOT also brings along a huge flow of privacy and security concerns whilst considering the huge inflow of data breaches and cyberattacks in the network data. The advancements in the security protocols of the data thus bring along mistrust among the users of IoT networks who might then no longer wish to be a part of this technological revolution. Therefore, it is important to maintain security assurance in the IoT based connection networks of wearable gadgets. This paper discusses the symmetric and asymmetric approaches of encryption over IoT which can help protect the network of smart phones connected to the wearable to assure a safe and smart data commute and avoid access to private data over the same internet connection.

Now a days social media is very popular medium for communication. People used various strategies to communicate with others. Like email, send message etc. email is very costly medium for communication with others. So now a days SMS is the best and effective medium for communication. Because it is very easy to use. But SMS Spanning problem is increase day by day. Because people send some illegal message which is very inconvenient to the users. In this paper we tried to build a model of detection of the spam message using classification algorithm along -with feature selection technique. In this paper we have used Naïve Bayes, Support vector machine, Random Forest Classifier, and K-Neighbours Classifier. With the help of these technique, we selected better features that provided better accuracy. We removed the inappropriate and redundant attributes that are not valuable for the accuracy of the model. A comparative study of different algorithm that has been discussed in literature review is also compared in terms of Precision, Recall, F1Score, and accuracy.

The intense demands of nickel chromium case hardened steel (EN-36C) alloy steel in different engineering applications are increasing day by day due to its cheap and easy availability. The effect of different stages of tempering on EN-36C alloy steel has been carried out to check the mechanical behavioural, microstructural properties and residual stresses in the present work. The effect of tempering on response variables viz., material removal rate, surface roughness, tool tip temperature and residual stresses are evaluated. The cosα method is used to measure the residual stresses in the specimen using two-dimensional detectors such as imaging plates. Further, the influence of tempering on mechanical properties such as tensile strength, hardness, toughness and percentage elongation has been observed. Experimental results depicted that the ductility and toughness are improved without significant change in hardness in the specimen tempered at 500°C. The mechanical properties of specimens tempered at 500°C are found to be a tensile strength of 680.42 MPa, hardness 92 Rockwell hardness of scale B and toughness 187 J. The maximum value of material removal rate (12596.80 mm 3 /min), minimum tool tip temperature (61.33°C), average surface roughness ( R a = 1.93, R q = 2.36, R z = 10.30) and lowest residual stress 217 MPa are observed in the specimens tempered at 500°C.

Artificial intelligence has rapidly grown and has made the scenario that no field can function without it. Like every field, it also plays a vital role in the sports field nowadays. In certain sports, injuries happen very often due to heavy training and sudden speedy actions, especially in athletics and football. Here arises a need to analyze the effect of physical training in sportsperson by collecting data from their daily training. With the help of artificial intelligence, a recurrent neural model is developed to analyze the effect of physical training and treatment concerning sports injury. A Recurrent Neural Network (RNN) can be a subsection of Artificial Neural Networks (ANN) that uses the neural nodes connected in a temporal sequence. The temporal sequence is one of the essential terms in this research, which denotes a data sequence of events in a given timeframe. The recurrent neural model is an intelligent machine learning method that comprises a neural schema replicating humans. This neural schema studies the data it collects from the athletes/players and processes it by analyzing previous injuries. Sports injuries have to be analyzed because, in some cases, it becomes more dangerous to the sportsperson that they may even lose their career due to disability. Sometimes it may cause a massive loss to the club or company that hired the sportsperson for the sport. The prediction process can give the player rest until he recovers, thus becoming the safest approach in sports. Therefore, it is essential to analyze the sportsperson's track data to keep an eye on his health. In this research, RNN model is compared with the existing Support Vector Machine (SVM) in concerning to the effect of physical training and treatment for sports. The results show that the proposed model has achieved 99% accuracy, which is higher than the existing algorithm.

The general data protection regulation (GDPR) is the tough privacy and security policy toward protection of data. The policy was drafted by the European Union but imposed obligations for any organization which collects data related to the people in the European Union. This policy came into effect from 2018. If any organization violets, the law will levy huge fines. Consumer driven companies in the areas like IT services and Fintech likely to affected by the GDPPR and have to comply. The research paper seeks to explore the implication of GDPR on these two industries. The challenges faced by the two industries in planning, implementation, and complying to GDPR, the overlaps and contradictions with the existing industry frameworks which will last post GDP6R implementation, the pre- and post-GDPR scenario analysis, and lastly the trial process for the data breach of the two industries. Based on the comprehensive study and research on the aforementioned areas, this research paper then delves into building a hypothesis through qualitative and quantitative data gathered which provides a solution for the two industries to prepare, plan, implement, and comply with GDPR across industry level with respect to user data management centers and Fintechs. We have used empirical methodology and collected responses through the questionnaire. Through the research study, we have found that how important is data encryption, not only because it is mandated in GDPR but also since any sort of data revelation to a criminal party can cause a lot of damage.

Breast cancer is one of the most chronic diseases found in women. There are two types of tumours found in the breasts: malignant and benign. A patient who has more percentage of malignant tumours is suffering from breast cancer. A model based on neuro-fuzzy is proposed to classify the tumour as malignant or benign. The designed system works on various attributes of tumour like tumour thickness, shape, size, etc. The classification process completes in three phases; phase 1 classifies the attributes as cat1 or cat2 on the basis of information gain. Then in phase 2, cat1 attributes are used to select the class of tumour by using the radial bias function neural network while the cat2 attributes use the fuzzy to select the class of tumour. The results of both techniques are collaborated by using the fuzzy inference system in the phase 3. The effectiveness of the technique is easily identified by the results. The results are compared for the accuracy of cancer detection of cat1 and cat2 with neuro-fuzzy system and decision tree.

The Cos α method has emerges as new technique for measurement of the residual stresses using whole Debye-Scherrer ring recorded on two dimensional imaging plates (IPs) with the help of X-ray residual stress measurement system μ-X360 Ver. 2. 3. 0. 1. Considering with more accountability of the tempering treatment process for decreasing or eliminating the residual stresses, the effect of tempering is monitored experimentally. The main objectives of this paper is to predict the residual stresses distribution, and surface roughness of the machined samples (with and without tempered) on CNC lathe at the same process parameters (speed, feed and depth of cut) using tungsten carbide tool. Surface residual stresses of five turned specimen with and without tempered have been measured. The residual stress graphs, FWHM (full width at half maximum) graphs and Debye –scherrer rings, and surface roughness of all samples have been obtained and critically studied. The residual stress and surface roughness were found minimum in sample tempered at 450°C. The min and max peak values were found as 149 k and 175 k for samples tempered at 450°C and untempered respectively. The average surface roughness (Ra = 2.06, Rq = 10.4 and Rz = 2.45) and(Ra = 3.65,Rq = 16.9 and Rz = 4.27) were seen in sample tempered at 450°C and untempered respectively. The results obtained from the present experiment, literature review and the theoretical study validate the present work and method in strong confirmation and bear a good agreement among them.

Quantum cryptography concentrates on the solution of cryptography that is imperishable due to the reason of fortification of secrecy which is applied to the public key distribution of quantum. It is a very prominent technology in which 2 beings can securely communicate along with the sights belongings to quantum physics. However, on basis of classical level cryptography, the used encodes were bits for data. As quantum utilizes the photons or particles polarize ones for encoding the quantized property. This is presented in qubits as a unit. Transmissions depend directly on the inalienable mechanic's law of quantum for security. This paper includes detailed insight into the three most used and appreciated quantum cryptography applications that are providing its domain-wide service in the field of mobile cloud computing. These services are (it) DARPA Network, (ii) IPSEC implementation, and (iii) the twisted light HD implementation along with quantum elements, key distribution, and protocols.

EN-36C alloy steel has a wide application in the field of automobile and aerospace sector due to its splendid metallurgical properties. There are several studies are conducted to investigate the mechanical and surface properties of EN-36C alloy. The present study focuses on methodology presented by various researchers to investigate the mechanical properties of EN-36C alloy steel. In this work, an attempt is made to explore the various processes that can be used to improve the machinability with preserving their mechanical properties.

Gene expression data mostly available as cancer data have major challenges such as analyze, pattern matching and classification. Sometime task become more complex with large number of genes and small samples are available with noise and redundant information. Meaningful correlated information from dataset is the first and most important steps to be extracted for better diagnosis through artificial intelligence (AI). Accordingly, recent work for AI based classification and prognosis are focused in two steps process that is: (a) Feature extraction, and, (b) Ensemble Classification. Feature extraction will help in eliminating redundant and irrelevant genes, whereas ensemble classifier will help to optimize the accuracy. In this paper, we use double RBF kernel function for feature selection and novel fusion-procedure for enhance the performance of three base classifiers i.e., K Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP) and Decision Tree (DT). Training of classifier is implemented based on k-fold cross validation techniques. The predicted accuracy of the proposed model has been compared with recent fusion methods such as Majority Voting, Distribution Summation and Dempster–Shafer on six benchmark cancer datasets. Experiment evaluation and result analysis gives promising and better performance than other fusion strategies, aiming at our goal-functions. Wisconsin Breast prognosis dataset is used with the proposed model for gene selection and prognosis prediction.

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These days, the Internet of Things (IoT) gets an incredible amount of thought from analysts as it transforms into significant innovation that guarantees a person's life, brings together things, machines and everything. By allowing the exchange of IoT, we talk about frameworks that incorporate objects into reality and sensors are connected or integrated with those components, which are associated to the Internet through wired and external system architecture. IoT sensors can use a variety of organizations, such as RFID, Wi-Fi, Bluetooth Bluetooth and ZigBee, despite allowing a wide range of networks that use multiple fields, GPRS, GSM, LTE and 3G. The IoT powerful object will share data on the location of the objects and the location of individuals, programming frameworks and different machines. In this paper, we take a look at some IoT applications

Automation of cooling system in machine tools is an effective method for achieving higher productivity and increased tool life. A cooling system is designed to control the operating temperature on the cutting tool tip by circulating coolant through a reservoir built on the top of the machine tool. This arrangement maintains the coolant flow rate as per variation of cutting tool tip temperature sensed by LM-35 temperature sensor which is located 1 cm away (calibrated distance) from the cutting tool tip and whose output voltage is linearly proportional to the temperature. Coolant flow rate is varied in such a manner that the temperature of the cutting tool tip remains within fixed value of temperature. The aim of present work is to develop a cooling control panel system to provide coolant on cutting tool tip in turning operation of mild steel. The coolant flow rate can be increased or decreased as per the variation of sensor temperature during turning of mild steel with high speed steel (HSS) cutting tool at different depth of cut, and spindle speed ,keeping feed rate constant which results in effective cooling of the cutting tool tip. The experiments were carried out with and without use of coolant. It supplies the coolant as per instructions of cooling control panel system which results in saving of coolant as well as power. The mechatronics application of designed cooling control panel system enabled the reduction in cutting tool tip temperature in more robust way as compare to conventional cooling system.

Now a day Breast cancer (BC) is very common and terrific disease in women, most detected and second leading cause of the ladies’ demise from the worldwide. Big number of people is passing their life or poor survival rate is because of this disease every year. Females are at high risk of BC, so it became quite essential and necessary for doctors to choose for an exact and suitable treatment for avoidance and remedy of cancer patients. So the basic motive is to find the cancer cells very correctly. Forecasting and categorization of BC using an effective and correct model of machine learning (ML) is essential for creation a new type of BC prognostic and diagnostic policies that really give a reduction push to the sufferer. Diversified technology, including Bayesian classifiers, Artificial Neural Networks and Decision Trees have been commonly applied in cancerous tumor. Undoubtedly methods used for Machine Learning may increase our understanding about breast cancer prediction and progression. It is important to consider these approaches in daily clinical practice. Neural networks are now a day’s very key and popular field in computational biology, chiefly in the area of radiology, oncology, cardiology and urology. In this study, we had summarized numerous ML techniques which could be used as an important tool by surgeons for timely detection, and prediction of cancerous cells has been studied and introduced.