Aim of this paper is to deduce computational cost using a token generation method compared to homomorphic algorithms in cloud storage. Token generation and homomorphic algorithms are compared and the study contains two groups with sample size N=10. Accuracy of each of the methods is compared for different sample sizes with G power value as 0.8. Homomorphic algorithm provides 1.2 times better performance compared to token generation algorithm for various datasets. Results were obtained with statistical significance of two algorithms: sig (2-tailed) with p-value observed is 0.002 (p<0.05) for both accuracy and loss. Homomorphic algorithms give significantly better performance in comparison with Token Generation algorithm.
The main objective is to reduce the noise in Magnetic Resonance Imaging of brain images using novel Contour coding technique and biologically inspired Support Vector Machine algorithm. Two calculations were applied, one is Novel Contour coding technique and another is biologically inspired Support Vector Machine. Sample size N=5 is taken for both algorithms. The computation processes were executed and verified for exactness. Novel Contour coding technique was applied and it has attained the improved mean accuracy of 93.2%, and it performs better than support vector machine having 87.4%. The performance comparison indicates that there is a statistically significant difference between Support Vector Machine clustering and Contour coding technique. The results were obtained with a level of significance value of 0.003 (p<0.05), with a pretest G-power value of 80% using SPSS tools.
The concept of Convolution Neural Networks (CNN) has been becoming highly significant in computer-vision-based applications. Their applications in disaster management, like fire detection, will definitely improve the social and ecological environment. Most of the existing fire detection systems fail to detect fire in certain environments like smoke, fog and so on. In this paper, we propose a Squeeze-Net framework-based CNN for detection of fire, localization and understanding the scene of fire. The method uses smaller convolution layers with no dense layers, thus minimizing the computational power. Experimental results suggest improved performance in terms of accuracy and loss parameters for both known and unpredictable image settings. Despite the low computational power, the method provides more accuracy than state-of-art techniques.
The software size estimation is the basic and essential process in the Software Development Life Cycle (SDLC) for budgeting, developing and delivering in the scheduled manner. The modern computer software like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT) and Data Analytics are highly dynamic, distributed and parallel processing. To identify the size of this versatile environment is highly difficult and risk based. A small fault may cause negative consequences and also suspension of the project. This article gives a study on the functional, social, economic, political, organizational and nonfunctional risks associated to the parallel computing applications.
EARLYBUDDY is a smart alarm clock app for Android devices. It primarily uses traffic data to help the user wake up on time when there is an estimated delay. Users can choose a timeframe within which they must awaken rather than a single wake-up time. The app will then select a time inside that window based on the projected delay based on frequent traffic data updates. If it detects that the user will be late to their destination, it will change the existing alarm to wake them up. Users can also specify their morning habits, which are factored into the total delay estimate. These routines are optional, but they are encouraged because they help the user to organize their morning and provide more data for the application to use when calculating delays. All humans are not made equal when it comes to sleep. EARLYBUDDY features a provision that prevents users from turning off the phone to silence the alarm if they find it difficult to get up from bed in the morning. Users must tap the stop alarm or snooze button, which only appears in the app itself at the center of the screen, to turn off the alarm. If you press anyplace else on the screen or push the snooze button incorrectly, the alarm will ring again. The basic goal here is to actively wake up the body and mind before hitting the snooze button. Users can also place their phones on the mattress nextto them, and EARLYBUDDY will analyze their sleeping habits using the device’s sensors. This may be used to assess the quality of the user’s night’s sleep and set the alarm at the most natural time.
To predict the air pollution level in a particular region area using a K-Nearest Neighbor algorithm compared with the Support Vector Machine algorithm, The Novel K-Nearest Neighbor Algorithm and the Support Vector Machine Algorithm are two groupings. The algorithms were implemented and evaluated on a dataset of 32516 records. Various air pollution was identified through a programming experiment with N = 5 iterations for each method. G power is set at 80%. The confidence interval is 95%, and the threshold value is 0.05%. The G-power test is around 80% accurate. The K-Nearest Neighbor algorithm (97.44%) has better accuracy when compared with the Support Vector Machine (70.32%). The K-Nearest Neighbor algorithm has the highest accuracy compared to the Support Vector Machine algorithm. In the prediction of air pollution, K-Nearest Neighbor has better performance when compared to the Support Vector Machine Algorithm.
The study’s primary purpose is to propose an automatic melanoma cancer detection system using the Decision Tree algorithm and convolutional neural network algorithm to detect melanoma cancer and compare their accuracy. Group 1 was the Decision Tree algorithm with a sample size of 10, and Group 2 was a convolutional neural network algorithm with a sample size of 10. They were iterated 20 times to predict the accuracy percentage of identifying melanoma cancer. Compared to convolutional neural network accuracy(75.58 %), the Decision Tree method has substantially higher accuracy (85.61%). The Decision Tree p=0.018 (p<0.05) Independent Sample T-test has a high statistical significance. Within the scope of this study, the Decision Tree method outperforms convolutional neural networks in melanoma skin cancer detection.
The current approaches for diagnosing mental disorders rely heavily on self-reported and clinical interview ratings. The development of an automatic recognition system assists in the early detection and discovery of biological markers for diagnostic purposes. In this paper, develops a multimodal machine learning model, where it processes multiple modalities like visual, acoustic and textual features using a cross-modality correlation. The study uses a Denoising Autoencoder that finds the multimodal representations and then it adopts Fisher Vector encoding to form session-level descriptors. Paragraph Vector (PV) are used for textual modality that embeds the interview sessions transcripts into document representations that tends to capture the mental disorder cues. Finally, the textual and audio-visual features are fused before training the 3-layered denoising autoencoder (DAE) with Residual Neural Network classifier. The proposed model is validated using two bipolar disorders that includes depression and bipolar disorder. The study uses two different datasets including Extended Distress Analysis Interview Corpus (E-DAIC) and Bipolar Disorder Corpus (BDC) to analyse the depression and bipolar disorders. The experimental evaluation is conducted to show the performance improvement using the proposed multimodal model than other state-of-the art methods in detecting the depression and bipolar disorders. The results of simulation show that the proposed method obtains an improved detection rate than existing models.
The new forms of networks labeled IoT are relatively new and which become buzz in this decade. The network architecture lets any smart device loosely connect to the Internet under internet protocol. However, the other dimension of this network facilitates intruders to access the network with no critical efforts. The context of intrusions has been delineated as intrusion practices of other devices connected to an IoT network that are connected to external networks through a gateway. Vice versa, the compromised IoT network intends to communicate with external devices or networks to perform intrusion practices. In this regard, intrusion detection through machine learning demands significant feature selection and optimization techniques. This manuscript endeavored to demonstrate the scope distribution diversity assessment methods of traditional statistical practices toward feature selection and optimization in this regard, the contribution “Distribution Diversity Method of Feature Optimization (DDMFO) to Protect Intrusion Practices on IoT Networks” of this paper uses the Dice Similarity Coefficient procedure to pick the optimum characteristics for the training of the classifier. The classifier that has been adopted in this contribution is Naïve Bayes, trained by the features selected by the proposal. The experimental research concludes the significance of the taxonomy, which demonstrates substantial accuracy and minimal false alarm.
Retinoblastoma is an embryonic intraocular tumor arising in the retina of the eye. It is a dangerous tumor that can damage the eye and its surrounding components. Chromosome 13q14.1-14.2 is the cytogenetic location of the RB1 gene. As a result, early identification of Retinoblastoma in children is essential. Over the last few decades, Retinoblastoma treatment has improved with the goal of not only saving life and the eye but also optimizing residual vision. In oncology, machine learning approaches used to predict cancer patient treatment outcomes include data collection and preprocessing, text mining of clinical literature, and constructing prediction models. This paper discusses recent advances in the management of Retinoblastoma, as well as data preparation and model construction for identifying patterns between Retinoblastoma clinical factors and predicting therapy success using machine learning.
The primary goal of this research work is to predict housing prices that are frequently overstated, using efficient machine learning algorithms to obtain better accuracy. This study compares the price prediction accuracy of Novel Voting Regression (Group 2) and Linear Regression (Group 1) algorithms. For each of the groups studied, the sample size was N=10. Clincle was used to figure out the sample size. The pretest analysis was maintained at 80%. Using G-power, the sample size is calculated. Statistical analysis yielded a value of 0.584 for significance. The accuracy of the Novel Voting Regression method for house price prediction is 80.92%, which is greater than the Linear Regression algorithm’s 69.81%. The Independent Sample T-test has a statistical significance of 0.584. So it can be concluded that the Novel Voting Regression technique can give near accurate values than the Linear Regression technique.
The main of this paper is to analyse the performance of customer behaviour in online shopping by using precision value.A novel Self-organizing map with Random Forest was iterated at different times for predicting accuracy percentage of customer behaviour. The result proved that the Novel Self-organizing map got significant results with 96% accuracy compared to Random Forest with 93% accuracy. Self-organizing mapis a simple and most effective algorithm to build fast machine learning models. The self-organizing map helps in predicting with more accuracy the percentage of customer behaviour.
Recent days, Road accidents are the major cause of deaths. Numerous lives are either lost or at risk due to car accidents. It is a very important and crucial area which needs lot of attention, huge exploration and high priority to detect the accidents, identify the cause, address the issue on time and provide feasible solution during road accidents due to vehicle crash. Time delay and response time to address the accidents are the major challenges to rescue and treat people during accidents and emergencies. In order to rescue and save lives due to accidents in remote places, an efficient automated system is needed for accident detection, cause identification and on time assisting the patients after the occurrence of accident. This automated system has to communicate with the concerned fatalities about the current status of crash and respond immediately within less time. Many researchers have proposed different accident detection and alert systems in their research and survey which involves the Bluetooth, Global Positioning System (GPS), and Global System for Mobile Communications (GSM), various algorithms involving machine learning and mobile applications. Also Sensors for accident detection are based on the acceleration parameters, Smart phone for accident detection etc. This research work provides a critical and in-depth review of various emerging methods and techniques for addressing the road accidents which must be resolved in order to save lives.
A Spoof news is a fraud content meant to misguide the reader about the event with ill motive. In this article a reactive technique using deep learning is proposed to deal with it effectively. Spoof news are innumerable in number over microblog twitter and have wide range of bad effects overall. This is causing chaos and hoax among the readers about the issue. They are getting mislead about the issue a lot. As of now automatic locators of fake news are ineffective and few in number. This emphasized us to come up with smart locator with deep learning mechanism. One way of dealing with this issue is to make “blacklist” of origins and composers of counterfeit news. Here we need to examine all irksome instances of origins and creators in gradual manner. To cater this need we came up with a classifier based on deep learning mechanism that studies linguistic, network account aspects of twitter news and then distinguishes them into spoof and legitimate ones. We set up a deep learning model that takes both legitimate and spoof news elements as input and learns by analyzing their constructs. Then do the binary classification of news effectively thus avoiding the user not to misled by fake.
Electronic health records (EHRs) are both important and sensitive since they store crucial data that is routinely shared across various parties, such as clinics, pharmacies, and medical practices. These health files require increased safety and confidentiality to prevent leakage or misuse by a third party. There have been instances where a security breach in a patient’s electronic medical records has occurred. Blockchain technology may be useful in providing privacy for these documents. The fundamental goal of the work mentioned in this article is to improve the security, privacy, management, and efficient sharing of medical records. In this study, we will present a complete assessment of several strategies for safeguarding EMR privacy using blockchain technology.
. Roller Bearing (RB) is one of the critical mechanical components in rotating machineries. Failure of a bearing may cause the fatal breakdown of an entire machine and inestimable financial losses due to its continuous rotation. Hence, it is significant to diagnose the fault accurately at an early stage so that it helps in predictive maintenance of the machine from malfunctioning. In the recent developments, Machine Learning (ML) has shown a drastic change in the way we predict, analyze and interpret the results. In this paper, a diagnostic technique is being proposed to identify the bearing faults that employs ensemble learning algorithms such as Bagging, Extra Tree and Gradient Boosting classifiers. The proposed method includes 1) Pre-processing of vibration data 2) Extracting statistical features such as Mean, Standard Deviation, Kurtosis, Crest Factor and Mel-Frequency Cepstral Co-efficient (MFCC) features and 3) Training the Ensemble Learning algorithms for classifying the various faults based on extracted features. For experimentation, vibration data is collected from the Case Western Reserve University (CWRU) Laboratory to diagnose 12 different fault types associated with Inner Race (IR), Outer Race (OR), Ball fault and normal bearing of varying diameters. Results shows that Ensemble learning algorithms performs better based on MFCC features as compared to statistical features.
The aim is to create an artificial conversation entity(chatbot) using python to predict disease and medicine for healthcare treatments. Two algorithms fuzzy support vector machine algorithms are compared with Decision tree algorithm sample size taken 28. G power of 81% and sample size is calculated using the G power tool. Performances of the score model validated test set accuracy with 95% confidence interval for fuzzy support vector machine algorithm with different sub-samples has 91.60% accuracy comparing with Decision tree which has 87.90% accuracy.Independent Sample T-test a significance difference in accuracy and loss is observed p<0.005.From the results it is concluded that proposed algorithm Fuzzy support vector machine will produce better results than the existing algorithm.
The aim is to improve and develop a novel scheme to detect loyalties of customers using pattern growth method.Novel Pattern growth method compared with upper bound taxonomy sequence algorithm are used to detect online sales customer loyalties. Sample size is determined using the G Power calculator and found to be 10 per group. Totally 20 samples are used. Pretest power is 80% with CI of 95%. Based on the analysis Novel Pattern growth method has an accuracy of 80.8% and upper bound taxonomy sequence algorithm has 67.25%. Significance value is 0.0001 (p<0.05, two-tailed). Proposed algorithm Novel Pattern-Growth method has higher accuracy than Upper Bound Taxonomy for selected datasets for more reviews.
The primary goal of this study is to use efficient machine learning algorithms to anticipate better house prices, typically inflated. Materials and Methods: : This study will study the differences between near-accurate price prediction utilizing Novel Voting Regression (Group 2) and Decision Tree methods (Group 1). The sample size used to carry out this research was N=10 for each group studied. Clincle was used to calculate the sample size. The pre-test analysis was maintained at 80%. G-power is used to calculate the sample size. Statistical analysis yielded a significance value of 0.001. Results: : The accuracy of the Novel Voting Regression Algorithm for house price prediction is 82.94%, which is greater than the Decision Tree Algorithm’s 72.54%. The Independent Sample T-test has a statistical significance of 0.584. Conclusion: : As a result, it can be stated that the Novel Voting Regression technique can produce results that are almost as accurate as of the Decision Tree technique.
Globally, numerous preventive measures were taken to treat the COVID-19 epidemic. Face masks and social distancing were two of the most crucial practices for limiting the spread of novel viruses. With YOLOv5 and a pre-trained framework, we present a novel method of complex mask detection. The primary objective is to detect complex different face masks at higher rates and obtain accuracy of about 94% to 99% on real-time video feeds. The proposed methodology also aims to implement a structure to detect social distance based on a YOLOv5 architecture for controlling, monitoring, accomplishing, and reducing the interaction of physical communication among people in the day-to-day environment. In order for the framework to be trained for the different crowd datasets from the top, it was trained for the human contrasts. Based on the pixel information and the violation threshold, the Euclidean distance between peoples is determined as soon as the people in the video are spotted. In the results, this social distance architecture is described as providing effective monitoring and alerting.
