International Research Journal on Advanced Science Hub

Anomaly detection in Internet of Things is a challenging issue and is being addressed in a wide range of domains, including fraudulent detection, mal- ware protection, information security and diagnosis of diseases. Due to the distributed nature of wireless transmission and the insufficient resources of end nodes, traditional anomaly detection techniques cannot be used in IoT directly. To extract uncommon behaviors or patterns from complex data, nevertheless, is a difficult task. As a result, this paper offers a thorough analysis of ML based methods to identify anomaly in the IoT healthcare data. Further, a detailed comparison of their performance is provided with reference to their benefits and disadvantages.

Heart disease is one of the leading killers that are widely recognized through- out the globe. Large volumes of clinical data are stored in a variety of sys- tems and biological equipment at hospitals. It is essential to grasp the facts of heart disease in order to improve forecast accuracy. In this paper, experimental evaluations have been conducted to assess the effectiveness of models created utilizing classification algorithms and relevant attributes selected using Extra Tree feature selection procedures. Several people suffer originated at heart disease globally. It is necessary to use data mining and machine learning techniques to extract new insights originated at this data. Analyzing medical data sets and diagnostic issues, including heart disease, involved the use of a number of categorization approaches. However, these methods were only per- formed on small, balanced data; then, the features must be derived originated at trial and error. Additionally, several sectors have made substantial use of feature selection techniques to enhance classification performance. This paper aims to propose a comprehensive approach to enhance the prediction of heart disease using several machine learning methods such as Bagging, Support Vec- tor Machine, Multilayer Perception and Gradient Boost with feature selection methods such as extra tree. The experimental results showed improvements of prediction. Bagging received scores in training model on 80% data sample as 99.08, 73.19, 67.20, 69.20 and 80.66 of accuracy, precision, recall, F1-score and roc respectively. In the experiment,  we have tested on 20% data sam- ple for each classifier algorithms and find Bagging classifier model perform higher score for accuracy, precision, recall, F1-score and roc 92.62, 48.44, 39.63, 41.89, 66.82 respectively.

High tensions within states or across borders are quite common nowadays. Protection of human resources i.e., soldiers is as important as the protection of national resources. It is a visible fact that the causality rate among the defense forces during a crisis is relatively higher. That is the reason why a majority of Governmental organizations of various nations are stressing the concepts of Automation. ROV or Remotely Operated Vehicle, a concept of automation, is playing a very vital role in almost all the advancements in this technologically developed society. Present work, one complete Armored Safety Vehicle (ASV) was designed with more featured to accommodate sensors for detection and coding are developed o detect the weapon using the image processing techniques. There is a special wheel design is introduced which is safe for easy and quick movement of a vehicle. From commercial vehicles to exploring the deepest trenches, ROV’s have been a substantial tool for developmental prospects. It was tested for one image file and worked well. It is integrated with Machine Learning and image processing concepts to automatically come up with solutions based on the inputs. These concepts when used in defense vehicles not only increase efficiency but also reduces the casualty rate during a crisis.

Cloud-based systems implies to applications, resources or services provided to users as per their requirement through the Internet using a cloud computing provider’s server. These clouds triggers alarm events to indicate the health of system. Monitoring these alarms is essential for maintaining the health and continuous functioning of cloud. Because of humungous alarms triggered on daily basis, notifying critical alarms in time and taking required action is quite challenging task. In this paper machine learning model is implemented using decision tree classifier to analyse each alarm and predict if any action required for that alarm or not and also notify the concerned team via creating JIRA tickets.

Basically, Big Data means large volumes of data that can be used to solve problems. It has piqued people’s attention over the past two decades because of the enormous potential it holds. Big data is generated, stored, and analyzed by a variety of  public  and  private  sector  industries  in  order to enhance the services they provide. Hospital reports, patient medical records, medical test outcomes, and internet of things applications are all examples of big data outlets in the healthcare industry. Biomedical research often produces a large amount of big data that is pertinent to public health. To extract useful information from this data, it must be properly managed and analyzed. Otherwise, finding solutions by analyzing big data quickly becomes impossible. The ability to identify trends and transform large amounts of data into actionable information for precision , medicine and decision makers is at the heart of Big Data’s potential in healthcare. In a variety of areas, the use of Big Data in healthcare is now offering solutions for optimizing patient care and creating value in healthcare organizations. In this paper, some big data solutions are provided for healthcare. Big Data Analytics strategies to mitigate covid-19 health disparities are provided. Finally we analyse some of the challenges with big data in healthcare.

Air Purification is considered to be the vital function to implement in our society for effective and healthy environment. Today, most of the countries are suffering from air pollution which may be caused by Industrial Exhaust, Agricultural activities, Mining operations, Transportations, etc. In these causes, the majority of the pollution occurred in Urban and rural areas are mainly because of Vehicles. To fulfil our daily basic needs, we are all dependent on the transportations. So the pollution caused by the vehicles is inevitable. It is the hardest Challenges to the government to overcome these situations. There are so many technologies for monitoring the air pollution level caused by vehicles and also to control pollution. Still, there is a problem for society which in evoking everyday as because of air pollution. Government is in search for better system for handling and controlling this problem. Here The Proposed System is to target the minimizing level of air pollution, which is caused by the vehicles. 

Agriculture is a process of growing crops, soil cultivating, it provides food and fabric and helps for growing country's economy. In India more than 50% of people directly and indirectly depend on the agriculture. For developing agriculture, main interceptions are weather hazards and the crop diseases. Weather hazards cannot be prevented, but the loss that occurs due to crop diseases can be reduced. This can be achieved by identifying the crop disease as early as possible and it is also important to identify the type of crop diseases for preventing the spreading of the disease. In India, we have 160 million hectares of arable land and it is second largest country after the United States. Identifying the crop diseases by human action manually is practically difficult and it is hard to identify the type of crop diseases. So, many researchers involved in to identify the crop diseases based on the image processing for helping the real-time gadgets which can be used to identify the crop disease and its types. This survey focuses on the investigation on the different surveys carried out and work related to the crop disease identification and detection based on the image processing. Computer vision and image processing-based work will help to detect of crop diseases along with many practical based applications like drones, IoT based devices etc. In recent studies most of the works are depending on the machine learning and deep learning-based image processing on various studies of predictions. After analyzing the related work on crop detection based on image processing, most of the works achieved better results based on deep learning algorithms compared to the machine learning algorithms.

This paper tries to attempt a review on deep neural network (DNN) method in music source separation (MSS) tools with emphasis to Spleeter by Deezer, an enhanced deep learning model for music sourceseparation. It is a set of pre-trainedmodel written in python using the Tensorflow machine learning library used for musicsource separation. It was developed by Deezer, on the need to separate a given mixed music track to its constituentinstrumental or vocal tracks usually known as stems. Spleeter offers 3 pre-trainedmodels namely 2, 4, and 5 stemseparation models that are capable of separating a given mix into 2, 4, and 5 stems respectively, which can be used forvarious needs like remixing, up-mixing,music transcription, etc. This paper is the first of its kind to review on DNN methods in MSS.In this paper, we will learn about the purpose and useof Spleeter developed by Deezer as well as about the technical aspect behind this software product that includes areas like ArtificialIntelligence (AI), Machine Learning and Deep Learning, and further about Time-Frequency (TF) masking and U-NetConvolution Neural Network (CNN) which are the methodology and architecture employed in it respectively. From thereview, we learned that Spleeter by Deezer is one of the latest advancement in MSS problem that comparatively has one of the best signal to distortion ratio (SDR), signal to artifacts ratio (SAR), signal to interference ratio (SIR), and sourceimage to spatial distortion ratio (ISR) and produce a state of the art solution, and it has also paved a way togreater development in MSS problem in the future.

Identifying the malicious traffic flows in Internet of things (IOT) is very important to monitor and avoid unwanted errors or the unwanted flows in the network.   So, for a security to this network various machine learning algorithms (ML) has been introduced by various analyst to avoid this flow of error in the network. But, owing to the unsuitable selection of features, the ML models which introduced previously suffer from misclassify errors. So, there arises a need to study the problem of feature selection more depth to predict the accurate traffic flow observation in the network. To overcome this problem, a new structure in machine learning (ML) is introduced. So, for thisa novel features selection metric CorrAUC is suggested. So, based on  this metric approach, a new feature selection algorithm CorrAUC is develop and design, it is based on wrapper technique to get features accurately by filtering to predict flow of traffic is suggested. Then, we applied multicriteria decision method called VIKOR which is used for validating the features selected for recognition the flow of traffic errors in the network. We estimate our approach by using the NSL-KDD dataset and three different ML algorithms.

Pandemic COVID-19 novelly induced corona viruses that are continually spreading all over the world. In most areas of COVID-19 affect creation fallen. There is a crisis in the health sector. There have been several precautionary steps brought to minimize the propagation of this disease, with a mask. We are proposing a system that reduces COVID-19 development, identifying individuals who don't use a facial mask in a smart city system that monitors all public places with CCTV cameras. The appropriate authority is alerted by the city network whilst an individual is identified without a mask. Architecture for a data set that includes images of individuals with and without masks from various sources can train machine learning. A qualified architecture has achieved precision in identifying individuals from previously unknown testing data with and without a face mask

The World Wide Web has taken seriously new ways for individuals to convey their views and conclusions on different topics, models and issues. Clients create content that resides in a variety of media, such as web gathering, conversation gathering, and weblogs, and provide a solid and generous foundation for gaining momentum in different areas such as advertising and research. Policy, logic research, market forecasts and business outlook. Hypothesis research extracts inferences from information available online and orders the emotions that the author conveys for a particular item into up to three predefined categories (good, negative, and unbiased). Identify the problem. This article outlines a hypothesis review cycle for quickly ordering unstructured news on Twitter. In addition, we are exploring different ways to perform a detailed emotional survey on Twitter News. In addition, it presents a parametric correlation of strategies considered according to recognized boundaries. This work tends to make the case enjoy investigating on Twitter; The values communicated in them represent the tweets: positive, negative or fair. Twitter is an online thumbnail that contributes to a blog and a wide range of interactions, allowing customers to create short 140-character short instructions. It is a fast growing association with more than 200 million subscribers, of which 100 million are dynamic customers and half of them constantly sign up for Twitter, generating around 250 million tweets every day. Due to this overwhelming use, we plan to achieve a biased impression of the public by breaking the estimates communicated in the tweets. Researching public opinion is important for some applications, for example, when companies are looking to respond to their material, predict political careers, and anticipate economic wonders like stock trading. The function of this to build a useful classifier for the command in a precise and programmed way of the stream of fuzzy tweets.

The rise in the volume of unwanted spam emails has made the development of a lot more necessary more reliable and robust filters for antispam. Current machine learning approaches are used to excel Spam emails can be detected and filtered. Filtering solutions to text spam. The analysis discusses core principles, actions, efficacy, and Spam filtering trend for research. The first topic in the research study aims at the requests Machine learning approaches for the operation of filters of spam by the leading providers of internet infrastructure (ISPs) The increasing quantity of unnecessary bulk email (also called spam) has generated a secure need Filters for anti-spam. Then the review compares the strengths and disadvantages of existing methods of machine learning and open research Spam handling problems. As future strategies suggested extreme leaning and strongly opposed schooling that can handle the danger of spam emails effectively.

The evolution of blockchain methodology has been a remarkable, highly transformative and trend-setting platform in current years. BT's accessible platform reinforces data protection and confidentiality. In addition, the consensus framework in it ensures system is protected and accurate. Nevertheless, it introduces additional security challenges such as invasion by the majority and double consumption. Data analysis on encrypted data centered on blockchain is crucial to manage the existing challenges. Insights on these results elevates the value of emerging of Machine Learning technique. It covers the fair quantity of data needed to make specific choices. Consistency of data and its distribution are very critical in ML to increase findings reliability. The fusion of these two techniques will produce extremely accurate outcomes. In this article, we describe a thorough analysis on ML implementation to make smart applications based on BT further robust to threats. There are numerous standard ML approaches such as Support Vector Machines (SVM), Clustering, Bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long-Term Memory (LSTM) that can be employed to evaluate the threats on a block chain network. Finally, we discuss how two different techniques can be implemented in a number of smart applications like Unmanned Aerial Vehicle (UAV), Smart Grid (SG), medical care and Smart cities.

Predicting the performance of students is one of the most important topics required for learning contexts such as colleges and universities, as it helps to design successful mechanisms that boost tutorial outcomes and prevent dropouts among various items. These are benefited by automating the many processes involved in the activities of usual students which handle huge volumes of information collected from package tools for technology-enhanced learning. Thus, the careful analysis and interpretation of these information would provide us with valuable data regarding the data of the students and therefore the relationship between them and hence the tutorial tasks. This data is the supply which feeds promising algorithms and methods able to estimate the success of the students. During this analysis, virtually many papers were analysed to show radically different trendy techniques widely applied to predict the success of students, along with the goals they need to achieve in this area. These computing-related techniques and approaches are mainly machine learning techniques, deep learning techniques, Artificial Neural Networks & Neural Networks Convolution, etc. This paper demonstrates the analysis and their comparisons of various methods used to forecast Student Academic success.

Artificial Intelligence is a developing zone in the field of innovation and furthermore attempts to show that the eventual fate of AI gains ground so that machines would function according to a human and would likewise convey the action of the person. It is difficult to create a machine like individuals who can appear feelings or think like individuals in different conditions. Directly we have recognized that AI is the examination of how to form things which can accurately fill in as people do. A working framework that utilizes AI reasoning procedures has a computerized reasoning motor, and experience scientific and Statistical module, an adjustment module and a UI. The computerized reasoning motor processes an accomplished expository boundary from a front code and a back code. The experience of scientific and Statistical module records and changes the experience's systematic boundary. The alteration module changes the front code and the back code as per the consequence of the experience logical and Statistical module computation of the experience systematic boundary. The UI inputs information or showcases the consequence of the computation. In the man-made consciousness motor, the experience diagnostic boundary is then again added to either the front code or the back code to register another experience investigative boundary. Such a game plan, the working framework can consequently change the consequence of the computation as per the decision or past decisions of the client.

Nowadays, a huge amount of data is available everywhere. Therefore, we need to prioritize analysing this dataset which would help us in gaining some meaningful information for the development of an algorithm based on the analysis. These feet can be obtained by using Machine Learning, Data Mining, and Data Analysis. Machine Learning which is a part of Artificial Intelligence is used for designing algorithms based on trends of data, patterns and the relation found between them. ML has been used in various fields such as Marketing, gameplay, intrusion detection, bioinformatics, information retrieval, healthcare, entertainment and also on COVID -19 applications and so on. This paper presents an overview of the contribution of ML in Entertainment industry

Interdisciplinary research is a manner of research carried out by an individual or group of persons. The knowledge, data, techniques, concepts are incorporated from two or more disciplines. In this paper we tried to throw light on this concept. Machine learning is a branch of computer science which uses the information, tools for collection of data, methods for analysis from the subjects like Electronics, Mathematics and Statistics. Why we use machine learning? Because it plays an influential role in prediction of data. Machine learning is used to find hidden patterns and essential ideas from data as well as it solve complex problems. In today’s world, many applications have large volume of data like structured, unstructured and semi structured. This unexploited resource of knowledge can be used to improve business decisions. As data diversifies many are adapting to machine learning tool for analysis of data, so that, they can exploit intelligence and benefit from that data at most. Machine learning adopts different algorithms and each algorithm performs different functionality. In this paper, we tried to explain through example, how Electronics is used for collection of data while Mathematics and Statistics are used for analysis and finally using Machine learning results can be predicted.

Health is a crucial resource for a person's being to measure in our society from any disease. The fast development of the population appears to be trying to record and dissect the massive measure of knowledge about patients. Healthcare may be a need, and clinical specialists are constantly attempting to get approaches to actualize innovations and give effective outcomes. The main problem faced by the healthcare industry is the rising costs which include diagnosis and prediction of diseases, drug discovery, medical imaging diagnosis, personalized medicine, behavior therapy, and smart health records. Machine learning gives us an advantage of processing these information naturally which helps in making the human services framework progressively powerful. Getting the correct determination may be a key part of Healthcare. It clarifies a patient's medical issue and suggests health care treatment. The disease diagnostic technique is a complex, community-oriented action that has clinical, intelligent and data social events to make a decision about a patient's medical issue. Google has built up a ML model to assist recognize dangerous tumours on mammograms. Stanford’s profound learning calculation to differentiate skin malignancy. This paper is focused on the importance of Machine Learning in Healthcare just like the different application areas, latest research works in healthcare, wise machine learning contribution in Healthcare, and so on. Machine Learning is an application of Artificial Intelligence that helps in automatically learning and improving itself from experience. It is used in many other sectors like Law, Marketing & Advertising, Finance, Retail& Customer Services and Healthcare which also includes Covid-19. This paper presents various research in the Medicine and Healthcare sector

The Neural network system is an educational paradigm that unites several neural networks to solve a problem. This paper explores the relationship between the ensemble and its networks of neural components, both from the viewpoint of regression and classification, which reveals that certain networks are stronger than other neural networks. This result is surprising because the rest of the neural networks enter the ensemble at present. To prove that a GASEN algorithm efficiently selects the appropriate neural networks to construct an ensemble from different neural networks available. At first several neuronal networks were taught by GASEN. Then the network allocates random weights and uses genetic algorithms to establish these weights to classify the fitness of the neural system in one ensemble to a certain degree. Ultimately, it used the weights designed for the ensemble for certain neural networks. A comprehensive analytical analysis reveals that, in comparison to typical assemblies, such as luggage, GASEN can generate network assemblies with much smaller sizes but with a higher generalization efficiency. This study, in addition, gives the mistake a gradual regression, demonstrating that the performance of GASEN could be that it can greatly reduce its bias and uncertainty such that GASEN is well aware of its operating mechanism.