People above the age of 65 are disproportionately affected by "Alzheimer's disease (AD)". AD is o... more People above the age of 65 are disproportionately affected by "Alzheimer's disease (AD)". AD is one of the most prevalent forms of dementia that causes a gradual decline in their cognition and mental abilities. Immediate medication is essential to halt the course of dementia, but this relies on an accurate AD diagnosis and its prior stage, "Mild Cognitive Impairment (MCI)". Through this effect, an accurate assessment from neuroimaging is needed. An effective diagnostics approach supported by the processing of "Magnetic Resonance Imaging (MRI)" results might provide a better comprehensive and trustworthy solution, and may even improve diagnostic accuracy. In the past, researchers examining MRI scans for AD problems have relied on massive multivariate analysis, which assumes that individual brain areas function autonomously. However, this view is inappropriate for comprehending how far the brain functions. In this research, an "Advanced Adaptive Neuro-Fuzzy Inference System (AANFIS)" classification is proposed for automatic multi-level AD categorization through MRI brain images. The "Neural Network (NN)" is used to enhance the roles of fuzzy membership as decision-making structures for the given MRI brain image. The "Fuzzy Logic (FL)" seems to have the capacity to explicitly transmit expertise results. If this is the case, it tries to enforce laws. However, it takes too long to establish and administer the Member States' position, which perceives certain linguistic elements quantitatively. Whereas automated learning is possible in NN processes. It lowers the expense and time of instruction. Also, it increases the performance of the classifier. So the combination of Advanced NN with FL offers stronger outcomes for AD classification. Classifying subjects of sMRI brain scans into "Cognitive Normal (CN)", "Mild Cognitive Impairment (MCI)", and "Pure Alzheimer's Disease (AD)" categories have been the focus of this present research, which employs an AANFIS classification. The proposed AANFIS method outperforms the state-of-the-art KNN as well as EFKNN methods in aspects of classifying and identification performance.
Data analytics is critical for businesses and organizations to understand their data's behaviour,... more Data analytics is critical for businesses and organizations to understand their data's behaviour, patterns and insights. Crime analysis is an important component of controlling crime rates in cities and regions based on historical data. This paper proposes a crime analysis methodology supported by machine learning techniques to predict and analyze criminal behaviour patterns. Machine learning algorithms such as Random Forest, Decision Tree, SVM, XGBoost and Multilayer Perceptron are used on the crime dataset for detailed analysis. The framework consists of three phases: data preprocessing, model training, and prediction and analysis. By analyzing crime data using Kaggle's open-source crime records, our project aims to understand crime patterns, determine the crime's severity, and identify where and when it occurred. We use the abovementioned algorithms to classify crime patterns and compare the performance accurately. Our proposed framework uses various visualization techniques to reveal crime trends and predict crimes using machine learning algorithms. Input features used in the algorithms include time, state, district, year, murder, rape, theft and dowry deaths.
Water is given to plants via irrigation systems in a single direction. Devices and sensors that a... more Water is given to plants via irrigation systems in a single direction. Devices and sensors that are connected to the Internet allow users to operate and monitor remotely in the Internet of Things (IoT) domain. In order to achieve this goal, this research article will employ the most recent version of Cisco Packet Tracer 7.3.0 (64-bit), a simulation programme for Cisco packet tracers. Here, temperature, humidity, and lawn sprinkler sensors are just a few examples of the study technology. In order to develop a smart water system with numerous components, including Together, these instruments monitor the surroundings to enhance the drainage system and encourage robust development. Because each of the aforementioned gadgets is linked to the home gateway, users can control and keep an eye on them from a tablet, computer, or smartphone. Simulation findings demonstrate the successful integration of smart devices into building portals, such as environmental monitoring sensors and sprinkler systems. Farmers and homeowners have benefited from the technology as it made it simple for them to grow and nurture plants while also remotely protecting the environment.
There are many distinct ailments that have caused different difficulties to the society. Out of t... more There are many distinct ailments that have caused different difficulties to the society. Out of those diseases, cancer has shown the foremost devastating results. Researchers have proposed that cancer may be identified in its early stages and may be prevented by conducting research and looking into the disease's pattern of growth. In this research, a hybrid method of detection, extracting further to classify the various lung cancers using deep learning technique has been proposed. First, datasets of various carcinomas are acquired, and then the dataset images are labelled using feature extraction. Deep learning, a latest branch of AI which can facilitate to boost the performance of Convolutional Neural Network-based systems has been employed.
Advanced Double Input Layered Neural Network has the potential to revolutionize medical diagnosti... more Advanced Double Input Layered Neural Network has the potential to revolutionize medical diagnostics by solving pressing problems. It improves diagnostic precision by providing a single, unified platform for the examination of both organized and unstructured medical data. It provides real-time decision assistance and data management by utilizing the scalable, secure, and efficient data processing capabilities made possible by cloud computing. Improved patient care, more effective therapy, and higher-quality healthcare are some areas where Advanced Double Input Layered Neural Network can make a difference. Rapid, precise, and secure data analysis; the administration of multiple data sources; and realtime decision support are a few of the difficulties inherent in medical diagnosis. This research proposes an Advanced Double Input Layered Neural Network (ADILNN) with a double input layered neural community, enabling it to examine prepared and unstructured scientific data. This novel technique improves the community's mastering functionality from various data kinds. By centralizing statistics garage, processing, and analysis on the cloud, computing sources may be extra reliably accessed whilst wanted. The network's diagnostic precision and flexibility are each advanced through way of tool gaining knowledge of strategies (MLM). Because of its adaptability, ADILNN can be utilized in diverse medical fields, which include radiology, pathology, cardiology, and genetics. It permits examine genomic information, making recovery alternatives, and analyzing x-ray photos. The technique has numerous ability makes use of in healthcare, improving prognosis accuracy in diverse settings. Simulation analysis is used to gauge ADILNN's capacity by way of gauging its diagnostic accuracy, processing speed, scalability, and records protection. These research validate ADILNN's potential to improve clinical analysis, streamline facts management, and guarantee healthcare records's safe and effective utility.
Search engines have become an integral part of our lives. To augment the power of such engines-ev... more Search engines have become an integral part of our lives. To augment the power of such engines-even while offline-was our goal. To accomplish this, a distinct offline search engine was created to retrieve data from archives. An updated UI was achieved to make interacting with the software more appealing and convenient. A spell check technique to recommendations emanated from the assessed query is the most major addition to the interface. The general goal is to make offline search engines more accessible to use. To provide satisfactory results with the spell check mechanism, to use a binary converter so that the query correction and evaluation become even more accurate.
For a variety of reasons, this identification strategy plans several compensations over outdated ... more For a variety of reasons, this identification strategy plans several compensations over outdated techniques including ID cards (tokens) or PIN statistics (passwords) such as, i) The person who is to be recognized is mandatory to be physically present at the point of identification. ii) Identification based on biometric methods eliminates the need to remember a password or carry an encrypted token. This work attempts to understand the significance of mobile devices for society. Using user-friendly safe mobile applications, the services can be distributed ensuring data security and protection and can be hosted on cloud service both as client-side service and backend service.
The Autism Spectrum Disorder (ASD) is a neurological disease, which affects the mental, social an... more The Autism Spectrum Disorder (ASD) is a neurological disease, which affects the mental, social and physical state of a person. A person of any age group can be found infected by it. It is very difficult to identify, if a person is the victim of this disorder. Classical approaches that find the occurrence of autism in a person is time consuming and expensive. Machine learning approaches, on the other hand, have paved the way for intelligent diagnostics. This paper focusses on identification of specific traits that helps to automate the diagnosis process and further evaluate and perform a comparative analysis of various machine learning algorithms namely K-Nearest Neighbour, Logistic Regression, SVM and Naïve Bayes, to predict the occurrence of autism disorder. Experimental analysis shows that the Naïve Bayes algorithm provides a better accuracy of 99.6% compared to other algorithms.
The field of computer science known as machine learning explores algorithms that learn from examp... more The field of computer science known as machine learning explores algorithms that learn from examples. Classification requires machine learning algorithms that understand how to apply a class label to data from the problem area. An easy-to-understand example is categorizing emails as "spam" or "not spam." Binary classification predicts one of two classes, whereas multi-class classification predicts one of several classes. In most binary classification tasks, there is one type of normal state and another type of aberrant state. This Project involves how lung disease prediction using x-ray images will predict through the binary classification model implemented, and various python libraries like Tensor Flow, Keras, NumPy, etc. are used. This research project will observe the prediction of lung diseases by using x-ray images and further the output will be predicted with the detailed example and a detailed source code. The implementation will be shown step by step with possible screenshots.
Brain tumours are one of the most fatal and common cancers in both children and adults. As a resu... more Brain tumours are one of the most fatal and common cancers in both children and adults. As a result, early detection of the exact type of brain tumour is critical for developing a precise treatment plan and predicting the patient's reaction to the treatment. In this field, there has recently been a spike of interest in using Convolution Neural Networks (CNN) to classify various kinds of brain cancer. However, CNNs require a large amount of training data and are incapable of managing input changes. Capsule networks (also known as CapsNets) are relatively new machine learning structures that were designed to address the weaknesses of CNNs. They are expected to improve deep learning solutions. Capsule networks are stable to rotation and affine translation, and therefore require much less training data, as is the case when processing medical image datasets such brain Magnetic Resonance Imaging (MRI) images, the proposed model provides better results.
Image hash regimes have been widely used for authenticating content, recovery of images and digit... more Image hash regimes have been widely used for authenticating content, recovery of images and digital forensics. In this article we propose a new algorithm for image haunting (SSL) with the most stable key points and regional features, strong against various manipulation of content conservation, including multiple combinatorial manipulations. In order to extract most stable keypoint, the proposed algorithm combines the Speed Up Robust Features (SURF) with Saliency detection. The keyboards and characteristics of the local area are then combined in a hash vector. There is also a sperate secret key that is randomly given for the hash vector to prevent an attacker from shaping the image and the new hash value. The proposed hacking algorithm shows that similar or initial images, which have been individually manipulated, combined and even multiple manipulated contents, can be visently identified by experimental result.The probability of collision between hacks of various images is almost nil. Furthermore, the keydependent security assessment shows the proposed regime safe to allow an attacker without knowing the secret key not to forge or estimate the right havoc value.
The advancement of technology offers solution to the complex problems faced by the society and br... more The advancement of technology offers solution to the complex problems faced by the society and brings the wellbeing of the individuals. Smart healthcare is prominent nowadays for diagnosis, treatment and constant monitoring which reduces visitation of hospital, transport cost and waiting time. Voice pathology is a decease which affects the person vocal cord so that one who facing difficult in speech. If the decease not identified in time, it leads to permanent loss of voice for an individual. Traditionally, the decease is identified through oral examination or manual procedures. Due to the advent of smart phone, one can record the voice and send it to the cloud server for processing. Our system classifies the voice data and provides the decision to the user. This greatly reduces the transport cost, and waiting time for oral examination at medical center. The mobile phone recorded the patient voice data and it will be stored into the cloud. The voice data is synthesized to signals and with the help of deep Neural network the voice pathology can be identified. The system has been tested with the data set and the test result shows promising.
Document clustering is an important aspect of big data. Data growth has been directed to time for... more Document clustering is an important aspect of big data. Data growth has been directed to time for many days, and storing the data in a structured manner is an issue. Clustering is a strategy and procedure for automatically grouping together relevant documents. The clustering of documents is a solution for organizing important database items. Our paper's major purpose is to bring together pertinent records. This work proposes a hybrid big data handling architecture as a clustering technique that effectively clusters documents. This project uses the K-Means and Gaussian Firefly Algorithms to cluster documents and search for beginning values, as well as gauze to optimize and increase the accuracy of square and time errors. Finally, to update the cluster's initial values or centroid, we add the squared errors. The cluster's efficiency improves as a result of this. The experimental data is sufficient, and the simulation results are extremely trustworthy. With a low squared error sum, the high-quality solutions look quite promising. (4)stract
The cloud is the leading technology in the IT world for storing and managing massive quantities o... more The cloud is the leading technology in the IT world for storing and managing massive quantities of data. Protection and data privacy preservation are two of the most common concerns in big data. Confidential information must be secured from multiple unauthorized accesses in attempt to optimize its security. Different traditional cryptography algorithms have been used in the security of big data in the cloud to enhance privacy. Still, because of its reduced security, there are some privacy protection concerns. With the emergence of IoT-cloud-based devices, IoT has advanced significantly in the field of big data processing. The health-care system is one of the recent IoT-based Big data applications. To preserve the privacy of patient data, several studies are required. Data security and computing overheads are still major challenges in the IoTcloud-based health system. To ensure the privacy of huge data, the ElGamal Elliptic Curve (EGEC) encryption technique is proposed. The results are analyzed and the comparison depicts the outperformance of the proposed system.
Nowadays Huge volume of data is stored in the public cloud by the proprietors in various sectors.... more Nowadays Huge volume of data is stored in the public cloud by the proprietors in various sectors. As these data is highly confidential they need to integrate cloud together with a particular set and encryption of characteristics to get into command on the cloud data. While uploading the data into the public cloud it will be assigned a bit of attribute ready to the data of theirs. In case any of the authorized cloud person really wish to retrieve back his own data, they need to type the specific feature placed to carry out additional steps on Data owner's information. A cloud person really wants to register the details of theirs below the cloud group to use the Data owner's information. Owners wish to publish the details of theirs as characteristics together with the designation of theirs. In line with the person specifics, Semi Trusted Authority makes decryption secrets to obtain command on the owner's information. An end-user is able to conduct a great deal of businesses with the cloud Data. The attribute-based security schemes for managing cloud data in our suggested system and attaining the security level when compared to the present system.
With the advancement of technology and ever-growing trough of data around the world there has bee... more With the advancement of technology and ever-growing trough of data around the world there has been a fast increase in the accessibility of clinical databases and clinical symbolisms to everyone around the globe. The inaccuracy of the world in foreseeing oncoming and identifying present diseases before they worsen, from these data troughs has invited the network of researchers to soak up the challenges in this area. The crucial apprehensive system of homo sapiens is specially composed of the combination of the cerebrum, pons, cerebellum, encephalon which we in short call as the 'Brain'. This brain is then connected to the rest of the body via the Spinal cord. The human mind tosses and throws a wide variety of demanding situations and a great number of difficulties to the community of researchers. Machine Learning(ML) capabilities furnish machines with the capacity to become competent in domains without being clear-cut coded for the same. Data from various clinical sources was dissected adequately utilizing ML calculations and interpretations were made on the outcomes. Vital elements of the chosen research-wings of the healthcare system, mining of data, various kinds of analytics, information, data and statistical assets-were extricated to give a methodical perspective on advancements in this field and conceivable future bearings. Absence of prescriptive investigation in exercise and integration of domain professionals in the dynamic procedure, stresses the need of examination and research in the upcoming future. In this paper we survey the various methods used for the implementation of ML backed technology into serving the needs of the medical field in both. Detection classification of a brain tumour from an MRI.
These days we have an increased number of heart diseases including increased risk of heart attack... more These days we have an increased number of heart diseases including increased risk of heart attacks. In our proposed system, patient use sensors that allows to detect heart problem of a person using pulse sensor, temperature sensor and ECG sensor even if the person is at home. The sensor is then interfaced to a microcontroller that allows checking heart rate readings and transmitting them over internet. After setting high and low limits, the system starts monitoring and as soon as patient heart beat goes above a certain limit, the system sends an alert to the controller which then transmits this over the internet and alerts concerned patient's caretakers and also provides recommendation of closest hospital location so that the person can be saved on time.
An intrusion detection system (IDS) is a product application or contraption that screens the fram... more An intrusion detection system (IDS) is a product application or contraption that screens the framework or system practices for methodology encroachment or dangerous activities and makes reports to the organizational framework. The principal centralization of intrusion discovery and aversion frameworks (IDPS) is to perceive the possible events, information logging about them, and interruption tries to report. Furthermore, the associations are utilizing IDPS for different purposes, such as recognizing issues identified with approaches of security, recording, and keeping the people existing dangers from encroaching arrangements of security. In this paper, anomaly is identified utilizing enhanced correlation-based feature selection (CFS), which is basically a subset technique and is based upon extreme learning machine, multilayer perceptron, and feature selection. This project scope involves identification of anomalies in the early stages and to increase the accuracy of identification or detection.
The quick increment in the online data content has made it extremely troublesome for individuals ... more The quick increment in the online data content has made it extremely troublesome for individuals to discover data that is pertinent to their requirements and interests. Proposal framework is an intense apparatus that gives a potential answer for this test by offering a mechanized component to search out applicable and new data. Individuals depend on client surveys on the web when they require data about item, motion picture, video, news, eatery, and so on. In any case, fundamental issue is giving suggestion to client shape huge information environment. Huge information is a rising innovation, as the name recommends is about taking care of huge measure of information, technique and preparing the information inside middle of the road passed time. An enormous information is gathered and handled to settle on some choice furthermore used to portray any sort of information which might be organized, semi-organized, unstructured and if the information develops. In this work our primary commitment is to prescribe online recordings to client in view of client intrigue. In past framework, recordings were positioned in view of the viewer of that video yet in our proposed framework recordings are positioned and prescribed to client in view of remarks, check and likes. Here client can give remarks just in the event that he/she has watched that specific video, generally clients are not qualified to give remarks on that video. By the assistance of this element we rank recordings in remarks and likes based. The upgrade of our proposition is the opinion grouping of client remarks, with two conceivable marks: negative (neg) and positive (pos). The given technique is utilized to enhance the versatility and effectiveness, if information develops.
RFID is a technology similar to that of bar code scanning. An RFID system consists of tags, which... more RFID is a technology similar to that of bar code scanning. An RFID system consists of tags, which use radio frequency signals to transmit its location information to a reader, which usually sends this information to a server that processes it according to the needs of the application. This paper presents a system that can track buses across a city by placing RFID tags in the buses and the readers in every alternative bus stop. The local server for the city recieves the location information, and alerts the forthcoming bus stops in the route of the bus, of the bus' number, route and expected time of arrival, which are then displayed at the stop.This system thus describes is a cost effective and easy to implement scheme for tracking buses in real time.
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