Published in 2026
Gulshan Kumar
The internet of things (IoT), also called internet of all, is a new paradigm that combines several technologies such as computers, the internet, sensors network, radio frequency identification (RFID), communication technology and embedded systems to form a system that links the real worlds with digital worlds. IoT represents a system which consists things in the real world, and sensors attached to or combined to these things, connected to the Internet via wired and wireless network structure. The IoT sensors can use various types of connections such as RFID, Wi-Fi, Bluetooth, and ZigBee, in addition to allowing wide area connectivity using many technologies such as GSM, GPRS, 3G, and LTE. By the technology of the IoT, the world will becomes smart in every aspects, since the IoT will provides a means of smart cities, smart healthcare, smart homes and building, in addition to many important applications such as smart energy, grid, transportation, waste management and monitoring . In this paper we review a concept of many IoT applications and future possibilities for new related technologies in addition to the challenges that facing the implementation of the IoT.
Internet of Things, ARPANET, ICANN, Radio Frequency Identification, WSN, Cloud Computing, Embedded Systems, Smart Object.
Shimaz Khan Shaik, Shameer Mohammed, Dr. Ramkumar, Dr. Syed ShahulHameed
Oman’s Ministry of Health (MOH) has adopted the National Electronic Health Record ‘NEHR’ system called Al-Shifa. The purpose of this paper is to review and condense the architecture paradigm and drawbacks of the Al-Shifa system and to suggest an alternative architecture to overcome its limitations. This study employs a focus group based qualitative approach and the Al-Shifa project team stationed in the Directorate General of Health Services (DGHS), Sur was part of this group and they provided valuable insight into the current Al-Shifa system. The study reveals that the existing Al-Shifa system is based on the client-server model and the module deployed on the server-side is monolithic and tightly coupled in nature. The important disadvantages that were identified are lack of interoperability, lack of support for new technology stack, difficult and time-consuming modifiability, and redundant health records across different health care units, and lack of data exchange. These limitations mitigate the various benefits of adopting EHR systems. Hence in this paper, an innovative architecture model based on the Microservice paradigm is proposed that could
EHR, Monolithic, Tightly coupled, MOH, IoT, Microservice
Gulshan Kumar
Cryptography is a technique used today hiding any confidential information from the attack of an intruder. Today data communication mainly depends upon digital data communication, where prior requirement is data security, so that data should reach to the intended user. The protection of multimedia data, sensitive information like credit cards, banking transactions and social security numbers is becoming very important. Data is any type of stored digital information. Security is about the protection of assets. Data security refers to protective digital privacy measures that are applied to prevent unauthorized access to computers, personal databases and websites. Cryptography is evergreen and developments. Cryptography protects users by providing functionality for the encryption of data and authentication of other users. Compression is the process of reducing the number of bits or bytes needed to represent a given set of data. It allows saving more data. Cryptography is a popular ways of sending vital information in a secret way. There are many cryptographic techniques available and among them AES is one of the most powerful techniques. The scenario of present day of information security system includes confidentiality, authenticity, integrity, no repudiation. The security of communication is a crucial issue on Wo
Cryptography, Data Encryption, Data Decryption, Digital Signature, Security, Integrity
S. Menaka, M.M. Kavitha
In cross bucket generalization, divide micro-data into equivalence teams and buckets. First, it provides separate protection for identity and sensitive values, and therefore the level of protection may be flexibly adjusted supported actual demands. Second, the sizes of equivalence teams and buckets are reduced as way as potential by solely satisfying the protection needs, that avoid the over shielding for identity and cut back data loss. Most duplicates are detected by progressive duplicate detection. Rather than reducing the general time required to end the whole method, progressive approaches try and scale back the typical time when that a replica is found. To discover the duplicity with less time of execution and additionally while not perturbing the dataset quality, strategies like concurrent Progressive blocking and concurrent Progressive Neighborhood are used. Progressive sorted neighborhood technique conjointly known as CC-PSNM is employed during this model for locating or sleuthing the duplicate during a parallel approach.
Data mining, Data bucketization, Concurrent Progressive Blocking, Concurrent sorted neighborhood, Privacy Data, Generalization, Cross Bucket.
S. Dhivya, K.M. Padma priya
In this thesis, execute a machine learning approach for elegant edges exploitation differential privacy. Privacy preservation is important for machine learning and massive data processing, however, measures designed to guard personal data usually end in a substitution reduced utility of the coaching samples. These papers introduce a privacy conserving approach that will be applied to call tree learning, without a connected loss of accuracy. It describes an approach to the preservation of the privacy of collecting information samples in cases wherever data from the sample information has been lost partly. This approach converts the initial sample knowledge set into a group of Non-Sensitive information sets, from that the initial samples may not be rebuild without the complete group of unreal information sets. Meanwhile, a correct analysis is often designed directly from those unreal information sets. This novel approach may be applied directly to the information storage as shortly as the initial sample is collected.
Data mining, Privacy Preserving, OTP, OBP, GP, Sources Anonymity, Cryptography Model.
R.Bharathi, L.Senthilkumar
A query facet is a significant list of information nuggets that explains an underlying aspect of a query. Existing algorithms mine facets of a query by extracting frequent lists contained in top search results. The coverage of facets and facet items mined by this kind of methods might be limited, because only a small number of search results are used. In order to solve this problem, we propose mining query facets by using knowledge bases which contain high-quality structured data. Specifically, we first generate facets based on the properties of the entities which are contained in Freebase and correspond to the query. Second, we mine initial query facets from search results, then expanding them by finding similar entities from Freebase. Experimental results show that our proposed method can significantly improve the coverage of facet items over the state-of-the-art algorithms.
mining, Query Facet Mining, QD Miner algorithm, clusters, Query Search
S.Ramya, S.Gandhimathi, Dr V.Baby Deepa
Pattern mining plays an essential role in many data mining tasks that attempt to find valuable patterns in massive databases. These patterns can include associations, correlations, sequences, episodes, classifiers and/or clusters. However, discovering such patterns is time-consuming because the extracted patterns are often too numerous and thus difficult to analyze by end users, especially when complex data structures are taken into consideration. Furthermore, existing work mainly. Concentrates on discovering common knowledge in a single data set; and not much attention has been given to identifying significant differences among several data sets. This thesis mainly targets four pattern mining topics on complex data structures to provide solutions to the above problems.
Pattern mining, Association Rules, Probability-Based Frequent Subtree, BFS-based Candidate Subtree Generation
S.Kaviya, K.Ranjith Singh
An Unattended Wireless Sensor Network (UWSN) is a type of sensor network where a trusted sink visits each node periodically to collect the data. Due to offline nature of this network, every node has to secure the sensed data until next visit of the sink i.e. until data is being sent to the sink. UWSNs are operating in hostile environment where the goal of an attacker is to prevent targeted data from ever reaching the sink or to send corrupted data to the sink. So, in a UWSN data confidentiality and data authenticity are required. Besides we have to take care of survivability of sensed data. In this paper presented a scheme that provides authenticity, confidentiality and survivability of sensed data in efficient manner. All these issues were not addressed together in any of the previous scheme for securing UWSNs. Trust management systems have been recently introduced as a security mechanism in UWSN Here trust means the confidence of an entity on another entity based on the expectation that the other entity will perform a particular action important to the trustee. CONFIDANT [24] protocol is a reputation system that has been applied to WSN. In this paper described the Directed Diffusion routing protocol, different threats and attacks on UWSN, overview of trust and different trust models in UWSN and attacks on Directed Diffusion. Our trust model is introduced that works on directed diffusion.
Unattended Wireless Sensor Network, Directed Diffusion routing protocol, Trust Management, Security
L.Sowmya, V.Shanmugapriya
Association rule mining, very important techniques of data mining. It aim at searching for interesting relationships in the middle of items in a large data set or database and discovers association rules among the huge no of item sets. ARM aims to analyze frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories. Data mining can perform these various activities using its technique like clustering, classification, prediction, association learning etc. This paper aims at giving a theoretical survey on some of the existing algorithms.
Association rule mining, Apriori, Item sets, SVM, K-Means, Decision Tree
S.Nithya, S.Sivakumar
In computer vision, image segmentation is the process of partitioning a digital image into multiple segments according to sets of pixels. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. The main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image recognition, image analysis, information retrieval, bioinformatics, data compression and computer graphics. Many clustering algorithms are used for analyzing an image. In this paper we have used two algorithms k-means clustering and Adaptive k-means clustering. K-means clustering algorithm belongs to Partitional clustering. It is widely used in image processing specially in image segmentation. K-means clustering segments the images but not gives satisfactory results of desire objects. To overcome the problems we used Adaptive k-means clustering algorithm. Both the algorithms implemented on images of different resolutions and compared the performance of the segmented images. The performances are compared based on some parameters. The parameters are PSNR, RMSE, SSIM, Elapsed time period. From each and every comparison we have concluded that the Adaptive k-means clustering gives better result as compared to k-means clustering. By using these techniques we can isolate any target object from medical images specially detection of cancerous data. Both the algorithms also implemented in other fields like telecommunication, scientific especially high resolution images.
Image segmentation, k-means clustering, Adaptive k-means clustering, MRI images, and image resolution, PSNR, RMSE, and SSIM etc.
U.Jayasri, Dr K.Jayasudha
The data mining is used with employee selection for the algorithm of Fuzzy c-means, Support vector machine. As the great growth of industrial side, many company are seeking for more and more employee, not only an ordinary and less-skilled employee, but to get well trained and fit best at their work easier without missing an opportunity to get a best employee. Also the objective of this study is to develop a decision making and evaluating system for employee recruitment using fussy analytic. The system will firstly, calculate the weight of pair wise comparison generated by administrator. Then, system will retrieve all data about the applicant. Every person has its own record in respective curriculum vitae. The system will retrieve only important data such as age, gender, education, work, experience, and desired salary. Then system will retrieve the condition of desired employee from human resource development department. Every single data on all participant is calculated as a number and the system will compare it with fuzzy analytical process (later on will abbreviated as APH) weight calculated from pair matrix and show a result. The result shows a form of rank, which shows the fittest applicant to the available job vacancy.
Fuzzy C-Means Algorithm, Neuro-Fuzzy Classifier, Support Vector Machines, Classifications
G.Haripriya, Dr S. Sathiyabama
Data mining is a process, which finds useful patterns from large amount of data. Data mining is looking for hidden, valid, and potentially useful patterns in huge data sets [1]. Data mining is classified into predictive and Descriptive models. In the present work the techniques of data mining used classification, were classification is a predictive method. The predictive method makes prediction about values of data. Classification techniques are using various decision tree algorithms. In this Paper, four decision tree algorithms (ID3, C4.5, Random forest and CART) have been applied on the historical data for predicting the Stock Market performance in Price. The efficiency of various decision tree algorithms can be analyzed based on their accuracy and time taken to derive the tree. The predictions obtained from the system have helped the Investor to identify and improve their performance. Particularly, this work is carried out to compare the four decision tree algorithms in the prediction of the performance accuracy in Stock Market data. All the algorithms are applied for Stock Market data to classify the data set for classification and prediction. Among these four methods, this work concludes the best algorithm for the chosen input data on decision tree supervised learning algorithms to predict the best classifier. This learn about tries to help the buyers in the inventory market to decide the better timing for buying or promoting shares based totally on the information extracted from the historical prices of such stocks. The decision taken will be based on the best decision tree algorithms.
Data mining Techniques, Knowledge discovery process, Data mining, Classification, and Decision tree Algorithms.
S.Narmatha, Dr C.Kavitha
Nowadays, the banking sector is a very necessary sector in our present day generation where almost each and every human has to deal with the bank either physically or online. In dealing with the banks, the clients and the banks face the possibilities of been trapped by fraudsters. Examples of fraud include insurance fraud, credit card fraud, accounting fraud, etc. The most frequent payment mode is credit card for both on-line and offline in today’s world, it presents cashless purchasing at each store in all countries. It will be the most convenient way to do on-line shopping, paying bills etc. Hence Detection of fraudulent activity is necessary in credit card transactions. Credit card fraud can be efficiently detected by data mining techniques. Data mining techniques such as classification, clustering and association rules analyze the credit card transaction data and predict the patterns which lead to fraud. Because of increased credit card fraud, the main aim of this research is to develop a model that should efficiently analyze and predict the fraudulent data set. In this research, an efficient credit card fraud detection model P-SVM classification is proposed and compared with decision tree and Naïve Bayes algorithms. The proposed model uses credit card holder’s expenditure behavior and location analysis for analyzing and predicting fraud in the credit card transaction data. Various evaluation measures are used for analyzing the performance of Naïve Bayes, Decision Tree and P-SVM classification technique and proved that P-SVM yields higher accuracy when compared with Naïve Bayes and Decision Tree.
Fraud Detection, P-SVM classification, Classification Algorithm, Naïve Bayes , Profile, Decision Tree, Credit Card, Time Series.