International Journals

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/157

Browse

Start date: 2020End date: 2023

Search Results

Now showing 1 - 9 of 9
  • No Thumbnail Available
    Item
    ANDROID APPLICATIONS FOR LUNG NODULES CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK
    (IGI Global, 2023) Karthikeyan, M P; Banupriya, C V; Kowsalya, R; Jayalakshmi, A
    Digital image processing is currently used in various fields of research. One of them is in the field of medicine. In fact, experienced radiologists have difficulty distinguishing the cancerous portions of the blood vessels in the lung or detecting fine nodules that suggest lung cancer on X-ray images. Previous studies have shown that doctors and radiologists fail to detect cancerous patches in 30% of positive cases. Implementation of CAD system to classify and detect parts of cancer has been developed, but the results obtained from this implementation are that there are still many errors in the classification results. Therefore, this study will develop android app image technique to perform the classification process of lung cancer. With this research, it is hoped that the developed algorithm can help doctors and radiologists to detect cancer in a short time with more accuracy. Finally, after 20 iterations, a percentage of 90.65% was attained for the test results' performance in classifying 10 X-ray pictures.
  • No Thumbnail Available
    Item
    A NOVEL HYBRID APPROACH ON SECURE DATA TRANSMISSION IN WIRELESS SENSOR NETWORKS
    (International Journal of Future Generation Communication and Networking, 2020) R, Kowsalya; B, Rosiline Jeetha
    n recent year wireless sensor network plays an important role in day to day life, to achieve the security, cryptography techniques are used. As wireless sensor has the limited memory space and energy consumption to provide security is vital problem. The main aim of this research work is to analysing different cryptographic techniques such as symmetric key cryptography and asymmetric key cryptography and comparing AES, DES, 3DES, RC5 and IDEA encryption techniques. In this paper, a new security symmetric algorithm was proposed to provide high security. It provides cryptographic primary key integrity, confidentiality and authentication. The results show that the proposed hybrid algorithm HSR19 gives efficient performance for communication devices with the parameters in computation time with different file sizes, encryption and decryption speed and energy
  • No Thumbnail Available
    Item
    AN ADAPTION OF CONGESTION AWARE ROUTING IN MANET USING MODIFIED BRANCH AND BOUND WITH DSR PROTOCOL
    (International Journal of Advanced Science and Technology, 2020) Sasikala S; Ponmuthuramalingam P
    Mobile adhoc network is a decentralized wireless system that does not rely on a pre-existing infrastructure. In adhoc the system can be setup at anytime and anywhere. Each node in a MANET act as a router and communicate packets with each other from source to destination. Mobile Adhoc network is a self configured network and nodes move randomly when the network changes frequently due to its infrastructure less network. Routing plays a vital role to select paths in a network. The routing decides the packets route between devices in a MANET. The proposed methodology Modify DSR with Modify Branch and Bound i.e., MDSR with MBB with congestion aware routing focus in rising the energy utilize level, forwarding the packet, Route discovery and avoiding collision when sending packets from an origin to end. The efficiency of the energy level is improved in MANET with the proposed methodology (MDSR with MBB). The metrics were used to calculate the efficiency in a network system such as Lifetime of a network, Energy utilization of a node, End to end delay of packet , Delivery ratio of Packet in a network ,Throughput and Routing overhead.
  • No Thumbnail Available
    Item
    ANALYSIS OF MACHINE LEARNING CLASSIFIERS TO DETECT MALICIOUS NODE IN VEHICULAR CLOUD COMPUTING
    (2022-04-30) Sheela Rini A; Meena C
    VANET or Vehicular networks are created using the principles of MANETS and are used by intelligent transport systems to offer efficient communication between the domains of vehicles. Increasing the number of vehicles requires communication between vehicles to be fast and secure, where cloud computing with VANET is more prominent. To provide a secure VANET communication environment, this paper proposes a malicious or hacked vehicle identification system. Malicious vehicles are identified using four steps. The first step uses a clustering algorithm for similar group vehicles. In the Second step, cluster heads are identified and elected. In the next step, Multiple Point Relays are selected. Finally, classifiers are used to identify hacked vehicles. However, the existing system performance degrades as soon as the number of vehicles increases, resulting in increased cost during Cluster head election, inability to produce stable clusters, and the need for accurate and fast classification in high traffic scenarios. This work improves clustering algorithms and examines several classification algorithms to solve these issues. The classifiers analyzed are Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Naïve-Bayes (NB). A Hybrid classifier that combines SVM and KNN classifiers is also analyzed for its effectiveness to detect malicious vehicles. From the experimental results, it could be observed that the detection accuracy is high while using the hybrid classifier.
  • No Thumbnail Available
    Item
    GATED RECURRENT NEURAL NETWORK FOR AUTISM SPECTRUM DISORDER GENE PREDICTION
    (Research Trend , International Journal on Emerging Technologies, 2020) Pream Sudha V; Vijaya M.S
    Autism Spectrum Disorder (ASD) is the fastest-growing complex disorder and the genetic ground of this comprehensive developmental disability is very difficult to research. Autism diagnosis for an average child is not done till the age of four, though it can be given at the age of 18 months to two years. Hence a computational model that enables the early diagnosis and personalized treatment is the need of the hour. In this research work, a deep learning based approach is proposed for Autism Spectrum Disorder (ASD) gene prediction. There are various contributors for Autism including genes, mutations, chromosomal settings influence of the environment, prenatal influences, family factors and problems during birth. Recurrent Neural Network (RNN) based Gated Recurrent Units (GRU) are implemented to develop a model that predicts ASD genes, mutations and gene susceptibility. GRUs with their internal memory capability are valuable to store and filter information using the update and reset gates. Also GRU offers a powerful tool to handle sequence data. The model is trained using three simulated datasets with features representing genes, mutations and gene susceptibility to ASD. Besides, the proposed approach is compared to original RNN and Long Short Term Memory Units (LSTM) for ASD prediction. The experimental results confirm that the proposed approach is promising with 82.5% accuracy and hence GRU RNN is found to be effective for ASD gene prediction
  • No Thumbnail Available
    Item
    PREDICTION OF GENE SUSCEPTIBILITY TO AUTISM SPECTRUM DISORDER USING DEEP ARCHITECTURES
    (International Journal of Scientific and Technology Research, 2020) Pream Sudha V; Vijaya M.S
    A genetic predisposition or susceptibility to Autism Spectrum Disorder (ASD) is an increased likelihood of developing it based on the genetic makeup of a person. The multiple variants found in each gene have their own probability of associated risk and so the major problem lies in the systematic evaluation of their functional significance to ASD. Hence it is essential to develop methods for quantitative evaluation of ASD candidate genes with co-occurring mutations which will provide a clear understanding of their relevance to ASD. This research work deals with the development of a discriminative model for prioritization of candidate genes considering mutations in them and to classify them based on their predisposition to the disorder. The model for gene susceptibility prediction is built by integrating the combined potential of substantiation for each ASD linked gene and the related mutations. In this research work gene susceptibility prediction is modelled as a pattern classification problem and deep learning techniques are employed to build the models. The performance evaluation of these models proves that Long Short Term Memory (LSTM) based gene susceptibility prediction model has shown better performance.
  • No Thumbnail Available
    Item
    CDARGA: CLUSTER-BASED DATA AGGREGATION WITH GENETIC ROUTING ALGORITHM IN WIRELESS SENSOR NETWORKS
    (International Journal of Recent Technology and Engineering, 2020-02) Kowsalya R; Rosiline Jeetha B
    In the wireless sensor networks (WSNs), the upholding the energy and routing formation at every sensor node is the major issues. The distance from base station and internal node mainly has imbalanced in the energy consumption during transformation of the data. To reduce the energy upholding and the data aggregation routing issues in Centralized Clustering-Task Scheduling for wireless sensor networks (WSNs), this paper focuses on a Cluster-Based Data Aggregation Routing with Genetic search Algorithm (CDARGA) , which reduces the energy consumption in a hyper round model. The proposed data aggregation routing protocol using the Genetic Algorithm (GA) estimates the fitness function using the three key parameters distance, energy, and Hyper round policy. The proposed methods were compared with RP-MAC and the experimental result shows that the proposed protocol is superior to RP-MAC protocol and the proposed algorithm improves the network lifetime which can used in real time application.
  • No Thumbnail Available
    Item
    CHATBOTS USING DEEP NEURAL NETWORK
    (Parishodh Journal, 2020-03) Pavithreja V; Gomathi S; Anitha G
    Businesses which presence across the globe running with a manpower of 5,000+ employees have several queries to enquire with HR like the salary package, the leave details, work enquiry and performance. The chatbots can juggle many basic, day-to-day HR tasks with ease, freeing up the HR professionals to focus on complex tasks that require in-depth expertise. Chatbots unlike answering randomly it acts wisely and analyze the message before conveying with the employees. The information provided by the chatbots will be ground truth. These chatbots that provide the exact information are trained by the set of queries using the ‘Deep Neural Networks’ and NLP. The NLP is used to tokenize and stemming the sentences in python using nltk to parse the messages. The entity prediction is done using the Deep Neural Networks in python using tensorflow module. The chatbot trained is then carried out to life in the Slack messaging app through python.
  • No Thumbnail Available
    Item
    PRODUCT RECOMMENDATION IN MARKET BASKET ANALYSIS
    (Parishodh Journal, 2020-03) Anitha G; Kavin Mozhi T
    Market Basket Analysis plays an important role in analytics. It is used in the retail showrooms to determine the place and sales of products, promotion for different types of customers to improve customer satisfaction and increase the profit of the retailers.This study deals with the concept of market basket analysis with the Apriori algorithm. The concept of the Apriori algorithm is to identify all the frequent itemsets. Through these frequent sets, able to derive association rules, these rules must satisfy minimum support threshold and minimum confidence threshold. It allows retailers to determine the relationship between the items that are purchased by their customers.