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  1. Article ; Online: A novel artificial intelligence approach to detect the breast cancer using KNNet technique with EPM gene profiling.

    Joshi, Shubham / Natteshan, N V S / Rastogi, Ravi / Sampathkumar, A / Pandimurugan, V / Sountharrajan, S

    Functional & integrative genomics

    2023  Volume 23, Issue 4, Page(s) 302

    Abstract: Women's most frequent type of cancer is breast cancer, second only to lung cancer. This paper summarizes changes in genomics and epigenetics and incremental biological activities. A tumour develops through a series of phases involving a separate abnormal ...

    Abstract Women's most frequent type of cancer is breast cancer, second only to lung cancer. This paper summarizes changes in genomics and epigenetics and incremental biological activities. A tumour develops through a series of phases involving a separate abnormal gene. Even though many diseases cause DNA mutations, most treatments are designed to relieve symptoms rather than change the DNA. Clustering short palindromic repeats (CRISPR) or Cas9 is the primary approach for discovering and confirming tumorigenic genomic targets. A Kohonen neural network with an expression programming model was developed for gene selection. The main problem in genetic selection is reducing the number of features chosen while maintaining accuracy. This purpose is accomplished systematically. In the end, the approach method performed better than the existing quantum squirrel-inspired algorithm and the recurrent neural network oppositional call search algorithm for genetic selection. The KNNet-EPM model used an expression programming approach to identify gene biomarkers for breast cancer. This method was achieved with RAE of 42%, sensitivity of 93%, f1 score of 88%, accuracy of 98%, kappa score of 83%, specificity of 92% and MAE of 30%.
    MeSH term(s) Female ; Humans ; Breast Neoplasms/diagnosis ; Breast Neoplasms/genetics ; Artificial Intelligence ; Algorithms ; Lung Neoplasms ; Carcinogenesis
    Language English
    Publishing date 2023-09-18
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2014670-X
    ISSN 1438-7948 ; 1438-793X
    ISSN (online) 1438-7948
    ISSN 1438-793X
    DOI 10.1007/s10142-023-01227-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media.

    Nitya Harshitha, T / Prabu, M / Suganya, E / Sountharrajan, S / Bavirisetti, Durga Prasad / Gadde, Navya / Uppu, Lakshmi Sahithi

    Frontiers in artificial intelligence

    2024  Volume 7, Page(s) 1269366

    Abstract: The emergence of social media has given rise to a variety of networking and communication opportunities, as well as the well-known issue of cyberbullying, which is continuously on the rise in the current world. Researchers have been actively addressing ... ...

    Abstract The emergence of social media has given rise to a variety of networking and communication opportunities, as well as the well-known issue of cyberbullying, which is continuously on the rise in the current world. Researchers have been actively addressing cyberbullying for a long time by applying machine learning and deep learning techniques. However, although these algorithms have performed well on artificial datasets, they do not provide similar results when applied to real-time datasets with high levels of noise and imbalance. Consequently, finding generic algorithms that can work on dynamic data available across several platforms is critical. This study used a unique hybrid random forest-based CNN model for text classification, combining the strengths of both approaches. Real-time datasets from Twitter and Instagram were collected and annotated to demonstrate the effectiveness of the proposed technique. The performance of various ML and DL algorithms was compared, and the RF-based CNN model outperformed them in accuracy and execution speed. This is particularly important for timely detection of bullying episodes and providing assistance to victims. The model achieved an accuracy of 96% and delivered results 3.4 seconds faster than standard CNN models.
    Language English
    Publishing date 2024-03-06
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2624-8212
    ISSN (online) 2624-8212
    DOI 10.3389/frai.2024.1269366
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Top ‘N’ Variant Random Forest Model for High Utility Itemsets Recommendation

    Pazhaniraja N / Sountharrajan S / Suganya E / Karthiga M

    EAI Endorsed Transactions on Energy Web, Vol 8, Iss

    2021  Volume 35

    Abstract: High-utility based itemset mining is the advancement of recurrent pattern mining that discovers occurrence of frequent transactions from a huge database. The issues in frequent pattern mining involve the elimination of quantities purchased by the ... ...

    Abstract High-utility based itemset mining is the advancement of recurrent pattern mining that discovers occurrence of frequent transactions from a huge database. The issues in frequent pattern mining involve the elimination of quantities purchased by the customers and cost of purchased product. This can be resolved by high utility itemset mining which includes quantities and profit of the products in the transactions. The conventional association rule mining algorithms results in huge memory consumption due to the complexity in pruning the search space. In this paper, machine learning based high-utility itemset mining is applied to predict next order in an online grocery store depending on the transactions. The overall goal is to enhance the business profitability by stocking the high utility items in market. The Top ‘N’ variant Random Forest model is proposed to recommend the high utility itemsets, thereby predicting the reordered/next ordered items. The model is evaluated using Instacart market dataset to measure accuracy, precision and recall.
    Keywords high utility itemset ; random forest ; machine learning ; association mining ; frequent itemsets ; feature selection ; Science ; Q ; Mathematics ; QA1-939 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 006
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher European Alliance for Innovation (EAI)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Sentence Semantic Similarity Model Using Convolutional Neural Networks

    Karthiga M / Sountharrajan S / Suganya E / Sankarananth S

    EAI Endorsed Transactions on Energy Web, Vol 8, Iss

    2021  Volume 35

    Abstract: In Natural Language Processing, determining the semantic likeness between sentences is an important research area. For example, there exists many possible semantics for a word (polysemy), and the synonym of the word differs. Double LSTM (Long Short Term ... ...

    Abstract In Natural Language Processing, determining the semantic likeness between sentences is an important research area. For example, there exists many possible semantics for a word (polysemy), and the synonym of the word differs. Double LSTM (Long Short Term Memory) working at same time on double phrase sequences model is projected to overcome the solitary sequence problem. Furthermore, with the goal of overcoming the second issue, as indicated by the qualities of English dialect, we utilized the British corpus semantic similarity datasets structured by specialists to prepare, and validate the technique. During the training process the stopwords were reserved for use. Convolution Neural Network and Semantic Likeness model based on grammar are used to compare the results of our projected representation. The outcomes demonstrate that the proposed methodology is more prominent than the previous approaches by means of precision, recall rate, accuracy etc., along with the enhanced generalization potential of the neural network.
    Keywords double sequence ; deep learning ; convolution neural network ; semantic similarity ; Science ; Q ; Mathematics ; QA1-939 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 401
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher European Alliance for Innovation (EAI)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Optimizing QoS and security in agriculture IoT deployments

    Sonali Mahendra Sonavane / G.R. Prashantha / Pranjali Deepak Nikam / Mayuri A V R / Jyoti Chauhan / Sountharrajan S / Durga Prasad Bavirisetti

    Heliyon, Vol 10, Iss 2, Pp e24224- (2024)

    A bioinspired Q-learning model with customized shards

    2024  

    Abstract: Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use ... ...

    Abstract Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use blockchains are either highly complex or require large delays & have higher energy consumption for larger networks. Moreover, the efficiency of these models depends directly on consensus-efficiency & miner-efficiency, which restricts their scalability under real-time scenarios. To overcome these limitations, this study proposes the design of an efficient Q-Learning bioinspired model for enhancing QoS of AIoT deployments via customized shards. The model initially collects temporal information about the deployed AIoT Nodes, and continuously updates individual recurring trust metrics. These trust metrics are used by a Q-Learning process for identification of miners that can participate in the block-addition process. The blocks are added via a novel Proof-of-Performance (PoP) based consensus model, which uses a dynamic consensus function that is based on temporal performance of miner nodes. The PoP consensus is facilitated via customized shards, wherein each shard is deployed based on its context of deployment, that decides the shard-length, hashing model used for the shard, and encryption technique used by these shards. This is facilitated by a Mayfly Optimization (MO) Model that uses PoP scores for selecting shard configurations. These shards are further segregated into smaller shards via a Bacterial Foraging Optimization (BFO) Model, which assists in identification of optimal shard length for underlying deployment contexts. Due to these optimizations, the model is able to improve the speed of mining by 4.5%, while reducing energy needed for mining by 10.4%, improving the throughput during AIoT communications by 8.3%, and improving the packet delivery consistency by 2.5% when compared with existing blockchain-based AIoT deployment models under similar ...
    Keywords Blockchain ; AIoT ; QoS ; Security ; Sharding ; Custom ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
    Subject code 006
    Language English
    Publishing date 2024-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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