34 research outputs found
A new design paradigm for provably secure keyless hash function with subsets and two variables polynomial function
No full textProvably secure keyless hash function uses Random Oracle (RO) or Sponge principles for the design and construction of security-centric hash algorithms. It capitalizes the aforesaid principles to produce outcomes like MD2, MD5, SHA-160, SHA-224/256, SHA-256, SHA-224/512, SHA-256/512, SHA-384/512, SHA-512, and SHA-3. These functions use bitwise AND, OR, XOR, and MOD operators to foresee randomness in their hash outputs. However, the partial breaking of SHA2 and SHA3 families and the breaking of MD5 and SHA-160 algorithms raise concerns on the use of bitwise operators at the block level. The proposed design tries to address this structural flaw through a polynomial function. A polynomial function of degree 128 demands arduous effort to be decoded in the opposing direction. The application of a polynomial on the blocks produces an unpredictable random response. It is a fact that the new design exhibits the merits of the polynomial function on subsets to achieve the avalanche response to a significant level. The output from experiments with more than 24 Million hash searches proves the proposed system is a provably secure hash function. The experiments on avalanche response and confusion and diffusion analysis prove it is an apt choice for security-centric cryptographic applications
HyPRETo: Hybrid Pre-trained Ontology Approach for Contextual Relation Classification on Mosquito Vector Biocontrol Agents
No full textPart 3: SDG 9 Industry, Innovation and InfrastructureInternational audiencePre-trained Language Model facilitates contextual relation classification by capturing contextual information, addressing word ambiguity, encoding global sentence context, enabling transfer learning, handling out-of-vocabulary words, and improving performance with limited labelled data. Existing pre-training approaches suffer in size, bias, interpretability, generalization, and the lack of domain specificity. To address this, HyPRETo, the hybrid model that combines the strength of token replacement and dynamic masking is proposed to achieve upgraded performance to increase classification accuracy. The Mosquito Vector Biocontrol Agents data is used for implementing the model for a contextual relation classification task. HyPRETo uses ontology to provide structured knowledge. HyPRETo is pre-trained by ELECTRA and fine-tuned by RoBERTa models. Feedforward and softmax activation function is used for classification. The Natural Language Processing technique and SQL database are used to develop an automated question-answering system. The HyPRETo was evaluated with state-of-art models and achieved 98.42% accuracy. As a contribution, the manually annotated input dataset on the mosquito vector control agent is prepared for the classification task. Subsequently, the enhanced model is developed. The interface for an automated question-answering system for mosquito vector biocontrol agents is developed to assist public health applications such as mosquito vector control, disease control, ecosystem management, environmental conservation, and so on
eHyPRETo: Enhanced Hybrid Pre-Trained and Transfer Learning-based Contextual Relation Classification Model
Get PDFIntroduction: relation classification (RC) plays a crucial role in enhancing the understanding of intricate relationships, as it helps with many NLP (Natural Language Processing) applications. To identify contextual subtleties in different domains, one might make use of pre-trained models.
Methods: to achieve successful relation classification, a recommended model called eHyPRETo, which is a hybrid pre-trained model, has to be used. The system comprises several components, including ELECTRA, RoBERTa, and Bi-LSTM. The integration of pre-trained models enabled the utilisation of Transfer Learning (TL) to acquire contextual information and complex patterns. Therefore, the amalgamation of pre-trained models has great importance. The major purpose of this related classification is to effectively handle irregular input and improve the overall efficiency of pre-trained models. The analysis of eHyPRETo involves the use of a carefully annotated biological dataset focused on Indian Mosquito Vector Biocontrol Agents.
Results: the eHyPRETo model has remarkable stability and effectiveness in categorising, as evidenced by its continuously high accuracy of 98,73 % achieved during training and evaluation throughout several epochs. The eHyPRETo model's domain applicability was assessed. The obtained p-value of 0,06 indicates that the model is successful and adaptable across many domains.
Conclusion: the suggested hybrid technique has great promise for practical applications such as medical diagnosis, financial fraud detection, climate change analysis, targeted marketing campaigns, and self-driving automobile navigation, among others. The eHyPRETo model has been developed in response to the challenges in RC, representing a significant advancement in the fields of linguistics and artificial intelligenc
eHyPRETo: Enhanced Hybrid Pre-Trained and Transfer Learning-based Contextual Relation Classification Model
No full textIntroduction: relation classification (RC) plays a crucial role in enhancing the understanding of intricate relationships, as it helps with many NLP (Natural Language Processing) applications. To identify contextual subtleties in different domains, one might make use of pre-trained models.
Methods: to achieve successful relation classification, a recommended model called eHyPRETo, which is a hybrid pre-trained model, has to be used. The system comprises several components, including ELECTRA, RoBERTa, and Bi-LSTM. The integration of pre-trained models enabled the utilisation of Transfer Learning (TL) to acquire contextual information and complex patterns. Therefore, the amalgamation of pre-trained models has great importance. The major purpose of this related classification is to effectively handle irregular input and improve the overall efficiency of pre-trained models. The analysis of eHyPRETo involves the use of a carefully annotated biological dataset focused on Indian Mosquito Vector Biocontrol Agents.
Results: the eHyPRETo model has remarkable stability and effectiveness in categorising, as evidenced by its continuously high accuracy of 98,73 % achieved during training and evaluation throughout several epochs. The eHyPRETo model's domain applicability was assessed. The obtained p-value of 0,06 indicates that the model is successful and adaptable across many domains.
Conclusion: the suggested hybrid technique has great promise for practical applications such as medical diagnosis, financial fraud detection, climate change analysis, targeted marketing campaigns, and self-driving automobile navigation, among others. The eHyPRETo model has been developed in response to the challenges in RC, representing a significant advancement in the fields of linguistics and artificial intelligenc
mAedesID: Android Application for Aedes Mosquito Species Identification using Convolutional Neural Network
Get PDFVector-Borne Disease (VBD) is an infectious disease transmitted through the
pathogenic female Aedes mosquito to humans and animals. It is important to
control dengue disease by reducing the spread of Aedes mosquito vectors.
Community awareness plays acrucial role to ensure Aedes control programmes and
encourages the communities to involve active participation. Identifying the
species of mosquito will help to recognize the mosquito density in the locality
and intensifying mosquito control efforts in particular areas. This willhelp in
avoiding Aedes breeding sites around residential areas and reduce adult
mosquitoes. To serve this purpose, an android application are developed to
identify Aedes species that help the community to contribute in mosquito
control events. Several Android applications have been developed to identify
species like birds, plant species, and Anopheles mosquito species. In this
work, a user-friendly mobile application mAedesID is developed for identifying
the Aedes mosquito species using a deep learning Convolutional Neural Network
(CNN) algorithm which is best suited for species image classification and
achieves better accuracy for voluminous images. The mobile application can be
downloaded from the URLhttps://tinyurl.com/mAedesID.Comment: 11 pages, 13 figures, This paper was presented at the International
Conference on KnowledgeDiscoveries on Statistical Innovations and Recent
Advances in Optimization (ICON-KSRAO)on 29th and 30th December 2022. only
abstract is printed in the conference proceeding
Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm for Image Segmentation
No full textConstriction coefficient based particle swarm optimization and gravitational search algorithm for multilevel image thresholding
No full text
