Standard Datasets Research Articles

Multi-classification in-vehicle intrusion detection system using packet- and sequence-level characteristics from time-embedded transformer with autoencoder

In recent years, the increased application of controller area network (CAN) protocols has made it the de facto standard for communication between electronic control units (ECUs) in the automotive and transportation fields. This widely used protocol was designed as a reliable and straightforward broadcast-based protocol that connects ECUs without considering security concerns such as node authentication or traffic encryption. Despite its efficiency, this tradeoff makes the CAN bus vulnerable to attacks. Implementing intrusion detection systems (IDSs) based on machine learning (ML) can address these security challenges effectively. However, existing ML-based IDSs have limited classification capabilities, lack adaptability and time sensitivity, incomprehensive analysis, and produce high false-negative rates (FNR), while attack schemes are becoming increasingly complex, resulting in insufficient capability of intrusion detection in real-time and insufficient ability to offer reliable protection. Therefore, our study proposes a novel in-vehicle IDS for multiclass classification using both packet- and sequence-level characteristics extracted from an autoencoder and a variant transformer (Time-embedded Transformer) with an improved position encoding mechanism, which analyses CAN traffic in-depth from various perspectives to overcome the existing challenges above. Both standard (Car-Hacking) and advanced (ROAD) datasets are used to evaluate the capabilities of our proposed IDS. The evaluation results demonstrated 100 % detection accuracy and 0 % FNR for both the Car-Hacking and ROAD Masquerade datasets, which also peaked at the highest F1 score for the ROAD Fabrication dataset, emphasizing superior intrusion detection to minimize FNR of the proposed model with high adaptability through its multi-dimensional analysis at packet- and sequence-level.

Reflection of Daily, Seasonal and Interannual Variations in Run-Off of a Small River in the Water Isotopic Composition (δ2H, δ18O): A Case of the Ala-Archa Mountain River Basin with Glaciation (Kyrgyzstan, Central Asia)

Small intermountain river basins are most suitable for developing new methods to estimate water balance due to their well-defined catchment boundaries, relatively rapid runoff processes, and accessible landscapes for study. In general terms, dissecting the hydrograph of a small mountain river requires calibration of the flow model against multi-year data sets, including (a) glacier mass balance and snow water content, (b) radiation balance calculation, (c) estimation of the groundwater contribution, and (d) water discharge measurements. The minimum primary data set is limited to the precipitation and temperature distributions at the catchment. This approach postulates that the conditions for the formation of all components of river flow are known in advance. It is reduced to calculating the dynamic balance between precipitation (input part) and runoff, ablation, and evaporation (output part). In practice, accurately accounting for the inflow and outflow components of the balance, as well as the impact of regulating reservoirs, can be a challenging task that requires significant effort and expense, even for the extensively researched catchments. Our studies indicate the potential benefits of an approach based on one-time, but detailed, observations of stable isotope composition, temperature, and water chemistry, in addition to standard datasets. This paper presents the results of the 2022–2023 work conducted in the basin of the small mountain river Ala-Archa, located on the northern slope of the Kyrgyz Range in Tien-Shan, which was chosen as an example due to its well-studied nature. Our approach could identify previously unknown factors of flow formation and assess the time and effectiveness of work in similar conditions.

Recommendations of the SUFU/AUGS/ICS Female Stress Urinary Incontinence Surgical Publication Working Group: A common standard minimum data set for the literature.

Relevant, meaningful, and achievable data points are critical in objectively assessing quality, utility, and outcomes in female stress urinary incontinence (SUI) surgery. A minimum data set female SUI surgery studies was proposed by the first American Urological Association guidelines on the surgical management of female SUI in 1997, but recommendation adherence has been suboptimal. The Female Stress Urinary Incontinence Surgical Publication Working Group (WG) was created from members of several prominent organizations to formulate a recommended standard of study structure, description, and minimum outcome data set to be utilized in designing and publishing future SUI studies. The goal of this WG was to create a body of evidence better able to assess the outcomes of female SUI surgery. The WG reviewed the minimum data set proposed in the 1997 AUA SUI Guideline document, and other relevant literature. The body of literature was examined in the context of the profound changes in the field over the past 25 years. Through a DELPHI process, a standard study structure and minimum data set were generated. Care was taken to balance the value of several meaningful and relevant data points against the burden of creating an excessively difficult or restrictive standard that would disincentivize widespread adoption and negatively impact manuscript production and acceptance. The WG outlined standardization in four major areas: (1) study design, (2) pretreatment demographics and characterization of the study population, (3) intraoperative events, and (4) posttreatment evaluation, and complications. Forty-two items were evaluated and graded as: STANDARD-must be included; ADDITIONAL-may be included for a specific study and is inclusive of the Standard items; OPTIMAL-may be included for a comprehensive study and is inclusive of the Standard and Additional items; UNNECESSARY/LEGACY-not relevant. A reasonable, achievable, and clinically meaningful minimum data set has been constructed. A structured framework will allow future surgical interventions for female SUI to be objectively scrutinized and compared in a clinically significant manner. Ultimately, such a data set, if adopted by the academic community, will enhance the quality of the scientific literature, and ultimately improve short and long-term outcomes for female patients undergoing surgery to correct SUI.

Multi-view graph-based interview representation to improve depression level estimation

Depression is a serious mental illness that affects millions worldwide and consequently has attracted considerable research interest in recent years. Within the field of automated depression estimation, most researchers focus on neural network architectures while ignoring other research directions. Within this paper, we explore an alternate approach and study the impact of input representations on the learning ability of the models. In particular, we work with graph-based representations to highlight different aspects of input transcripts, both at the interview and corpus levels. We use sentence similarity graphs and keyword correlation graphs to exemplify the advantages of graphical representations over sequential models for binary classification problems within depression estimation. Additionally, we design multi-view architectures that split interview transcripts into question and answer views in order to take into account dialogue structure. Our experiments show the benefits of multi-view based graphical input encodings over sequential models and provide new state-of-the-art results for binary classification on the gold standard DAIC-WOZ dataset. Further analysis establishes our method as a means for generating meaningful insights and visual summaries of interview transcripts that can be used by medical professionals.

APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control.

Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.

Recommendations of the SUFU/AUGS/ICS female stress urinary incontinence surgical publication working group: A common standard minimum data set for the literature

Introduction and ObjectivesRelevant, meaningful, and achievable data points are critical in objectively assessing quality, utility, and outcomes in female stress urinary incontinence (SUI) surgery. A minimum data set female SUI surgery studies was proposed by the first American Urological Association guidelines on the surgical management of female SUI in 1997, but recommendation adherence has been suboptimal The Female Stress Urinary Incontinence Surgical Publication Working Group (WG) was created from members of several prominent organizations to formulate a recommended standard of study structure, description, and minimum outcome data set to be utilized in designing and publishing future SUI studies. The goal of this WG was to create a body of evidence better able to assess the outcomes of female SUI surgery. MethodsThe WG reviewed the minimum data set proposed in the 1997 AUA SUI Guideline document, and other relevant literature. The body of literature was examined in the context of the profound changes in the field over the past 25 years. Through a DELPHI process, a standard study structure and minimum data set were generated. Care was taken to balance the value of several meaningful and relevant data points against the burden of creating an excessively difficult or restrictive standard that would disincentivize widespread adoption and negatively impact manuscript production and acceptance. Results:The WG outlined standardization in four major areas: (1) study design, (2) pretreatment demographics and characterization of the study population, (3) intraoperative events, and (4) post-treatment evaluation, and complications. Forty-two items were evaluated and graded as: STANDARD — must be included; ADDITIONAL — may be included for a specific study and is inclusive of the Standard items; OPTIMAL — may be included for a comprehensive study and is inclusive of the Standard and Additional items; UNNECESSARY/LEGACY — not relevant. Conclusion:A reasonable, achievable, and clinically meaningful minimum data set has been constructed. A structured framework will allow future surgical interventions for female SUI to be objectively scrutinized and compared in a clinically significant manner. Ultimately, such a data set, if adopted by the academic community, will enhance the quality of the scientific literature, and ultimately improve short and long-term outcomes for female patients undergoing surgery to correct SUI.

Weighted Gene Co-expression Network Analysis of the Inflammatory Wnt Signaling Reveals Biomarkers Related to Bone Formation.

Background and aim Osteocytes regulate bone metabolism and balance through various mechanisms, including the Wnt (Wingless-related integration site signal transduction) signaling pathway. Weighted gene co-expression network analysis (WGCNA) is a computational method to identify functionally related genes based on expression patterns, especially in the Wnt-beta-catenin and osteo-regenerative pathways. This study aims to analyze gene modules of the Wnt signaling pathway from WGCNA analysis. Methods The study used a microarray dataset from the GEO (GSE228306) to analyze differential gene expression in human primary monocytes. The study standardized datasets usingRobust Multi-Array Average (RMA)expression measure and Integrated Differential Expression and Pathway (IDEP) analysistool, building a co-expression network for group-specific component (GC) genes. Results The study uses WGCNA to identify co-expression modules with dysregulated mRNAs, revealing enrichment in Wnt-associated pathways and top hub-enriched genes like colony-stimulating factor 3 (CSF3), interleukin-6 (IL-6), IL-23 subunit alpha (IL23A), suppressor of cytokine signaling 1 (SOCS1), and C-C motif chemokine ligand 19 (CCL19). Conclusion WGCNA analysis of the Wnt signaling pathway will involve functional annotation, network visualization, validation, integration with other omics data, and addressing method limitations for better understanding.

Automating the detection of treatment progression in patients with lung cancer using large language models.

e13620 Background: Accurate assessment of treatment response is fundamental to advancing cancer patient care, particularly in lung cancer, where treatment modalities are diverse and complex. The extraction of treatment response, particularly disease progression information, from Electronic Health Records (EHRs) is essential for several reasons: It facilitates generation of real-world evidence from real-world data, enables personalized treatment planning, and contributes to a broader understanding of cancer therapeutics. However, much of treatment response information is documented in free-text EHR notes and traditional methods of extracting such information from notes are labor-intensive, error-prone, and inefficient, presenting significant barriers to timely and accurate real-world evidence studies. The advancement of Natural Language Processing (NLP) technologies, especially Large Language Models (LLMs), presents a transformative opportunity to automate the extraction of treatment responses. Methods: We used a cohort of 1,953 primary lung cancer patients identified from UPMC Hillman Cancer Center cancer registry and retrieved ~113,000 clinical notes from UPMC clinical data warehouse. We focused on extracting disease progression information, following the RECIST guidelines which define progression as an increase in tumor size or cancer markers after therapy. We selected ~50 notes to perform manual annotations by an experienced oncologist and created a gold standard dataset to validate the NLP models. We fine-tuned a state-of-the-art open-source LLM named LLAMA-2 and compared against a traditional rule-based NLP system for the automated extraction of disease progression from notes. The process of fine-tuning involved adjusting the model's parameters specifically to improve its ability to recognize and classify instances of disease progression. Results: Our analysis demonstrated a significant enhancement in performance with the LLM compared to the traditional rule-based NLP system. LLM exhibited a remarkable increase in sensitivity by ~37%, indicating its superior ability to accurately identify disease progression. Additionally, the model maintained high specificity and PPV, achieving scores nearly comparable to the rule-based system but with a notable improvement in the F1-score by ~14%. Conclusions: Our research highlights the transformative potential of LLM-based NLP algorithms in automating the extraction of treatment responses from free-text EHRs. This methodology not only provides a scalable and efficient mechanism for processing large volumes of clinical text but also significantly enhances the accuracy of lung cancer treatment response assessments. [Table: see text]

Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models.

Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and viruses. Several methods have emerged to pinpoint these influential entities in networks, represented as graphs. In these graphs, nodes correspond to individuals and edges indicate their connections. This study focuses on centrality measures, prized for their straightforwardness and effectiveness. We divide structural centrality into two categories: local, considering a node's immediate vicinity, and global, accounting for overarching path structures. Some techniques blend both centralities to highlight nodes influential at both micro and macro levels. Our paper presents a novel centrality measure, accentuating node degree and incorporating the network's broader features, especially paths of different lengths. Through Spearman and Pearson correlations tested on seven standard datasets, our method proves its merit against traditional centrality measures. Additionally, we employ the susceptible-infected-recovered (SIR) model, portraying virus spread, to further validate our approach. The ultimate influential node is gauged by its capacity to infect the most nodes during the SIR model's progression. Our results indicate a notable correlative efficacy across various real-world networks relative to other centrality metrics.

A software defect prediction method using binary gray wolf optimizer and machine learning algorithms

ContextSoftware defect prediction means finding defect-prone modules before the testing process which will reduce testing cost and time. Machine learning methods can provide valuable models for developers to classify software faulty modules. ProblemThe inherent problem of the classification is the large volume of the training dataset's features, which reduces the accuracy and precision of the classification results. The selection of the effective features of the training dataset for classification is an NP-hard problem that can be solved using heuristic algorithms. MethodIn this study, a binary version of the Gray Wolf optimizer (bGWO) was developed to select the most effective features of the training dataset. By selecting the most influential features in the classification, the precision and accuracy of the software module classifiers can be increased. ContributionDeveloping a binary version of the gray wolf optimization algorithm to optimally select the effective features and creating an effective defect predictor are the main contributions of this study. To evaluate the effectiveness of the proposed method, five real-world and standard datasets have been used for the training and testing stages of the classifier. ResultsThe results indicate that among the 21 features of the train datasets, the basic complexity, sum of operators and operands, lines of codes, number of lines containing code and comments, and sum of operands have the greatest effect in predicting software defects. In this research, by combining the bGWO method and machine learning algorithms, accuracy, precision, recall, and F1 criteria have been considerably increased.

Hindi-Handwritten-Character-Recognition using Deep learning

Abstract: Recognizing handwritten Hindi characters poses a significant challenge in the realms of machine learning and computer vision, particularly in the context of India's accelerating digitization. To address this, accurate and efficient algorithms are imperative for applications ranging from document analysis to postal automation and data entry. Leveraging the advancements in deep learning, we propose a novel approach to Hindi Handwritten Character Recognition. Our method employs a combination of Convolutional Neural Networks (CNNs) to extract image features and Recurrent Neural Networks (RNNs) to capture temporal dependencies within character sequences. Through rigorous evaluation on a standard benchmark dataset, our approach achieves state-of-the-art recognition accuracy. Furthermore, we validate its practical utility by successfully recognizing handwritten postal addresses on envelopes and other real-world applications. This research offers a promising solution to the challenges of Hindi Handwritten Character Recognition, with potential implications for advancing the digitization efforts not only in India but also in analogous regions.

A multifaceted architecture to Automate Essay Scoring for assessing english article writing: Integrating semantic, thematic, and linguistic representations

We propose a multifaceted architecture for automated essay scoring integrating semantic, thematic, and linguistic representations. Specifically, for the deep semantic content of the essay, we employ Convolutional Neural Network (ConvNet) and attention mechanism module to extract local semantic representation, and use ConvNet, Long Short-Term Memory network (LSTM), and attention mechanism module to capture the global semantic representation, providing the comprehensive deep semantic-facet representations. To answer the presence of prompt in the test essay, we utilize the Doc2Vec model to represent the target essay and the essay prompt as vectors and calculate their textual similarity as thematic-facet representations. In addition, we construct six categories of nine manual features, including grammatical errors, essay length, textual complexity, word complexity, sentence complexity, and the number of clauses, to capture the shallow linguistic representation that deep learning models may struggle to uncover. Finally, we integrate the multifaceted representation through the fully connected layer to automatically score essays. Experimental results on the standard public dataset ASAP and the more complex dataset CLC-FCE, which varies in standards, lengths, and topics, demonstrate that the proposed method effectively captures diverse aspects of textual representations. For the ASAP dataset, our method reduced the training time to 1862 s per essay and the inference time to 0.9778 s, achieving a 1.13 improvement in the Quadratic Weighted Kappa score over the previous best method. On the CLC-FCE dataset, our approach enhanced the Pearson's product-moment correlation coefficient and Spearman's rank correlation coefficient by 0.005 and 0.0337 respectively, surpassing prior benchmarks.

Graph Kernel Neural Networks.

The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is readily applicable to data such as images, which can be represented as regular grids in the Euclidean space, extending the convolution operator to work on graphs proves more challenging, due to their irregular structure. In this article, we propose to use graph kernels, i.e., kernel functions that compute an inner product on graphs, to extend the standard convolution operator to the graph domain. This allows us to define an entirely structural model that does not require computing the embedding of the input graph. Our architecture allows to plug-in any type of graph kernels and has the added benefit of providing some interpretability in terms of the structural masks that are learned during the training process, similar to what happens for convolutional masks in traditional convolutional neural networks (CNNs). We perform an extensive ablation study to investigate the model hyperparameters' impact and show that our model achieves competitive performance on standard graph classification and regression datasets.

Improvement of feature engineering on emotion detection from textual data

This paper introduces a new method for selecting terms in the field of emotion recognition from text. Instead of focusing solely on very common or very rare terms, this approach considers moderately frequent terms as well. The idea is that these moderately frequent terms might also contain important information for distinguishing between emotions. Compared to traditional methods like Chi-Square and Gini-Text, this new approach performs better in many cases. To represent documents, the bag-of-words approach is used, where each document is represented by a vector. In this vector, each selected term is given a weight of 1 if it appears in the document, and 0 if it does not. Importantly, this new method includes terms that are not selected by Chi-Square and Gini-Text. Experiments conducted on a standard dataset demonstrate that including moderately frequent terms improves the accuracy of emotion recognition. This improvement is evident in terms of accuracy scores.

Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells

Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.

Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA

Novel methods for sampling and characterizing biodiversity hold great promise for re-evaluating patterns of life across the planet. The sampling of airborne spores with a cyclone sampler, and the sequencing of their DNA, have been suggested as an efficient and well-calibrated tool for surveying fungal diversity across various environments. Here we present data originating from the Global Spore Sampling Project, comprising 2,768 samples collected during two years at 47 outdoor locations across the world. Each sample represents fungal DNA extracted from 24 m3 of air. We applied a conservative bioinformatics pipeline that filtered out sequences that did not show strong evidence of representing a fungal species. The pipeline yielded 27,954 species-level operational taxonomic units (OTUs). Each OTU is accompanied by a probabilistic taxonomic classification, validated through comparison with expert evaluations. To examine the potential of the data for ecological analyses, we partitioned the variation in species distributions into spatial and seasonal components, showing a strong effect of the annual mean temperature on community composition.

MFPINC: prediction of plant ncRNAs based on multi-source feature fusion

Non-coding RNAs (ncRNAs) are recognized as pivotal players in the regulation of essential physiological processes such as nutrient homeostasis, development, and stress responses in plants. Common methods for predicting ncRNAs are susceptible to significant effects of experimental conditions and computational methods, resulting in the need for significant investment of time and resources. Therefore, we constructed an ncRNA predictor(MFPINC), to predict potential ncRNA in plants which is based on the PINC tool proposed by our previous studies. Specifically, sequence features were carefully refined using variance thresholding and F-test methods, while deep features were extracted and feature fusion were performed by applying the GRU model. The comprehensive evaluation of multiple standard datasets shows that MFPINC not only achieves more comprehensive and accurate identification of gene sequences, but also significantly improves the expressive and generalization performance of the model, and MFPINC significantly outperforms the existing competing methods in ncRNA identification. In addition, it is worth mentioning that our tool can also be found on Github (https://github.com/Zhenj-Nie/MFPINC) the data and source code can also be downloaded for free.

A Novel Source Code Representation Approach Based on Multi-Head Attention

Code classification and code clone detection are crucial for understanding and maintaining large software systems. Although deep learning surpasses traditional techniques in capturing the features of source code, existing models suffer from low processing power and high complexity. We propose a novel source code representation method based on the multi-head attention mechanism (SCRMHA). SCRMHA captures the vector representation of entire code segments, enabling it to focus on different positions of the input sequence, capture richer semantic information, and simultaneously process different aspects and relationships of the sequence. Moreover, it can calculate multiple attention heads in parallel, speeding up the computational process. We evaluate SCRMHA on both the standard dataset and an actual industrial dataset, and analyze the differences between these two datasets. Experiment results in code classification and clone detection tasks show that SCRMHA consumes less time and reduces complexity by about one-third compared with traditional source code feature representation methods. The results demonstrate that SCRMHA reduces the computational complexity and time consumption of the model while maintaining accuracy.

Experimental study on short-text clustering using transformer-based semantic similarity measure

Sentence clustering plays a central role in various text-processing activities and has received extensive attention for measuring semantic similarity between compared sentences. However, relatively little focus has been placed on evaluating clustering performance using available similarity measures that adopt low-dimensional continuous representations. Such representations are crucial in domains like sentence clustering, where traditional word co-occurrence representations often achieve poor results when clustering semantically similar sentences that share no common words. This article presents a new implementation that incorporates a sentence similarity measure based on the notion of embedding representation for evaluating the performance of three types of text clustering methods: partitional clustering, hierarchical clustering, and fuzzy clustering, on standard textual datasets. This measure derives its semantic information from pre-training models designed to simulate human knowledge about words in natural language. The article also compares the performance of the used similarity measure by training it on two state-of-the-art pre-training models to investigate which yields better results. We argue that the superior performance of the selected clustering methods stems from their more effective use of the semantic information offered by this embedding-based similarity measure. Furthermore, we use hierarchical clustering, the best-performing method, for a text summarization task and report the results. The implementation in this article demonstrates that incorporating the sentence embedding measure leads to significantly improved performance in both text clustering and text summarization tasks.

Leveraging Data Locality in Quantum Convolutional Classifiers.

Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and possess a simpler structure than a fully connected multi-layer perceptrons (MLPs) without compromising the accuracy of classification. However, the concept of preserving data locality is usually overlooked in the existing quantum counterparts of CNNs, particularly for extracting multifeatures in multidimensional data. In this paper, we present an multidimensional quantum convolutional classifier (MQCC) that performs multidimensional and multifeature quantum convolution with average and Euclidean pooling, thus adapting the CNN structure to a variational quantum algorithm (VQA). The experimental work was conducted using multidimensional data to validate the correctness and demonstrate the scalability of the proposed method utilizing both noisy and noise-free quantum simulations. We evaluated the MQCC model with reference to reported work on state-of-the-art quantum simulators from IBM Quantum and Xanadu using a variety of standard ML datasets. The experimental results show the favorable characteristics of our proposed techniques compared with existing work with respect to a number of quantitative metrics, such as the number of training parameters, cross-entropy loss, classification accuracy, circuit depth, and quantum gate count.