There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid ...
There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National Center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images.
In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potent...
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Within observational datascience workloads, Berkson's paradox can lead to false causal inferences. One of the prominent quasi-experimental methods to mitigate this selection bias is Propensity Score Matching (PSM...
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ISBN:
(数字)9798331539603
ISBN:
(纸本)9798331539610
Within observational datascience workloads, Berkson's paradox can lead to false causal inferences. One of the prominent quasi-experimental methods to mitigate this selection bias is Propensity Score Matching (PSM). An approach called Neural PSM (NPSM) was developed to overcome the drawbacks of conventional regression-based PSM, including its limited flexibility to model high-dimensional data and non-linear relationships that could cause imperfect covariate balance. In this study, a three-layer depth of Deep Neural Networks was designed to estimate propensity scores and finally balance both control and treatment groups of the Groupon dataset. An unsupervised k-Nearest Neighbor algorithm then helped the model to efficiently detect and cluster similar matching points. From the five salient features presented, NPSM successfully achieved lower differences in Cohen's d effect size, i.e., 0.313 for coupon duration, 0.017 for promotion length, 0.425 for quantity sold, -0.199 for limited supply, and 0.395 for Facebook likes. While these results mostly outperformed Linear Regression (LR) and Random Forest (RF) models, further evaluation is needed to verify the true effectiveness of NPSM in mitigating Berkson's paradox in broader e-commerce contexts.
In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inferen...
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ISBN:
(数字)9798350390025
ISBN:
(纸本)9798350390032
In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inference latency. While there are many prior available approaches to solve this problem, most of them are inherently complex. This paper introduced a Knowledge Graph-enhanced Semantic Cache mechanism as an alternative, lightweight technique to boost retrieval for similar prompts. The latest state-of-the-art open-source LLM, named Google's Gemma-2B-it, was used to generate sample prompts and responses as a draft, while a knowledge graph (KG) was built from Wikipedia sentences. To create embeddings of prompts and KG, all-MiniLM-L6-v2 from SentenceTransformer was used. This new cache system resulted in up to 28% improvement over a standard model. In particular, reinforcement with KG cache embeddings yielded more than 85% semantic cache accuracy. To map the next trajectory of this pilot study, an overview of the extended framework for LLM knowledge management was also presented in this paper. The framework includes the new KG- enhanced cache system equipped with scalable security and fallback mechanisms that can promote green technology through substantial improvements in latency, throughput, and overall LLM costs.
Single-nucleotide polymorphism (SNP) analysis has become a pivotal strategy for drug discovery within bioinformatics, especially for incurable diseases like cancer. With the increasing number of researchers starting t...
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ISBN:
(数字)9798331539603
ISBN:
(纸本)9798331539610
Single-nucleotide polymorphism (SNP) analysis has become a pivotal strategy for drug discovery within bioinformatics, especially for incurable diseases like cancer. With the increasing number of researchers starting to embrace metaheuristic methods, a review was done by gathering papers from the Google Scholar database from 2018 to 2023, which resulted in 20 papers after title, abstract, and content filtering. The findings show that the Genetic Algorithm and the Harmony Search Algorithm have become popular approaches in SNP analysis, particularly in studies on breast cancer, age-related macular degeneration, and colorectal cancer. However, the review shows that while researchers have proven most methods effective in finding disease-related SNPs, a more measurable study in SNP analysis is needed, due to the lack of elaboration on measurement metrics in the found studies.
We propose a method to statistically analyze rates obtained from count data in spatio-temporal terms, allowing for regional and temporal comparisons. Generalized fused Lasso Poisson model is used to estimate the spati...
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Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model’s training distribution to prevent potentially unsafe actions....
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ISBN:
(数字)9798350387957
ISBN:
(纸本)9798350387964
Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model’s training distribution to prevent potentially unsafe actions. However, OOD detectors are often implemented using deep neural networks, which makes it difficult to meet real-time deadlines on embedded systems with memory and power constraints. We consider the class of variational autoencoder (VAE) based OOD detectors where OOD detection is performed in latent space, and apply quantization, pruning, and knowledge distillation. These techniques have been explored for other deep models, but no work has considered their combined effect on latent space OOD detection. While these techniques increase the VAE’s test loss, this does not correspond to a proportional decrease in OOD detection performance and we leverage this to develop lean OOD detectors capable of real-time inference on embedded CPUs and GPUs. We propose a design methodology that combines all three compression techniques and yields a significant decrease in memory and execution time while maintaining AUROC for a given OOD detector. We demonstrate this methodology with two existing OOD detectors on a Jetson Nano and reduce GPU and CPU inference time by 20% and 28% respectively while keeping AUROC within 5% of the baseline.
At the moment, weather data is crucial for supporting neighborhood activities. The economy and trade are both centered in Jakarta, which is also Indonesia's capital. Therefore, it is crucial to have access to weat...
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A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based o...
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ISBN:
(数字)9798331519643
ISBN:
(纸本)9798331519650
A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based on a combination of fuzzy logic and object-oriented methods to predict sugarcane yield. The research is conducted in four main stages, employing object-oriented methods for model design and fuzzy logic for model construction. Object and activity diagrams are used for the object-oriented model design. The fuzzy membership functions employed are a combination of trapezoidal and triangular shapes. The resulting decision model can simulate 2,225 data from plantation areas in Indonesia. Based on the 10 examples of plantation area data in Indonesia, plantation number one obtained the largest sugarcane yield, which was 4.79%, with a similarity value of 0.90 (when compared to manual calculations as its ground truth). This similarity value is a higher value when compared to the average similarity value, which is 0.89.
Push notifications efficiently deliver real-time messages, boosting user engagement and website traffic. However, users often passively receive notifications without active interaction in recommendation contexts. Cons...
Push notifications efficiently deliver real-time messages, boosting user engagement and website traffic. However, users often passively receive notifications without active interaction in recommendation contexts. Consequently, for precise recommendations, Click-Through Rate (CTR) prediction for push notifications requires addressing challenges such as user temporal and contextual preferences, the dynamic nature of user click behavior, and limited interactions between users and items. We propose Push4Rec, a novel push notification recommendation model designed explicitly for news articles. Push4Rec integrates pivotal learners to extract information adeptly. It assesses click behavior, captures preferences, and comprehends trends’ influence. A fusion function and gating network ensure versatile extraction of user click preferences. We assessed Push4Rec using a real-world push notification dataset from our partnering company. Push4Rec outperformed benchmark models, delivering state-of-the-art results across all evaluation metrics. Thus, we believe that Push4Rec, with its novel approach, sets a new standard in push notification services, driving forward the field of personalized recommendation systems.
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