Performance Evaluation Metrics Research Articles

Optimization of Performance in Cloud Data Streaming: Comprehensive Review

With the proliferation of cloud computing, the landscape of data processing has undergone a transformative shift. Cloud data streaming, a linchpin of real-time data processing, has emerged as a critical enabler for organizations seeking timely insights and informed decision-making. However, optimizing the performance of cloud data streaming systems poses intricate challenges that necessitate exploration. This comprehensive review article navigates the multifaceted terrain of enhancing performance in cloud data streaming. It encompasses foundational concepts, performance evaluation metrics, prevalent challenges, advanced optimization strategies, illustrative case studies, comparative analysis of platforms, anticipatory trends, and directions for further research.

Multi-dimensional fusion: transformer and GANs-based multimodal audiovisual perception robot for musical performance art.

In the context of evolving societal preferences for deeper emotional connections in art, this paper explores the emergence of multimodal robot music performance art. It investigates the fusion of music and motion in robot performances to enhance expressiveness and emotional impact. The study employs Transformer models to combine audio and video signals, enabling robots to better understand music's rhythm, melody, and emotional content. Generative Adversarial Networks (GANs) are utilized to create lifelike visual performances synchronized with music, bridging auditory and visual perception. Multimodal reinforcement learning is employed to achieve harmonious alignment between sound and motion. The study leverages Transformer models to process audio and video signals in robot performances. Generative Adversarial Networks are employed to generate visually appealing performances that align with the musical input. Multimodal reinforcement learning is used to synchronize robot actions with music. Diverse music styles and emotions are considered in the experiments. Performance evaluation metrics include accuracy, recall rate, and F1 score. The proposed approach yields promising results across various music styles and emotional contexts. Performance smoothness scores exceed 94 points, demonstrating the fluidity of robot actions. An accuracy rate of 95% highlights the precision of the system in aligning robot actions with music. Notably, there is a substantial 33% enhancement in performance recall rate compared to baseline modules. The collective improvement in F1 score emphasizes the advantages of the proposed approach in the realm of robot music performance art. The study's findings demonstrate the potential of multimodal robot music performance art in achieving heightened emotional impact. By combining audio and visual cues, robots can better interpret and respond to music, resulting in smoother and more precise performances. The substantial improvement in recall rate suggests that the proposed approach enhances the robots' ability to accurately mirror the emotional nuances of the music. These results signify the potential of this approach to transform the landscape of artistic expression through robotics, opening new avenues for emotionally resonant performances.

Prediction of automobile insurance fraud claims using machine learning

Automobile insurance fraud is a significant issue for insurance firms, causing financial losses and higher premiums for policyholders. This study aims to create a predictive model for accurately identifying potential vehicle insurance fraud claims. Understanding fraud detection processes and operationalizing information communication technology is crucial for implementing corrective actions, but personally reviewing insurance claims is time-consuming and costly. This study explored machine learning algorithms to detect fraudulent vehicle insurance claims. The research evaluated AdaBoost, XGboostNB, SVM, LR, DT, ANN, and RF. AdaBoost and XGBoost classifiers outperformed other models with 84.5% classification accuracy, while LR classifiers performed poorly with balanced and unbalanced data. The ANN classifier performed better with unbalanced data. Performance evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of the models. The results demonstrate the effectiveness of machine learning in distinguishing between genuine and fraudulent claims, providing insurance companies with a powerful tool to proactively combat fraud and improve their overall risk management strategies. The findings of this research contribute to the insurance industry’s efforts to enhance fraud detection systems, reduce financial losses, and offer more competitive insurance premiums to honest policyholders.

DeepHistoNet: A robust deep-learning model for the classification of hepatocellular, lung, and colon carcinoma.

In recent days, non-communicable diseases (NCDs) require more attention since they require specialized infrastructure for treatment. As per the cancer population registry estimate, nearly 800,000 new cancer cases will be detected yearly. The statistics alarm the need for early cancer detection and diagnosis. Cancer identification can be made either through manual efforts or by computer-aided algorithms. Manual efforts-based cancer detection is labor intensive and also offers more time complexity. In contrast, computer-aided algorithms offer feasibility in reducing time and manual efforts. With the motivation to develop a computer-aided diagnosis system for NCD, we developed a cancer detection methodology. In the present article, a deep learning (DL)-based cancer identification model is developed. In DL-based architectures, the features are generally extracted using convolutional neural networks. The proposed attention-guided, densely connected residual, and dilated convolution deep neural network called DeepHistoNet acquire precise patterns for classification. Experimentation has been carried out on Kasturba Medical College (KMC), TCGA-LIHC, and LC25000 datasets to prove the robustness of the model. Performance evaluation metrics like F1-score, sensitivity, specificity, recall, and accuracy validate the experimentation. Experimental results demonstrate that the proposed DeepHistoNet model outperforms the other state-of-the-art methods. The proposed model has been able to classify the KMC liver dataset with 97.1% accuracy and 0.9867 value of area under the curve-receiver operating characteristic curve (AUC-ROC), which is the best result obtained compared to the state-of-the-art techniques. The performance of the DeepHistoNet has been even better on the LC25000 dataset. On the LC25000 dataset, the proposed model achieved 99.8% classification accuracy. To our knowledge, DeepHistoNet is a novel approach for multiple histopathological image classification. RESEARCH HIGHLIGHTS: A novel robust DL model is proposed for histopathological image carcinoma classification. The precise patterns for accurate classification are extracted using dense cross-connected residual blocks. Spatial attention is provided to the network so that the spatial information is not lost during the feature extraction. DeepHistoNet is trained and evaluated on the liver, lung, and colon histopathology datasets to demonstrate its resilience. The results are promising and outperform state-of-the-art techniques. The proposed methodology has obtained the AUC-ROC value of 0.9867 with a classification accuracy of 97.1% on the KMC dataset. The proposed DeepHistoNet has classified the LC25000 dataset with 99.8% accuracy. The results are the best obtained till date.

Hybrid coordination scheme based on fuzzy inference mechanism for residential charging of electric vehicles

The charging of electric vehicles (EVs) at residential premises is orchestrated through either centralized or decentralized control mechanisms. The former emphasizes adherence to power grid constraints, employing demand management techniques to restrict EV charging when the aggregated demand exceeds a predetermined threshold, which may result in user discontentment. Conversely, the latter endows EV users with the authority to self-regulate their charging behavior to optimize cost, allowing a multitude of interconnected EVs to charge during the same off-peak window. However, this decentralized approach gives rise to the herding problem, wherein a simultaneous surge in EV charging during off-peak periods burdens the power grid, leading to potential system overloads. This paper presents a hybrid coordinating scheme that integrates a fuzzy inference mechanism to synergistically blend the merits of centralized and decentralized coordinations. The proposed hybrid coordination scheme aims to minimize peak load, alleviate herding, and optimize charging costs while ensuring adherence to EV users’ charging obligations at the lowest feasible expense. The problem is formulated with the introduction of a novel fuzzy objective function and subsequently resolved through the fuzzy inference mechanism. The fuzzy inference encapsulates independent and uncertain price profiles, consumption load patterns, and state-of-charge data collected from the power grid, households, and EV domains, which are effectively integrated into weighted variables for the requesting EVs. The proposed hybrid coordinating scheme leverages weighted variables to optimize the objective function, enabling the determination of an optimal charging schedule that satisfies the charging requirements of the requesting EVs, while adhering to stringent power grid operational constraints and minimizing charging costs. To assess the efficacy of the hybrid coordination scheme, we conducted two meticulous case studies employing the IEEE 34 bus system as a testbed, thoroughly evaluating performance metrics encompassing charging cost, load profile impact, and peak-to-average ratio. The results demonstrate the superior performance of the proposed hybrid coordination scheme compared to alternative charging strategies, including uncoordinated charging, standard-rate charging, time-of-use charging, and two-layer decentralized approaches.

An Efficient Machine Learning-Based Cluster Analysis Mechanism for IoT Data

The prevailing developments in internet of things (IoT) and other sensor technologies such as cyber physical systems (CPS) and wireless sensor networks (WSNs), the huge amount of sensor data has been generating from various IoT devices and protocols. Making predictions and finding density patterns over such data is a challenging task. In order to find the density patterns and make analysis over real-time dynamic data, the machine learning (ML) based algorithms are widely used to deal with the IoT data. In this article, the authors proposed an efficient ML-based cluster analysis mechanism for finding density patterns in IoT dynamic data effectively. In this proposed mechanism, the k-means and GMM models are used for clustering data analysis. The proposed mechanism has been implemented on ThingSpeak Cloud platform for analysing the data efficiently on daily and weekly basis. Finally, the proposed mechanism acquired superior results than the existing benchmarked mechanisms over all the performance evaluation metrics used for analysis over IoT dynamic data.

Direct Ink Writing 3D Printing for High-Performance Electrochemical Energy Storage Devices: A Minireview.

Despite tremendous efforts that have been dedicated to high-performance electrochemical energy storage devices (EESDs), traditional electrode fabrication processes still face the daunting challenge of limited energy/power density or compromised mechanical compliance. 3D thick electrodes can maximize the utilization of z-axis space to enhance the energy density of EESDs but still suffer from limitations in terms of poor mechanical stability and sluggish electron/ion transport. Direct ink writing (DIW), an eminent branch of 3D printing technology, has gained popularity in the manufacture of 3D electrodes with intricately designed architectures and rationally regulated porosity, promoting a triple boost in areal mass loading, ion diffusion kinetics, and mechanical flexibility. This focus review highlights the fundamentals of printable inks and typical configurations of 3D-printed devices. In particular, preparation strategies for high-performance and multifunctional 3D-printed EESDs are systemically discussed and classified according to performance evaluation metrics such as high areal energy density, high power density, high volumetric energy density, and mechanical flexibility. Challenges and prospects for the fabrication of high-performance 3D-printed EESDs are outlined, aiming to provide valuable insights into this thriving field.

A blockchain-enabled, trust and location dependent - Privacy preserving system in VANET

Intelligent Transportation System (ITS) heavily relies on the unique mobile ad-hoc network (MANET) known as the vehicular ad-hoc network (VANET). The convenience that the Location Based Service (LBS), security issues arising from VANETs' great portability. Among the most widely used privacy-preserving techniques, distributed k-anonymity does not consider users' reliability, which results in hostile vehicle tracking. Therefore, Blockchain-enabled, Trust and Location dependent-Privacy Preserving (BTLB-PP) authentication system in VANET to overcome issues. Trust Management (TM) based on Dirichlet distribution, client and member will only collaborate with vehicles they confide by explicitly examining various prerequisites of claimed vehicle and collaborative vehicle during construction of unidentified obfuscation region and integrating attributes of these two functions. Using blockchain, a data structure has been proposed to promptly register trust of vehicles on publicly available blocks, allowing any vehicle to retrieve. In the trials, tests on various datasets have been run. Suggested system is resistant to multiple trust model threats, improving the security of vehicles' confidentiality and privacy. The performance evaluation metrics are precision, recall, f-measure and false positive rate (FPR) are used to evaluate the proposed system. Results from the simulation show that the proposed method is efficient and practical in reality. The suggested method was tested in a simulated traffic situation to verify its effectiveness.

Hybrid machine learning-based model for predicting chloride ion concentration in coral aggregate concrete and its ethically aligned graphical user interface design

The chloride ion concentration distribution in coral aggregate concrete (CAC-CC) holds significant importance for evaluating CAC performance, assessing rebar corrosion, and understanding chloride ion diffusion. Thus, this study employs hybrid machine learning algorithms to establish predictive models for CAC-CC under three environmental conditions (salt spray zone, tidal zone, and underwater zone) and evaluates the accuracy and generalization ability of these models. The results demonstrate that the genetic algorithm-optimized support vector regression (GA-SVR) model provides predictions closer to the actual values, exhibiting smoother error fluctuations. The GA-SVR model also exhibits smaller mean and standard deviation in the residual distribution. Furthermore, the GA-SVR model excels in five performance evaluation metrics, with R2, MAE, MAPE, MSE, and RMSE values of 0.986, 1.25e-2, 3.24e-2, 0.276e-3, and 1.66e-2, respectively. Hence, the GA-SVR model emerges as the optimal choice for CAC-CC prediction. Additionally, this work utilizes Shapley Additive Explanations (SHAP) to assess the contribution of eight features. The analysis reveals that the water-binder ratio and the pre-wetted water to total water ratio are the two most critical features. Moreover, an increase in the pre-wetted water to total water ratio and a decrease in the water-cement ratio hinder chloride ion diffusion. Mechanistic analysis of the feature contribution to CAC-CC is further explored in this study. Based on the GA-SVR model, a graphical user interface for CAC-CC has been developed, enabling visualization of CAC-CC predictions. This research introduces a novel approach to optimize CAC performance and predict chloride ion concentration, laying the foundation for CAC application in reef construction.

Categorization of Select Cockpit Performance Evaluation Techniques.

INTRODUCTION: The modern aircraft cockpit has evolved into a complex system of systems. Numerous performance evaluation metrics and techniques exist that can measure the effectiveness of cockpit components in terms of how they influence the human operator's ability to perform tasks relevant to mission success. As no prior review of these metrics has been found in the literature, this effort attempts to do so, albeit without applying the metrics to a novel cockpit evaluation.METHODS: These metrics and techniques are discussed and presented in five defined categories as they relate to evaluating cockpit subsystems: ergonomics and anthropometrics; human-computer interaction; data management and presentation; crew resource management and operations; and ingress and egress.DISCUSSION: While this effort is significant and novel, it is not necessarily comprehensive. In conclusion, it is noted that no single holistic quantitative metric to evaluate cockpit design and performance yet exists. Utilizing some of the preexisting metrics presented to develop such a metric would be beneficial in efforts to evaluate aircraft cockpit designs and performance, as well as aiding future cockpit designs.Brighton EM, Klaus DM. Categorization of select cockpit performance evaluation techniques. Aerosp Med Hum Perform. 2023; 94(9):696-704.

A Sustainable Price Prediction Model for Airbnb Listings Using Machine Learning and Sentiment Analysis

Since 2008, the company Airbnb has brought significant changes to the hospitality industry worldwide. Experiencing remarkable growth, it currently offers over six million listings in 191 countries across one hundred thousand cities. Airbnb has gained immense popularity among travellers seeking accommodations globally. Consequently, Airbnb generates extensive datasets from its listings that contain rich features that have captured the attention of researchers. These datasets offer potentially valuable information that can be extracted to greatly assist individuals and governments in making more informed decisions. Pricing rental properties on Airbnb still presents a challenge for owners, as it directly impacts customer demand. This research aimed to conquer the challenge by developing a sustainable price prediction model for Airbnb listings by incorporating property specifications, owner information and customer reviews. By utilising this model, owners can estimate the expected value of their Airbnb listings. We trained and fine-tuned several machine learning models using an Airbnb listing dataset from Barcelona. Performance evaluation metrics, such as mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE) and R2 score were then used to compare the models. To enhance the performance of the predictive models, sentiment analysis was used to extract relevant features from customer reviews. Feature importance analysis was also conducted to determine which attributes were the most influential on listing price predictions. The results show that the Lasso and Ridge models outperformed the others considered in the study, with an average R2 score of 99%. We found that amenities-related features had a negligible impact on all models’ performance. The most significant features found were polarity (positive/negative sentiment), the number of bedrooms, the accommodation’s maximum capacity, the number of beds and the quantity of reviews received by the listing in the past 12 months, respectively. We found that certain room types (categorized as entire home/apartment, private room or shared room) are associated with lower predicted prices.

Securing Industrial Internet of Things Against Botnet Attacks Using Hybrid Deep Learning Approach

Industrial Internet of Things (IIoT) formation of richer ecosystem of intelligent interconnected devices while enabling new levels of digital innovation has essentially transformed and revolutionized global manufacturing and industry 4.0. Conversely, the prevalent distributed nature of IIoT, Industrial 5G, underlying IoT sensing devices, IT/OT convergence, Edge Computing, and Time Sensitive Networking makes it an impressive and potential target for cyber-attackers. Multi-variant persistent and sophisticated bot attacks are considered catastrophic for connects IIoTs. Besides, botnet attack detection is extremely complex and decisive. Thus, efficient and timely detection of IIoT botnets is a dire need of the day. We propose a hybrid intelligent Deep Learning (DL)-enabled mechanism to secure IIoT infrastructure from lethal and sophisticated multi-variant botnet attacks. The proposed mechanism has been rigorously evaluated with latest available dataset, standard and extended performance evaluation metrics, and current DL benchmark algorithms. Besides,cross validation of our results are also performed to clearly show overall performance. The proposed mechanisms outperforms in identifying accurately multi-variant sophisticated bot attacks. Besides, our proposed technique also show promising results in terms of speed efficiency.

Real-Time Identification of Pancreatic Cancer Cases Using Artificial Intelligence Developed on Danish Nationwide Registry Data.

Pancreatic cancer is expected to be the second leading cause of cancer-related deaths worldwide within few years. Most patients are not diagnosed in time for curative-intent treatment. Accelerating the time of diagnosis is a key component of reducing pancreatic cancer mortality. We developed and tested a dynamic algorithm aiming at proactively identifying patients with a substantially elevated risk of having undiagnosed pancreatic cancer. Machine learning methodology was applied to a live stream of nationwide Danish registry data. A hybrid case-control and prospective cohort design relying on incidence density sampling was used. Three models with minimal tuning were tested. All performance evaluation metrics were based on out-of-sample, out-of-time data in a monthly walk-forward strategy to avoid any temporal biases or inflation of performance metrics. Outcome was a diagnosis of pancreatic cancer. Subgroups identified had a 10.1% risk of being diagnosed with pancreatic cancer within 1 year, corresponding to a number needed to screen of 9.9. When considering competing, potentially computed tomography-detectable GI cancers, this number is reduced to 5.7. The time of diagnosis can be accelerated by up to 142 days. Currently available nationwide live data and computational resources are sufficient for real-time identification of individuals with at least 10.1% risk of having undiagnosed pancreatic cancer and 17.7% risk of any GI cancer in the Danish population. For prospective identification of high-risk patients, the area under the curve is not a useful indication of the positive predictive values achieved. Viable design solutions are demonstrated, which address the main shortfalls of the existing cancer prediction efforts in relation to temporal biases, leaks, and performance metric inflation. Efficacy evaluations with resection rates and mortality as end points are needed.

Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey

With the rapid development of sensor technologies and the widespread use of laser scanning equipment, point clouds, as the main data form and an important information carrier for 3D scene analysis and understanding, play an essential role in the realization of national strategic needs, such as traffic scene perception, natural resource management, and forest biomass carbon stock estimation. As an important research direction in 3D computer vision, point cloud semantic segmentation has attracted more and more researchers’ attention. In this paper, we systematically outline the main research problems and related research methods in point cloud semantic segmentation and summarize the mainstream public datasets and common performance evaluation metrics. Point cloud semantic segmentation methods are classified into rule-based methods and point-based methods according to the representation of the input data. On this basis, the core ideas of each type of segmentation method are introduced, the representative and innovative algorithms of each type of method are elaborated, and the experimental results on the datasets are compared and analyzed. Finally, some promising research directions and potential tendencies are proposed.

A novel approach for image-based olive leaf diseases classification using a deep hybrid model

The olive tree is affected by a variety of diseases. To identify these diseases, many farmers typically use traditional methods that require a lot of effort and specialization. These methods include visually observing the tree or conducting tests in a laboratory. Fortunately, recent progress in machine learning (ML) and deep learning (DL) has demonstrated promising potential to automatically classify diseases with both high accuracy and speed. However, as indicated by the literature, only a few studies are utilizing ML and DL techniques for identifying and categorizing diseases that affect olive trees. Therefore, in this study, we collected a dataset containing 4138 images of olive leaves from various sources. The dataset comprises four categories: three representing diseases and one denoting a healthy category. We also introduced an innovative approach to classify olive leaf diseases by combining deep learning architectures, specifically convolutional neural networks (CNNs), with machine learning classifiers. In this approach, we developed a total of 30 distinct deep hybrid models (DHMs), utilizing six pre-trained convolutional neural network architectures (VGG19, ResNet50, MobileNetV2, InceptionV3, DenseNet201, and EfficientNetB0) as feature extractors, along with five machine learning classifiers (MLP, LR, RF, SVM, and DT). To assess the performance of the DHMs, we used performance evaluation metrics (Accuracy, Precision, Recall, F1-score) and we conducted an assessment to validate the reliability rating of the DHMs using a cross-validation technique. Additionally, we employed the Non-Parametric ScottKnott ESD (NPSK) test to assess the ranking of the best DHMs. The study’s findings revealed that the most efficient deep hybrid model was achieved by using the EfficientNetB0 model in combination with a logistic regression classifier, achieving an impressive accuracy score of 96.14%. Our approach has the potential to significantly assist olive farmers in rapidly and accurately identifying diseases, thereby potentially reducing economic losses.

Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images.

Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might mislead radiologists and health experts when classifying chest diseases. Chest X-rays (CXR), cough sounds, and computed tomography (CT) scans are utilized by researchers and doctors to identify chest diseases such as LC, COVID-19, PNEU, and TB. The objective of the work is to identify nine different types of chest diseases, including COVID-19, edema (EDE), LC, PNEU, pneumothorax (PNEUTH), normal, atelectasis (ATE), and consolidation lung (COL). Therefore, we designed a novel deep learning (DL)-based chest disease detection network (DCDD_Net) that uses a CXR, CT scans, and cough sound images for the identification of nine different types of chest diseases. The scalogram method is used to convert the cough sounds into an image. Before training the proposed DCDD_Net model, the borderline (BL) SMOTE is applied to balance the CXR, CT scans, and cough sound images of nine chest diseases. The proposed DCDD_Net model is trained and evaluated on 20 publicly available benchmark chest disease datasets of CXR, CT scan, and cough sound images. The classification performance of the DCDD_Net is compared with four baseline models, i.e., InceptionResNet-V2, EfficientNet-B0, DenseNet-201, and Xception, as well as state-of-the-art (SOTA) classifiers. The DCDD_Net achieved an accuracy of 96.67%, a precision of 96.82%, a recall of 95.76%, an F1-score of 95.61%, and an area under the curve (AUC) of 99.43%. The results reveal that DCDD_Net outperformed the other four baseline models in terms of many performance evaluation metrics. Thus, the proposed DCDD_Net model can provide significant assistance to radiologists and medical experts. Additionally, the proposed model was also shown to be resilient by statistical evaluations of the datasets using McNemar and ANOVA tests.

Relative informative power and stock return predictability: a new perspective from Egypt

Purpose This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms. Design/methodology/approach The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model. Findings The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners. Practical implications This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies. Originality/value In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

An evaluation of the organ procurement and transplantation network's expanded post-transplant performance metrics.

On July 14, 2022, the Organ Procurement and Transplantation Network's (OPTN) Membership and Professional Standards Committee (MPSC) approved bylaws including two new post-transplant performance evaluation metrics, the 90-day (90D) and 1-year conditional on the 90-day (1YC90D) graft survival hazard ratio (HR). These metrics have replaced the previous 1-year (1Y) unconditional, post-transplant graft survival HR and are used to nationally rank and identify programs for MPSC review. The MPSC's policies have major implications for all transplant programs, providers, and patients across the United States. Herein we show two significant limitations with the new evaluation criteria, arbitrary censoring periods and interdependence in the new performance metrics. We have demonstrated a strong and consistent inverse correlation between the new evaluation metrics, thus proving a lack of independence. Moreover, these two evaluation criteria are interdependent even at nominal HRs. Thus, the 90D cohort can be used to accurately predict whether the 1YC90D is above or below a given HR threshold. This could alter practice behaviors and the timing of patient event reporting, which may result in many unintended consequences related to clinical practice. Here we provide the first evidence that this new evaluation system will lead to a significant increase in the number of programs flagged for MPSC review. When this occurs, the cost of operating a transplant program will increase without a clear demonstration of an increased accuracy in identifying problematic programs.

Recognizing sounds of Red Palm Weevils (RPW) based on the VGGish model: Transfer learning methodology

The red palm weevil (RPW) is one of the most popular and damaging pests which threaten palm cultivation and production in many countries in the world. Recognizing RPW pests is not an easy task as it is difficult to detect the RPW insect in the early stages of infestation, because there are not many visible external signs on the palm trees, and once the tree is infested it is too late to save it. In response to this urgent problem, a proposed transfer learning methodology is developed to recognize RPW pests using the VGGish deep learning model based on the acoustic signal features of RPW pests sounds. The proposed model has been trained and validated using RPW sound samples taken from RPW public benchmarked dataset. The RPW dataset consists of 2485 audio samples divided into 1754 healthy samples and 731 infested samples. The validation results clarified the efficiency of the proposed transfer learning model in recognizing RPW pest' sounds based on the amplitude and phase features of the acoustic signals. The performance evaluation metrics showed promising results in detecting the RPW pest's sounds, where it achieved an accuracy of 94.5%, precision of 93%, recall of 95%, and F1-score of 94%. To validate the efficiency of the proposed VGGish model, four practical comparisons with four machine learning models (i.e. Support vector machine (SVM), decision tree (DT), Gaussian naïve Bayes (NB), and Logistic regression (LR)) and five practical comparisons with five deep learning models (i.e. InceptionResV3, ResNet50, DenseNet121, EfficientNetB0, and Xception) are conducted using the same dataset. The comparison results confirmed the superiority of the VGGish model in recognizing RPW pest's sounds compared to the four and five machine and deep learning models respectively.

A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics.

There are millions of people who lose their life due to several types of fatal diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol consumption, infections, ultraviolet radiation, smoking, and unhealthy lifestyles. Cancer is abnormal and uncontrolled tissue growth inside the body which may be spread to other body parts other than where it has originated. Hence it is very much required to diagnose the cancer at an early stage to provide correct and timely treatment. Also, manual diagnosis and diagnostic error may cause of the death of many patients hence much research are going on for the automatic and accurate detection of cancer at early stage. In this paper, we have done the comparative analysis of the diagnosis and recent advancement for the detection of various cancer types using traditional machine learning (ML) and deep learning (DL) models. In this study, we have included four types of cancers, brain, lung, skin, and breast and their detection using ML and DL techniques. In extensive review we have included a total of 130 pieces of literature among which 56 are of ML-based and 74 are from DL-based cancer detection techniques. Only the peer reviewed research papers published in the recent 5-year span (2018-2023) have been included for the analysis based on the parameters, year of publication, feature utilized, best model, dataset/images utilized, and best accuracy. We have reviewed ML and DL-based techniques for cancer detection separately and included accuracy as the performance evaluation metrics to maintain the homogeneity while verifying the classifier efficiency. Among all the reviewed literatures, DL techniques achieved the highest accuracy of 100%, while ML techniques achieved 99.89%. The lowest accuracy achieved using DL and ML approaches were 70% and 75.48%, respectively. The difference in accuracy between the highest and lowest performing models is about 28.8% for skin cancer detection. In addition, the key findings, and challenges for each type of cancer detection using ML and DL techniques have been presented. The comparative analysis between the best performing and worst performing models, along with overall key findings and challenges, has been provided for future research purposes. Although the analysis is based on accuracy as the performance metric and various parameters, the results demonstrate a significant scope for improvement in classification efficiency. The paper concludes that both ML and DL techniques hold promise in the early detection of various cancer types. However, the study identifies specific challenges that need to be addressed for the widespread implementation of these techniques in clinical settings. The presented results offer valuable guidance for future research in cancer detection, emphasizing the need for continued advancements in ML and DL-based approaches to improve diagnostic accuracy and ultimately save more lives.