Traditional Evaluation Research Articles

Towards evaluation of the PID criteria based UAVs observation and tracking head within resizable selection by COA algorithm

The study of unmanned systems has garnered significant academic attention worldwide. The advancements in tracking technologies, particularly the unmanned aerial vehicle (UAV) developed by the US Air Force, have motivated scientists to extensively explore this field due to the abundance of available aerial resources. However, the UAV’s effectiveness in motion is influenced by its three fundamental motions: pitch, roll, and yaw. To address this, scientists have been developing optimization algorithms and models to enhance the UAV’s performance. Nonetheless, in practical scenarios, each motion requires specific measurements, and their relative importance varies. This presents several challenges, including the existence of multiple criteria for selecting optimization values, determining the criteria’s significance, and evaluating trade-offs between performance across different optimization cases. Consequently, the selection and evaluation of UAV motion control algorithms becomes complex. This study proposes a novel selection process that employs the cuckoo optimization algorithm (COA) and real measured resizable margins for goal detection instead of traditional fixed-size evaluation criteria. The framework consists of enhancing PID motion gains using the COA algorithm, focusing on altitude and attitude functional PID controllers. The results of the selection process demonstrate the significance of various criteria for different motions. Rigorous evaluation and analysis were conducted to validate the proposed research framework.

Risk Assessment Method and Application of Urban River Ecological Governance Project Based on Cloud Model Improvement

This study classifies risk factors and establishes the risk-evaluation index system for an urban river ecological management project. That system includes construction personnel risk, construction technology risk, construction management factor risk, construction duration factor risk, and other risks. A total of 23 indicators determine the level and standard of each indicator. This study proposes a risk index system that uses the cloud model evaluation method and explains that model’s process. To demonstrate the risk evaluation methodology, it was applied to the ecological management project of Jinghe River in Jinghe New City, China. The results showed that: (1) the urban river ecological management project was subject to “a medium level of personnel risk, management risk, and construction period risk and a relatively high level of technical risk and other risks.” According to the evaluation results, the overall risk level of the project was medium, which is consistent with the risk level of the Jinghe River ecological management project at the current stage. (2) The limitation of the subjective qualitative concept caused by the traditional fuzzy comprehensive evaluation was fully considered in the improved method, and the application of the cloud-model based improved method in risk evaluation of the urban river ecological management project significantly improved the reliability and visualization of the evaluation results. Finally, the urban river ecological management project in China was adopted as an example to prove that the model boasts high stability, and some corresponding countermeasures were also proposed. The research results are expected to provide valuable references and scientific criteria for implementing urban river ecological management projects.

Decision tree of indicator benchmark: A hybrid method for assessing cities’ performance through urban indicators and benchmark

The rapid process of urbanization has brought out a series of challenges in urban governance, which prompts an increasing number of studies focusing on monitoring and evaluating the status of the cities. However, the detailed illustration of the establishment of a scientific-made basis for setting cities’ indicators benchmarks is often overlooked or inadequately explained in previous studies. Therefore, this study proposes a hybrid method to determine the benchmark for assessing cities’ performance. Firstly, this study establishes a mechanism called the Decision Tree of Indicator Benchmark (DTIB) which incorporates decision tree theory and benchmark management theory for urban indicator benchmark determination. Then, a deviation-degree-based model is proposed for assessing the performance of each indicator and the AHP method is applied for attaining the comprehensive performance of the city. To verify the effectiveness of the proposed hybrid method, an empirical study is conducted, and a comparison between the proposed method with other traditional evaluation methods is made to demonstrate the advantages of the proposed method. This hybrid method can facilitate decision-makers to understand the status of a city and explore the underlying problems of urban development to design more tailored policies and plans for cities.

Multi-index fuzzy comprehensive evaluation model with information entropy of alfalfa salt tolerance based on LiDAR data and hyperspectral image data.

Rapid, non-destructive and automated salt tolerance evaluation is particularly important for screening salt-tolerant germplasm of alfalfa. Traditional evaluation of salt tolerance is mostly based on phenotypic traits obtained by some broken ways, which is time-consuming and difficult to meet the needs of large-scale breeding screening. Therefore, this paper proposed a non-contact and non-destructive multi-index fuzzy comprehensive evaluation model for evaluating the salt tolerance of alfalfa from Light Detection and Ranging data (LiDAR) and HyperSpectral Image data (HSI). Firstly, the structural traits related to growth status were extracted from the LiDAR data of alfalfa, and the spectral traits representing the physical and chemical characteristics were extracted from HSI data. In this paper, these phenotypic traits obtained automatically by computation were called Computing Phenotypic Traits (CPT). Subsequently, the multi-index fuzzy evaluation system of alfalfa salt tolerance was constructed by CPT, and according to the fuzzy mathematics theory, a multi-index Fuzzy Comprehensive Evaluation model with information Entropy of alfalfa salt tolerance (FCE-E) was proposed, which comprehensively evaluated the salt tolerance of alfalfa from the aspects of growth structure, physiology and biochemistry. Finally, comparative experiments showed that: (1) The multi-index FCE-E model based on the CPT was proposed in this paper, which could find more salt-sensitive information than the evaluation method based on the measured Typical Phenotypic Traits (TPT) such as fresh weight, dry weight, water content and chlorophyll. The two evaluation results had 66.67% consistent results, indicating that the multi-index FCE-E model integrates more information about alfalfa and more comprehensive evaluation. (2) On the basis of the CPT, the results of the multi-index FCE-E method were basically consistent with those of Principal Component Analysis (PCA), indicating that the multi-index FCE-E model could accurately evaluate the salt tolerance of alfalfa. Three highly salt-tolerant alfalfa varieties and two highly salt-susceptible alfalfa varieties were screened by the multi-index FCE-E method. The multi-index FCE-E method provides a new method for non-contact non-destructive evaluation of salt tolerance of alfalfa.

Dual core processors: Coupled queues: Transient performance evaluation

High-Performance Computing (HiPC) systems routinely employ multi/many – core processors. Specifically, dual – core processors find many applications in pervasive computing devices. Dual–core processors employ buffers for queueing the incoming jobs. Traditionally, the queues at the processors are assumed to be independent and the queueing system is analyzed in equilibrium for tractability purposes. Queues are modeled using Continuous Time Markov Chains (CTMC's) and the equilibrium performance measures are determined to analyze as well as design the queueing systems. In most interesting cases, the incoming jobs are routed to the queues using the Join the Shortest Queue (JSQ) policy. Thus, with such an adaptive routing algorithm, the two queues are evidently coupled and are not statistically independent. Hence traditional equilibrium performance evaluation doesn't provide realistic performance measures. In this research paper, the two queues associated with buffers in dual-core processors are considered to be coupled. The Coupled Queues are modeled using a Quasi – Birth – and – Death (QBD) process. Using traditional results related to QBD processes, equilibrium performance measures are determined. More interestingly, we demonstrate the tractability of the computation of transient probability distribution of a QBD process. In the research literature, transient analysis of the QBD process was shown to be tractable in the Laplace transform domain. But in this research paper, we prove that the matrix exponential eQt arising in transient analysis (where Q is the generator matrix of the QBD process) can be computed directly in the time domain rendering efficient transient analysis of QBD process. Using the transient probability mass function of queue length, estimation of transient performance measures such as expected queue length, average delay, and tail distribution can be determined. Further, optimal adaptive routing algorithms for coupled queues can be designed.

Cross-functional Analysis of Generalization in Behavioral Learning

Abstract In behavioral testing, system functionalities underrepresented in the standard evaluation setting (with a held-out test set) are validated through controlled input-output pairs. Optimizing performance on the behavioral tests during training (behavioral learning) would improve coverage of phenomena not sufficiently represented in the i.i.d. data and could lead to seemingly more robust models. However, there is the risk that the model narrowly captures spurious correlations from the behavioral test suite, leading to overestimation and misrepresentation of model performance—one of the original pitfalls of traditional evaluation. In this work, we introduce BeLUGA, an analysis method for evaluating behavioral learning considering generalization across dimensions of different granularity levels. We optimize behavior-specific loss functions and evaluate models on several partitions of the behavioral test suite controlled to leave out specific phenomena. An aggregate score measures generalization to unseen functionalities (or overfitting). We use BeLUGA to examine three representative NLP tasks (sentiment analysis, paraphrase identification, and reading comprehension) and compare the impact of a diverse set of regularization and domain generalization methods on generalization performance.1

Extraction and Characterization of Bio-Silica from Sri Lankan Traditional Rice Husks and Evaluation to TLC Application

Rice husk (RH) is one of the most abundant agricultural wastes, but can be identified as a useful bio resource. Gonabaru, Kahawanu, Patchaperumal, Dahanala and Suwandel are five immerging traditional rice varieties, competing with genetically improved rice in Sri Lankan rice market. Ash content of RHs of traditional rice varieties (>20.0%) was comparatively higher than genetically improved rice. It was identified that additional acid washing step was essential to improve the purity of bio silica. Mineral impurities such as Na, K, Ca, Fe and Mn were determined using atomic absorption spectrometer (AAS). High silica content was extracted from RHs of traditional rice varieties (>23.0%) than that of improved variety, BW 364 (~16.2%). The functional groups present in Rice husk ash (RHA) and rice husk silica (RHS) were determined using Fourier transform infrared (FTIR) analysis. The potential application of purified RHS was observed by preparing and analyzing the thin layer chromatography (TLC) in comparison with commercial TLC silica.

Deep learning prediction model for central lymph node metastasis in papillary thyroid microcarcinoma based on cytology.

Controversy exists regarding whether patients with low-risk papillary thyroid microcarcinoma (PTMC) should undergo surgery or active surveillance; the inaccuracy of the preoperative clinical lymph node status assessment is one of the primary factors contributing to the controversy. It is imperative to accurately predict the lymph node status of PTMC before surgery. We selected 208 preoperative fine-needle aspiration (FNA) liquid-based preparations of PTMC as our research objects; all of these instances underwent lymph node dissection and, aside from lymph node status, were consistent with low-risk PTMC. We separated them into two groups according to whether the postoperative pathology showed central lymph node metastases. The deep learning model was expected to predict, based on the preoperative thyroid FNA liquid-based preparation, whether PTMC was accompanied by central lymph node metastases. Our deep learning model attained a sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and accuracy of 78.9% (15/19), 73.9% (17/23), 71.4% (15/21), 81.0% (17/21), and 76.2% (32/42), respectively. The area under the receiver operating characteristic curve (value was 0.8503. The predictive performance of the deep learning model was superior to that of the traditional clinical evaluation, and further analysis revealed the cell morphologies that played key roles in model prediction. Our study suggests that the deep learning model based on preoperative thyroid FNA liquid-based preparation is a reliable strategy for predicting central lymph node metastases in thyroid micropapillary carcinoma, and its performance surpasses that of traditional clinical examination.

Potential pathogenic germline variant reporting from tumor comprehensive genomic profiling complements classic approaches to germline testing

Existing guidance regarding clinically informed germline testing for patients with cancer is effective for evaluation of classic hereditary cancer syndromes and established gene/cancer type associations. However, current screening methods may miss patients with rare, reduced penetrance, or otherwise occult hereditary risk. Secondary finding of suspected germline variants that may confer inherited cancer risk via tumor comprehensive genomic profiling (CGP) has the potential to help address these limitations. However, reporting practices for secondary finding of germline variants are inconsistent, necessitating solutions for transparent and coherent communication of these potentially important findings. A workflow for improved confidence detection and clear reporting of potential pathogenic germline variants (PPGV) in select cancer susceptibility genes (CSG) was applied to a research dataset from real-world clinical tumor CGP of > 125,000 patients with advanced cancer. The presence and patterns of PPGVs identified across tumor types was assessed with a focus on scenarios in which traditional clinical germline evaluation may have been insufficient to capture genetic risk. PPGVs were identified in 9.7% of tumor CGP cases using tissue- and liquid-based assays across a broad range of cancer types, including in a number of “off-tumor” contexts. Overall, PPGVs were identified in a similar proportion of cancers with National Comprehensive Cancer Network (NCCN) recommendations for germline testing regardless of family history (11%) as in all other cancer types (9%). These findings suggest that tumor CGP can serve as a tool that is complementary to traditional germline genetic evaluation in helping to ascertain inherited susceptibility in patients with advanced cancer.

1D convolutional neural network combined with long-short term memory-based cell-to-cell inconsistency estimation considering recursive least squares and Euclidean distance

As the scale of application utilizing batteries as a power source expands, operation at the cell-tray and pack level has become indispensable. Such expansion amplifies the importance of accurate and consistent monitoring of cell-to-cell inconsistencies during battery operation for efficient and safe use. Consequently, this study addresses the limitations associated with inconsistency evaluations that rely solely on voltage deviation, which may lead to inaccurate inconsistent information, contingent on operational conditions and assessment timelines. By integrating the internal dynamics of an RLS-based 24S1P cell-tray, the aim is to derive deviations in electrical equivalent circuit model (EECM) parameters between cells, facilitating real-time battery inconsistency evaluations. The extracted EECM parameters are quantified utilizing the Euclidean distance for an effective quantification and scoring of multi-variables for inconsistency assessment. Additionally, to alleviate the computational burden associated with deriving the inconsistency evaluation index, an approach has been adopted that learns 60 % of the data prior to the occurrence of inconsistency through model-based index training, thereby verifying the results of the inconsistency estimation. In this process, a 1D CNN-LSTM model is developed, capable of proficiently learning and estimating the patterns of data changes. Finally, by benchmarking performance against traditional imbalance evaluations based solely on voltage deviation, this study demonstrates the robustness and precision of the proposed methodology, underscoring its capability for accurate imbalance evaluation, irrespective of SOC segment or evaluation timing.

A Novel Power Quality Comprehensive Estimation Model Based on Multi-Factor Variance Analysis for Distribution Network with DG

The power quality estimation for distribution network connected DG (distributed generation) is important in the power system. The significance testing for power quality indicator is less used in traditional power quality evaluation. However, the power quality indicator is affected by various factors of the power system, which seriously impact the power quality evaluation result. To solve this problem, A novel power quality comprehensive estimation model based on multi-factor variance analysis for distribution network with DG is proposed in this paper, in which the significance testing is carried out for power quality indicator with the various system factors, and then to generate the evaluation weights in different levels, further to obtain the power quality assessment results for single node. And then, the dual-significance tests are carried out to generate the weight of node and to obtain the comprehensive estimation result of whole system. At last, an example is developed to validate that, compared with the traditional power quality evaluation, the proposed method is more reasonable and effective in the power quality evaluation for DG connected distribution network.

Automatic patient functionality assessment from multimodal data using deep learning techniques – Development and feasibility evaluation

Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations.This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients.Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.

An Efficient Method for the Reliability Evaluation of Power Systems Considering the Variable Photovoltaic Power Output

The operational reliability of power systems is threatened by the random failure of components and uncertain power output of renewable energies, such as photovoltaics. Under such circumstances, reliability evaluation is necessary for maintaining a continuous and stable energy supply. However, traditional reliability evaluation methods are usually extremely time-consuming, considering the numerous system states that need to be analysed. Hence, the reliability evaluation process cannot follow up the dynamic changes in PV output, which makes the timeline of the evaluation disappointing. This paper proposes an efficient reliability evaluation method for power systems with PV integration. The method reveals the analytical relationship between the reliability levels of the power system and the uncertainty factors that influence the reliability, such as the PV output. In this way, the dynamic reliability evaluation is achieved, and the evaluation results can be updated timely when the output of PV changes. First, a Gaussian mixture-hidden Markov model (GMM-HMM) is used to model the distribution characteristics of PV output. Then, the state enumeration and the hyperbolic truncated polynomial chaos expansion method are used to determine the analytical relationship between the reliability indices and PV output. Lastly, based on the analytical function, the operational reliability of the power systems is dynamically evaluated considering the real-time PV output. The effectiveness of the proposed method is verified using the modified IEEE 30 system as an example.

Investigation into Mining Economic Evaluation Approaches Based on the Rosenblueth Point Estimate Method

Conducting technical and economic evaluations is important for mining investment and mining operation decision-making. Traditional economic evaluation methods rarely address the issue of evaluation reliability and usually require complex calculations to obtain the optimal solution. In this study, the Rosenblueth point estimate method for reliability evaluation of engineering project schemes is introduced. Combined with the cash flow method for economic evaluation of mines, the Rosenblueth point estimate method for evaluating the reliability of mining economy is established. Based on the technical and economic index of the case mine, taking the ore grade as a sensitivity indicator, empirical research on established models and methods was carried out. The results of the economic reliability evaluation and the variation rules obtained using the Rosenblueth point estimate method model were basically consistent with the actual production and operation rules of mining enterprise. The similar results also proved that the proposed model has good applicability and reliability for mining economic evaluation. Using the proposed RPEM economic reliability model, the economic reliability of a certain iron mine in Liaoning Province was calculated to be 99.95, which was a huge improvement compared with the traditional evaluation method. Additionally, the calculation process of the proposed model for economic reliability evaluation is simple and the accuracy is controllable. The economic reliability of the project can be calculated based on changes in sensitivity indicators, and the value range of sensitivity indicators can also be calculated through the required reliability. The obtained results and the proposed evaluation model provide a decision-making basis for mining investment projects and operation management.

Fuzzy assessment and improvement path of green finance quality in “Belt and Road”: Survey evidence from China

The “One Belt and One Road” is a major strategic deployment proposed by Chinese President Xi Jinping in 2013, and it is important to study the construction of green financial system under the “One Belt and One Road” initiative, especially the quality assessment of green finance to promote high-quality economic development along the route. At the same time, green finance and green “Belt and Road” have become a hot academic topic in the world. In this study, firstly, on the basis of the existing research on green finance, focusing on the evaluation of economic quality of green finance, we innovatively draw on the quality function deployment theory in marketing to logically transform our research ideas and propose a combined comprehensive evaluation method based on the hierarchical analysis (AHP) and entropy method in fuzzy mathematical theory, which makes up for the traditional single fuzzy evaluation method’s influence on the evaluation results. This method makes up for the shortcomings of the traditional single fuzzy evaluation method to evaluate the results of subjective or objective weighting results. In this study, we apply this method to the assessment of the quality of green finance development in “One Belt, One Road”, and it is important that we construct a system of “One Belt, One Road” green finance quality assessment indicators, including one primary indicator, four secondary indicators and 12 tertiary indicators. It is worth noting that our indicator system is different from the traditional quality system of financial quality assessment in that we take green factors into account in the construction of the tertiary indicators, and then use this assessment method to calculate and rank the weights (importance) of the 12 tertiary indicators, taking the actual situation in China as an example. More importantly, our study not only extends the academic research results of economic quality evaluation, but also combines quantitative research with qualitative analysis to propose three targeted countermeasures for the development of green finance in the countries along the Belt and Road. This study can also provide theoretical support for the quality assessment of green finance in countries along the Belt and Road, and promote the high-quality development of green finance in countries along the Belt and Road.

Using Beta Rhythm From EEG to Assess Physicians' Operative Skills in Virtual Surgical Training

The advancement of virtual reality technology has ushered in new developments in the medical field. The use of virtual surgery training simulators alleviates the paucity of training resources and high training expenses associated with traditional surgical capabilities. Regardless of the type of schooling, doctors must continue to educate themselves. The postoperative evaluation mechanism is incomplete. Traditional objective evaluation indicators are unable to meet surgeons' stringent expectations. The electroencephalograph (EEG) rhythm index is proposed in this article as a new tool for evaluating and distinguishing between novice and expert doctors. The experiment uses a cutting training module from neurosurgery training and compares it with established assessment metrics to determine the correct rate of classification of new evaluation metrics, classifying testers by both metrics and finding a 20% increase in correctness. Additionally, this article compares the energy topographic maps of different EEG rhythms of novices and experts. For classification, two-machine learning algorithms, SVM and random forest, are utilized at the same time. The findings reveal that the accuracy of distinguishing indicators based on EEG cycles is 10% higher than that of typical objective evaluation indicators, regardless of the categorization method. ROC curve analysis was also used to compare the two classification models. The AUC value for the EEG rhythm evaluation index model was 0.971, whereas the AUC value for the classic objective evaluation index model was 0.761, which explains the EEG rhythm evaluation index. The model demonstrates a categorization standard that is reliable.

An ensemble model for efficiency evaluation of enterprise performance based on evidential reasoning approach

Performance evaluation is one of the most important standards to measure the competitiveness and productivity of enterprises. Although existing studies could obtain the specific values of enterprises performance based on historical data, they usually failed to effectively evaluate enterprises performance in the consideration of different indicators. Meanwhile, as the characteristics of existing performance evaluation models are uneven, how to choose a reasonable data envelopment analysis (DEA) model for enterprises performance evaluation must be considered. Therefore, a new ensemble model on the basis of homogeneous, heterogeneous, and hybrid efficiency evaluation together with the evidential reasoning (ER) approach is proposed in this study for enterprises performance evaluation, so called the ER-based ensemble model. The ER-based ensemble model can overcome the inconsistency results caused by the application of different indicators and different DEA models. In case study, 40 state-own holding enterprises in China are selected and all these enterprises are evaluated and ranked using the integrated efficiency obtained from the ER-based ensemble model. Comparative analysis demonstrates that the ER-based model is better than some traditional efficiency evaluation models in enterprises performance evaluation and performance ranking.

Study on facial emotional expression induced by the annoying noise

Noise is annoying. Facial expressions indicate diverse emotions instantly, objectively and accurately. In this paper, the facial emotional expression is used to assess the noise annoyance to nullify the disadvantages of traditional evaluation methods. Firstly, by exploiting listening experiments, the facial videos, physiological signals and individual annoyance from 30 listeners with different nationalities were collected. Then, the facial action units (AUs) were extracted, identified and analyzed, and the AUs combination reflects annoyed emotion induced by the noise was confirmed. Furthermore, a novel evaluation scale embodying color and emotional symbols was proposed, which has been proven to be valid by the social surveys. The present study confirms that it is feasible to evaluate the noise annoyance more objectively by exploring the listener's facial "annoyance" expression. The innovative scale can avoid the understanding bias and improve the evaluation efficiency remarkably.

Evaluation of Online Learners’ Learning Performance Based on Fuzzy Borda Method

Since the traditional evaluation system of learning focuses only on the summative evaluation of learners’ test scores, ignoring the process evaluation of learners in learning process, the evaluation system should not only consider the content of the online learning process but also that of face-to-face classroom teaching process to evaluate learners’ learning performance in multiple dimensions and levels. This approach evaluates online learning more reasonably, operably and feasibly. To overcome limitation of using more than one method in the evaluation of learning performance, the learning performance of online learners was first evaluated using the principal component analysis method, entropy method, comprehensive indicator method, and TOPSIS method. Based on these four evaluation results, a combination evaluation model of online learners’ learning performance was constructed. The Fuzzy Borda method was used to evaluate nine administrative classes of accounting majors in three colleges and universities in Hainan. The results show that simply using a single evaluation method to evaluate the learning performance of online learners has defects, and the evaluation results are too biased. By using the Fuzzy Borda method, multiple evaluation methods can be combined, allowing a single evaluation method to realize complementary advantages and obtain more comprehensive and credible evaluation results. Using the Fuzzy Borda method to systematically evaluate the learning performance of online learners improves the scientificity of evaluation results and provides a new idea and method for the evaluation of online learning performance. The results of this study have significant reference value for using scientific evaluation methods and objective data to evaluate online learning performance, rank online learners scientifically, and summarize teaching experience to improve online learning performance.

Assessment Literacy, Current Assessment Practices and Future Training: Reflections of Teachers in Higher Education

The aim of this study is to explore assessment literacy, to analyse the current assessment practices of teachers in Higher Education (HE) and to understand the need for alternative assessment practices in light of teachers’ reflections. The research employed incidental sampling to select (n=58) HE teachers from a single multidisciplinary university in Maharashtra State, India. A comprehensive literature review on assessment literacy provided the theoretical foundation, and a specialized assessment tool was developed to assess the teachers' assessment literacy level, perspectives, and practices in assessment. Data were collected through voluntary participation and analysed using quantitative and qualitative techniques. The findings revealed that the majority of HE teachers (79%) demonstrated a moderate level of assessment literacy, while 16% exhibited low levels and only 5% showed high levels. Additionally, a discrepancy was observed between the preferred assessment types of teachers and learners, with learners emphasizing active participation and real-world applications, while teachers leaned toward traditional evaluation methods. Participants were found to be literate only in specific aspects such as meaning, basic forms of assessments, and the purpose of educational assessments. Furthermore, teachers minimally acknowledge and have a lesser preference for alternative assessment methods, such as article analysis, team projects, case studies, and discussions. Overall findings indicate teachers' lack of knowledge about assessment practices that involve evaluating student performance, fostering collaboration, assessing higher-order thinking skills, and utilizing assessment as a tool for learning and improvement. This research confirms the need for training to enhance teachers' assessment literacy, promoting the adoption of alternative assessment practices in higher education to optimize student learning experiences.