Categorization Procedure Research Articles

Quality Inspection of Battery Separators by Partial Discharge Spectroscopy

AbstractQuality control is highly relevant for safety, sustainability and efficiency of the battery manufacturing process. An early and reliable detection of failures in the production chain is important. Here we present a method for detecting micrometric imperfections and contaminations on the battery separator before filling the battery stack with the electrolyte. We sense these irregularities by measuring an increase of partial discharges when applying between the battery electrodes potentials close, but still well below the breakdown voltage of the separator. We can distinguish different degrees and different types of contamination with a very high confidence. This is enabled by a throughout statistical analysis of the partial discharge events. The overall reliability of detecting a contaminated against the clean separator is 96 %. The technique, as implemented here, uses categorization procedures and machine learning algorithms to automate decision‐making and can accelerate the quality assessment process in pilot lines or small‐ manufacturing. Compared to other methods, like optical detection or full discharge measurements, the here presented approach is very reliable, simple to implement and virtually noninvasive.

An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm.

Breast cancer is one of the most often diagnosed cancers in women, and identifying breast cancer histological images is an essential challenge in automated pathology analysis. According to research, the global BrC is around 12% of all cancer cases. Furthermore, around 25% of women suffer from BrC. Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. Using a BreakHis dataset, we demonstrated in this work the viability of automatically identifying and classifying BrC. The first stage is pre-processing, which employs an Adaptive Switching Modified Decision Based Unsymmetrical Trimmed Median Filter (ASMDBUTMF) to remove high-density noise. After the image has been pre-processed, it is segmented using the Thresholding Level set approach. Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. The suggested strategy facilitates the acquisition of precise functionality attributes, hence simplifying the detection procedure. Additionally, it aids in resolving problems pertaining to global optimization. Following the selection, the best characteristics proceed to the categorization procedure. A DL classifier called the Conditional Variation Autoencoder is used to discriminate between cancerous and benign tumors while categorizing them. Consequently, a classification accuracy of 99.4%, Precision of 99.2%, Recall of 99.1%, F- score of 99%, Specificity of 99.14%, FDR of 0.54, FNR of 0.001, FPR of 0.002, MCC of 0.98 and NPV of 0.99 were obtained using the proposed approach. Furthermore, compared to other research using the current BreakHis dataset, the results of our research are more desirable.

Economic Factors Affecting Peace and Stability in Oromia Regional State, Ethiopia

Oromia Regional National state has experienced violent conflicts with alarmingly increased frequency and magnitude since 2018.However, there is a need for scientific and deep analysis of the economic factors that have been affecting the peace and stability of the region. Therefore, this study is aimed to assess the economicfactors of peace andstabilityin the Oromia Regional State. Based on qualitative and quantitative approaches, data were collected from thevarious key informants, four group discussions via purposive techniques and 180 police officers from 23 woredas belonging to seven zones and one city administration based on stratified random sampling. Besides, the study was employed based on descriptive and explanatory designs. The data collected via the qualitative research approach were analyzed using thematic categorization procedures. While descriptive statistics were also used to describe, summarize, or explain a given set of data, particularly, inferential and correlation analysis was employedtolookat thepatternof relationshipsbetweenseveral variables simultaneously. Thefindingsof the study indicated that the roles of the economy are significantly affecting peace and stability of the region.The result explained that78.6% changes in the status of peace and stability of the region is as a result of independent variables. It is reasonable to infer that 78.5% of the current problem of peace and stability in the Oromia regional state is determined as a result of economic problems. Consequently, the government should strongly work to avoid and manage maladministration, unemployment, and economic conspiracy. This day requires unique economic packages that attracts the youth to the development and democratic path of the country. In addition to this, the avenues for raising political consciousness and political community should be taken place. Keywords: Economic, Instability, Peace, Factors

영아교사의 놀이중심 보육과정 실천에 대한 인식탐색

Objectives This study aims to explore the awareness of infant teachers regarding play-centered curriculum practices, based on the emphasis on ‘a play-centered curriculum’ in the 4th Standard Childcare Curriculum.
 Methods To achieve this, the Photovoice research method was implemented with 10 lead teachers in infant classes from childcare centers in the G region of Gyeongsangbuk-do. The participants utilized photos they captured themselves to collect research data, engaging in discussions and expressing their voices. The collected data underwent analysis procedures of selection, contextualization, and categorization.
 Results Subcategories of infant teachers' awareness of play-centered curriculum practices emerged through this study, including play as a way of life, positive emotions from play, characteristics of play, new possibilities and opportunities, change for growth, teacher interactions, and collaboration with parents. These were categorized as play-centered curricula shaping the child's past and present, vibrant play-centered curricula fostering the child's uniqueness, and resonant communication-driven play-centered curricula.
 Conclusions Based on these findings, it is significant to highlight that play-centered curriculum practices contribute to supporting the growth and development of infants, emphasizing the importance of collaboration between teachers and parents in enhancing their effectiveness.

Kano Model Categorization Methods: Typology and Systematic Critical Overview for Hospitality and Tourism Academics and Practitioners

The Kano model has been widely applied in multiple disciplines, particularly in product or service development and optimization. Although the original categorization framework remains the most applied, modifications and new approaches have recently been developed. Yet, most of the hospitality and tourism studies continue to use the original Kano method when newer and superior methods are available. The new categorization methods also have flaws, which academics and practitioners should be aware of. The present study uses a systematic literature review approach to provide a comprehensive synthesis and critical analysis of the existing Kano model categorization methods structuring them in groups based on categorization procedures and pointing to the strengths, weaknesses, and fit of each method. The manuscript aims to provide the most current systematic roadmap and “how-to” of the existing methods. It also points to the existing knowledge gaps and directions for future research.

Multi-Layer Architecture for Enhancing Crop Quality with AI and IoT: A Structural Modelling Approach

Conventional crop management methods must be improved to address the increasing global food requirements. The exponential growth of the population exacerbates the issue at hand, the impacts of climate change and inadequate farming practices. This study analyzes the key determinants contributing to establishing a comprehensive framework for using Internet of Things (IoT) technology in the agricultural sector. The proposed Multi-Layer Architecture for Crop Quality (MLA-CQ) employs a modified version of the Total Interpretive Structural Modelling (mv-TISM) methodology to achieve this objective. This research used a mv-TISM approach to build and analyze the interrelationships among various factors that influence the adoption of IoT technology in the agriculture industry. This study introduces Artificial Intelligence (AI) by incorporating soft sensors into a remote sensing framework via deep learning. The initial data has undergone pre-processing procedures to identify and address missing values and perform data cleaning and noise reduction on the picture data obtained from farmland. Following the feature representation, a categorization procedure was performed employing an ensemble design. The suggested approach has been used to conduct experimental trials on various crops, resulting in a computing time reduction of 62%, accuracy of 95.2%, precision of 91.3 %, recall of 92.3%, and an F score of 93.1%.

Current state of knowledge and the applicability of augmented reality in orthopedic surgery – a systematic review

Background: Surgical practice is constantly altering due to computer-assisted solutions. When it comes to computer-integrated surgical methods, augmented reality is one of the most innovative technologies. Although augmented reality is becoming more and more common in many medical fields, its use in orthopedic surgery is still unclear. This article aims to give a comprehensive overview of the state of the art and the potential applications of AR in orthopedic surgery. Method: A thorough study of the literature was conducted to determine the present level of knowledge and the relevance of AR in orthopedic surgery. The three databases \"PubMed,\" \"Cochrane Library,\" and \"Web of Science\" were thoroughly searched. The \"Preferred Reporting Items on Systematic Reviews and Meta-analysis\" criteria were adhered to during the systematic review. Results: Based on the previously mentioned preset search keywords and exclusion criteria, six papers were selected for additional examination. During the categorization procedure, the gathered studies were separated into the following groups: trauma, placement, and surgical education and training. Every study included populations of people. Two HMDs, a projector, and a monitor were employed as the display type in three of the trials. Conclusion: The research and current understanding of augmented reality in orthopedic surgery as reported in the literature is summarized in this publication.

A Perspective Study on Tweet Sentiment Analysis using Data Mining, Machine Learning and Deep Learning Paradigms

In recent years there has been an increase in interest in collecting and studying text from social networks, review websites, blogs, forums and other forms of user-generated information. The text offers a vast array of ideas from people of diverse profiles, including education, age and their perspectives, region of residence, on how they see goods and services, policy opinions, etc. The analysis of judgments, responses, and emotions drawn from texts is known as sentiment analysis. The sentiment categorization procedure establishes whether a text is subjective or objective, or whether it provokes both positive and negative responses. The most popular method of classification is based on polarity or orientation for accomplishing tweet sentiment analysis. In this paper, a detailed survey on various algorithms used for performing opinion mining, sentiment analysis, tweet sentiment analysis is discussed in detail. The study shows that text preprocessing, data mining, machine learning algorithm and deep learning paradigms plays a vital role in categorization of people’s feeling on a specific topic or a product. In this study, the existing challenges in optimizing the process of tweet sentiment analysis is also discussed and the suggestions for improving is also discussed.

An automatic and association-based procedure for hierarchical publication subject categorization

Subject categorization of scientific publications, i.e., journals, book series or conference proceedings, has become a main concern in academia, as publication impact and ranking are considered a basic criterion to evaluate paper quality. Publishers usually propose their own categorization, but they often include only their own publications and their categories might not be coherent with other proposals. Also, due to the dynamic nature of science, new categories may frequently appear. As traditional mechanisms for categorization have been questioned by many authors, a new research line has emerged to improve the category assignment process. Approaches usually rely on assessing publication similarity in terms of topics, co-citation, editorial boards, and/or shared author profiles. In this work, we propose a novel procedure for scientific publication hierarchical categorization based on the repetition or absence of relevant descriptors in association rules among publications. The key idea is that publication categories can be automatically defined by strong associations of nuclear topics. Also, some very specific subcategories can be defined by exclusion from any set of rules. This process can be used to construct a data-driven hierarchy of scientific publication categories from scratch or to improve any existing categorization by discovering new fields. In this paper the proposed algorithm uses SJR descriptors all journals in the SCImago dataset and the three-level classification in the Scopus dataset (covering only 35 % of publications of the SCImago dataset) to discover new categories and assign every journal to the resulting enhanced hierarchy one. We have focused on the field of “Physical Sciences and Engineering”, using the SCImago and Scopus datasets from 2019 (30,883 scientific publications). Our procedure combines data engineering techniques with association rules and generates as a result potential new categories and outlier subcategories. To evaluate the suitability of our proposal, we have analyzed classification results based on the original category list and our extended two-level categorization via the Jensen–Shannon divergence and supervised machine-learning techniques. Results reveal the consistency and suitability of our categorization procedure.

Deep learning Binary/Multi classification for music's brainwave entrainment beats.

The last two decades have seen the emergence of a brand-new kind of music known as digital brain stimulant, also known as instrumental music or music without lyrics, which mostly comprises entrainment beats. While listening to it has the same ability to affect the brain as taking medication, it also has the risk of having a negative impact or encouraging unwanted behavior. This sparked the interest of a large number of studies in the psychological and physiological effects of music's brainwave entrainment beats on listeners. These studies started to categorize and examine how musical beats affected brainwave entrainment by looking at electroencephalogram (EEG) signals. Although this categorization represents a step forward for the early research efforts, it is constrained by the difficulty of having each musical track and conducting EEG tests on humans exposed to distortion due to noise in order to determine its influence. The work proposed in this article continues to explore this topic but in a novel, simple, accurate, and reliable categorization procedure based on the music signal elements themselves rather than dependent on EEG. VGGish and YAMNET based transfer deep learning models, are tuned to handle a straightforward, accurate real-time detector for the existence of the music beats inside music files with accuracy of 98.5 and 98.4, respectively. Despite the fact that they yield results that are equivalent, the YAMNET model is more suited for use with mobile devices due to its low power consumption and low latency. The article also proposes modified version of VGGish and YAMNET binary classifying models called BW-VGGish and BW-YAMNET respectively. The modification was to turn the binary classification into multi-classification. These multi-classifiers handle the classification of the influence of music beats (five different brain waves) on human brainwave entrainment with average accuracy of 94.5% and 94.5%, respectively. Since there was a lack of datasets addressing this kind of music, two datasets, the Brainwave Entrainment Beats (BWEB) dataset and the Brainwave Music Manipulation (BWMM) dataset, were generated for classification training and testing. The re-testing on a sample of music files that have their impact on brain waves (with their EEG) in an earlier study is done to strengthen the validity of the proposed work and to overcome the potential limitation of utilizing a music dataset that is not proved with its EEG. The success of the suggested models was demonstrated.

Categorical extension of dualities: From Stone to de Vries and beyond, II

Under a general categorical procedure for the extension of dual equivalences as presented in this paper's predecessor, a new algebraically defined category is established that is dually equivalent to the category LKHaus of locally compact Hausdorff spaces and continuous maps, with the dual equivalence extending a Stone-type duality for the category of extremally disconnected locally compact Hausdorff spaces and continuous maps. The new category is then shown to be isomorphic to the category CLCA of complete local contact algebras and suitable morphisms. Thereby, a new proof is presented for the equivalence LKHaus≃CLCAop that was obtained by the first author more than a decade ago. Unlike the morphisms of CLCA, the morphisms of the new category and their composition law are very natural and easy to handle.

K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups

The research conducted in this study was driven by the East Java provincial government's requirement to assess the transaction levels of the Student Business Group (KUS) in the SMA Double Track program. These transaction levels are a basis for allocating supplementary financial aid to each business group. The system's primary objective is to assist the provincial government of East Java in making well-informed choices pertaining to the distribution of supplementary capital to the KUS. The classification technique employed in this study is the multilayer perceptron. However, the K-Means Clustering method is utilised to generate target data due to the limited availability during the classification process, which involves dividing the transaction level attributes into three distinct groups: (0) low transactions, (1) medium transactions, and (2) high transactions. The clustering process encompasses three distinct features: (1) income, (2) spending, and (3) profit. These three traits will be utilized as input data throughout the categorization procedure. The classification procedure employing the Multilayer Perceptron technique involved processing a dataset including 1383 data points. The training data constituted 80% of the dataset, while the remaining 20% was allocated for testing. In order to evaluate the efficacy of the constructed model, the training error was assessed using K-Fold cross-validation, yielding an average accuracy score of 0.92. In the present study, the categorization technique yielded an accuracy of 0.96. This model aims to classify scenarios when the dataset lacks prior target data.

English

Crop mapping and its health monitoring are of key importance for a country like Pakistan where a considerable share of the GDP originates from agricultural production. Currently, extensive capital and human resource are utilized to record the crop types and monitor crop health due to variations in crop pattern and intercropping practices being observed in many parts of the country. Satellite Remote Sensing, however, can be utilized very efficiently to identify crop types and estimate acreage by using unique phenological cycles derived from NDVI thresholds. In this research, we proposed a semi-automated, efficient and cost-effective state-of-the-art technique for crop classification using satellite remote sensing datasets and GIS techniques to replace outdated conventional survey-based crop estimations procedures. This research was conducted in Sahiwal district, Pakistan to categorise several crop types using two major inputs: satellite (Sentinel-2, 10m) imagery and local crop calendar. The research adopted a semi-automatic approach to find the main crops of Rabi (winter) and Kharif (summer) using orthorectified and atmospherically corrected Level-1C images to extract phenological information based on Normalized Difference Vegetation Index (NDVI). The complete phenological life cycle of crops including sowing, growing, and harvesting stages were deeply analyzed with the help of crop calendar and NDVI profiles. Then this information passed through iterative selforganizing (ISO Data Model) of unsupervised classification technique in the form of temporal stacked images. The NDVI profiles of each crop were synchronized with the crop calendar of the study area to develop the crop categorization procedure. The results were validated with the statistics of the agriculture department Lahore and field samples gave about 85% and 88% accuracy for Rabi and Kharif cropping seasons respectively. It is concluded from the research that the tested methodology is capable of providing a fast, cost-efficient, and reliable solution for crop mapping instead of conventional survey-based crop area assessment techniques (conventional Girdawri) which requires a vast number of resources and time, and it can be replicated in different areas with minor changes

Emerging micropollutants in aquatic ecosystems and nanotechnology-based removal alternatives: A review

There is a significant concern about the accessibility of uncontaminated and safe drinking water, a fundamental necessity for human beings. This concern is attributed to the toxic micropollutants from several emission sources, including industrial toxins, agricultural runoff, wastewater discharges, sewer overflows, landfills, algal blooms and microbiota. Emerging micropollutants (EMs) encompass a broad spectrum of compounds, including pharmaceutically active chemicals, personal care products, pesticides, industrial chemicals, steroid hormones, toxic nanomaterials, microplastics, heavy metals, and microorganisms. The pervasive and enduring nature of EMs has resulted in a detrimental impact on global urban water systems. Of late, these contaminants are receiving more attention due to their inherent potential to generate environmental toxicity and adverse health effects on humans and aquatic life. Although little progress has been made in discovering removal methodologies for EMs, a basic categorization procedure is required to identify and restrict the EMs to tackle the problem of these emerging contaminants. The present review paper provides a crude classification of EMs and their associated negative impact on aquatic life. Furthermore, it delves into various nanotechnology-based approaches as effective solutions to address the challenge of removing EMs from water, thereby ensuring potable drinking water. To conclude, this review paper addresses the challenges associated with the commercialization of nanomaterial, such as toxicity, high cost, inadequate government policies, and incompatibility with the present water purification system and recommends crucial directions for further research that should be pursued.

Desarrollo económico y orgullo: el caso de las artesanas ralámuri de Majimachi, Chihuahua, México

The artisan woman appears strongly in issues of local economic development, being the bearer of ancestral knowledge and transmitter of the same. The objective is to analyze the regional economic development that is generated around the craft sale, customer hugging, pride in their roots. It is based on the qualitative method under the oral history methodology, under the open categorization procedure. Applying semi-structured survey technique, field observation and interview, within the first semester of the year 2021. With a representation of 25 oral histories, 25 surveys and 10 interviews, applied to a Ralámuri woman from the ejido San Luis de Majimachi, Bocoyna, Chihuahua, Mexico.The results show an economic flow in the tourist regions of the municipality of Bocoyna and tourist centers near the Ejido San Luis de Majimachi through , the sale of handicrafts, triggering the main attraction the Ralámuri culture, in addition, the autonomy of women fordecision-making regardingfamily spending,what to produce, how to produce it and for whom to do it, the high degree of haggling for its clients and unfair sales by resellers is shown, in addition, the pride of its ralámuri roots and being an artisan was verified

DenseNet_ HybWWoA: A DenseNet-Based Brain Metastasis Classification with a Hybrid Metaheuristic Feature Selection Strategy.

Brain metastases (BM) are the most severe consequence of malignancy in the brain, resulting in substantial illness and death. The most common primary tumors that progress to BM are lung, breast, and melanoma. Historically, BM patients had poor clinical outcomes, with limited treatment options including surgery, stereotactic radiation therapy (SRS), whole brain radiation therapy (WBRT), systemic therapy, and symptom control alone. Magnetic Resonance Imaging (MRI) is a valuable tool for detecting cerebral tumors, though it is not infallible, as cerebral matter is interchangeable. This study offers a novel method for categorizing differing brain tumors in this context. This research additionally presents a combination of optimization algorithms called the Hybrid Whale and Water Waves Optimization Algorithm (HybWWoA), which is used to identify features by reducing the size of recovered features. This algorithm combines whale optimization and water waves optimization. The categorization procedure is consequently carried out using a DenseNet algorithm. The suggested cancer categorization method is evaluated on a number of factors, including precision, specificity, and sensitivity. The final assessment findings showed that the suggested approach exceeded the authors' expectations, with an F1-score of 97% and accuracy, precision, memory, and recollection of 92.1%, 98.5%, and 92.1%, respectively.

Enhancing Dorsalgia Prediction using Neutrosophic Sets in a Genetic Algorithm-Optimized Hybrid CNN-LSTM Framework on Spinal Geometry Parameters

Predicting dorsalgia involves a multifaceted approach that encompasses the analysis of demographic, lifestyle, and medical data. Machine learning algorithms and advanced data analytics play a pivotal role in forecasting the risk of developing back pain. Early prediction aids in proactive interventions and personalized healthcare strategies, thereby mitigating the burden of dorsalgia on individuals and healthcare systems. The proposed feature selection is the initial feature set’s most educational elements by evolutionary gravitational search-based feature selection (EGSFS). Specifically, the framework is trained and fine-tuned using spinal geometry parameters, enabling precise identification of individuals at risk of developing dorsalgia. This study presents a novel approach for classification tasks using a Genetic Algorithm (GA)-optimized hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. The GA optimizes the model’s architecture and hyperparameters to enhance its performance. The framework is implemented using Python. In the categorization procedure, the Single Neutrosophic sets aid in capturing ambiguity, which is particularly beneficial when handling dorsalgia disorders that may present with confusing symptoms, thus enhancing the accuracy of classifying various dorsalgia conditions. Experimental results demonstrate that this hybrid approach significantly improves classification accuracy, making it a viable option for several practical applications. Experimental results exhibit remarkable improvements in accuracy and predictive power, underscoring the potential of this innovative approach in advancing preventative and personalized healthcare strategies for back pain management. The experiment was built on the lower back pain symptoms dataset. A comparison is made between the experimental results and previous prediction models like Logistic Regression, Decision Tree Classifier, Random Forest, and Support Vector Machine in terms of accuracy, F1-score, precision, and recall. The accuracy of normal and abnormal data is 99%.

Clustering and switching analysis of verb fluency in individuals with Alzheimer’s disease

To investigate verb fluency performance in individuals with Alzheimer's disease compared with healthy older adults by analyzing total correct responses, number of clusters, average cluster size, and number of switches. This is a case-control study of 39 healthy older adults and 29 older adults with a diagnosis of Alzheimer's disease. Verb fluency performance was analyzed in terms of total number of correct verbs retrieved, number of clusters, average cluster size, and number of switches. To obtain the study outcomes, we previously conducted a procedure for categorization of the verbs that would compose the clusters. The classification of verbs was adapted for this study, including assessment by raters and analysis of inter-rater reliability. Individuals with Alzheimer's disease showed significantly poorer performance than healthy controls in the number of switches and total number of correct verbs retrieved. The two groups did not differ significantly in the other measures. In this study, individuals with Alzheimer's disease showed impaired verb fluency, characterized by a reduced number of verbs retrieved and fewer transitions between verb categories. The findings suggest that, in Alzheimer's disease, verb fluency is more sensitive to cognitive deficits resulting from executive dysfunction than from semantic disruption.

Análise de clustering e switching da fluência de verbos em indivíduos com doença de Alzheimer

ABSTRACT Purpose To investigate verb fluency performance in individuals with Alzheimer’s disease compared with healthy older adults by analyzing total correct responses, number of clusters, average cluster size, and number of switches. Methods This is a case-control study of 39 healthy older adults and 29 older adults with a diagnosis of Alzheimer’s disease. Verb fluency performance was analyzed in terms of total number of correct verbs retrieved, number of clusters, average cluster size, and number of switches. To obtain the study outcomes, we previously conducted a procedure for categorization of the verbs that would compose the clusters. The classification of verbs was adapted for this study, including assessment by raters and analysis of inter-rater reliability. Results Individuals with Alzheimer’s disease showed significantly poorer performance than healthy controls in the number of switches and total number of correct verbs retrieved. The two groups did not differ significantly in the other measures. Conclusion In this study, individuals with Alzheimer’s disease showed impaired verb fluency, characterized by a reduced number of verbs retrieved and fewer transitions between verb categories. The findings suggest that, in Alzheimer’s disease, verb fluency is more sensitive to cognitive deficits resulting from executive dysfunction than from semantic disruption.

Detection and classification of plant diseases in leaves through machine learning

Plant diseases cause significant productivity and economic losses, as well as a reduction in agricultural product quality and quantity. One principal impact on low crop yield is sickness due to bacteria, virus and fungus It is possible to avoid it by employing plant disease detection and categorization procedures. We used machine learning to detect and classify diseases in plant leaves because it evaluates data from several perspectives and categorizes it into one of several predefined classifications. In this research we create a model for the classification task which is sequential model. We trained a convolutional neural network (CNN) with help of the plant village dataset, which have 55,000 images divided into 39 completely distinct categories of each healthy and effected leaves. We trained data by using Adam optimization technique because it almost constantly plays quicker and higher global minimal convergence in comparison to the alternative optimization techniques. We achieved a validation accuracy of 98.74% using the architecture of CNN containing optimized parameters. CNNs, as can be observed, have a high-stop overall performance, making them surprisingly suitable for computerized identification of plant illnesses using simple plant leaf images. The experiment effects completed are similar with different current strategies in literature.