Primitive Methods Research Articles

An Overview of the Most Important Methods for Coloring Grayscale Images

Colorization: Converting a grayscale image into an image with colors acceptable to the viewer and achieving the minimum coloration goal until it becomes closer to reality. The goal of the coloring process is to produce a convincing image and the result is close to the reality of the colored image. Converting grayscale images to color images is a very difficult process due to the specificity of grayscale images and how closely they match color in every detail of the image. Coloring methods have evolved to become less invasive and produce more acceptable results to the viewer's eye, but they have become more difficult. This research provides an overview of image colorization methods, their advantages and disadvantages, and compares the quality of each method according to specific criteria. Coloring methods have evolved from primitive methods based on the manual principle to using computing techniques such as adding scribbles within areas of the image, then moving to choosing a reference image that can be used to obtain colors, to using deep learning techniques that rely on training a model on colored images and then testing this model and showing Methods based on deep learning have better results than other methods, and the algorithm showed GAN outstanding results in coloring that outperform some deep learning coloring methods, and also outperform other methods. We hope that this study will be a scientific product that will benefit the research community and enable them to obtain a useful overview of the most important methods of coloring images.

Boosting the classification success in imbalanced data of bee larva cells

Selecting the appropriate honey harvesting method is crucial for sustainable beekeeping and optimal honey production. The use of primitive harvesting methods can lead to the death of bees and a decrease in honey yield. This study aims to address the issue of detecting and classifying young larvae on honeycombs. However, the area where young larvae are found is limited compared to other areas. In this study, the dataset obtained from honeycombs was imbalanced, which has used the Synthetic Minority Oversampling TEchnique (SMOTE) algorithm to balance it. The SMOTE algorithm is a synthetic data generation method. The balanced dataset was then used for classification processes with k-Nearest Neighbors algorithm (k-NN), Decision Trees, and Support Vector Machines. The evaluation of the classification results included the F1-Score, G-Mean, and AUC metrics. The results showed that the classification of the dataset balanced with synthetic data was more successful.

DESIGN FOR SUSTAINABILITY USING A CRAFT-BASED HUMAN-CENTRIC APPROACH AND CONSUMER PARTICIPATION

It is often perceived that craft means no technology, primitive methods, and materials that have zero or significantly less impact on the actual needs of people. However, this research is trying to trace the craft's nature, importance, and potential in creating human-centric designs with sustainable methods. This research discusses the role of craft and design for a sustainable future. The intent is to build a bridge between design, craft, and sustainability. This paper hopes to bring together all three and benefit the participating stakeholders, researchers, designers, and artisans, allowing them to exchange and coordinate their points of view and expertise to find new solutions to associate craft with sustainable design, which are the crucial design challenges of today.This research contributes to (1) exploring practices and connotations of traditional craft about human-centric design thinking and sustainability; (2) validating the hypothetical framing of the coordination amid craft and sustainability through actual case studies, (3) stakeholder engagement can be enhanced through a craft-based approach towards sustainable development. This paper discusses Indian consumers' feedback towards craft-based products, accepts their suggestions, authenticates, and draws conclusions. Hypothetically we can say that A craft-based strategy might have a good impact on sustainable design.The research paper's outcome is a suggestive plan to incorporate craft as the basic structure and vital resource of human-centric design and contribute to sustainability. We claim that craft has critical potential to contribute towards evolution for sustainable design transformation for people and society, which may lead to significant changes in worldview, economic activities, and environmental concerns.

Sika deer trajectory prediction considering environmental factors by timeseries transformer-based architecture

Recently, the damage to agriculture, forestry, and fishery by wildlife has become a serious social problem. The damage caused by a conflict between wildlife and human society often causes less motivation for farming and is expected to significantly inhibit the development of local agriculture and forestry. However, the current management of wildlife is limited to primitive methods such as stabilizing the population by culling reliant on experience. To prevent this problem, the trajectory prediction of wildlife is one of the solutions for efficient wildlife management.In this study, we propose a machine learning architecture for predicting wildlife trajectories, considering surrounding environmental factors. Particularly, we propose a machine learning architecture that more accurately predicts trajectories using a timeseries transformer model that can learn long-term dependencies and add a submodel that can consider surrounding environmental factors. The proposed architecture allows interpreting which past trajectory areas should be focused on when predicting a trajectory by visualizing the weight of the focus mechanism, one of the elements comprising the transformer model. Further, the proposed architecture can decide the priority of elements of a multivariate timeseries input by considering a variable selection network (VSN), which considers the relationship of timeseries between a given time point and those before and after using casual convolutions. The main advantage of our methods is to understand the potential environmental factors to predict animal trajectories by analyzing the importance of each input feature representation in VSN.We experimented to evaluate the proposed method using one of the largest biologging datasets of sika deer (Cervus nippon) in Kanagawa Prefecture, Japan. The proposed method outperforms existing methods on various evaluation metrics and can effectively consider environmental factors in predicting trajectories. In addition, we analyze the results and show that the proposed architecture can pay attention to the times that individuals moved significantly within the observation period by visualizing the trajectories for the best and worst cases and the weight of each environmental factor.

Development of a Firefighting Drone for Constructing Fire-breaks to Suppress Nascent Low-Intensity Fires

Korean forests are highly vulnerable to forest fires, which can severely damage property and human life. This necessitates the establishment of a rapid response system and the construction of firebreaks to prevent the spread of fires and protect key facilities. The existing firebreak construction methods can be classified into prevention- and response-stage methods. In the prevention stage, the progression and spread of fire are delayed, while in the response stage, primitive manual methods involving tools such as hooks are used, in addition to aerial deployment of water and fire retardants through helicopters. Herein, we propose the use of “fire-extinguishing drones” for firebreak construction during the initial, low-intensity stage of a fire before the deployment of firefighting personnel. We implement a continuous fire-extinguishing module capable of carrying six fire-extinguishing balls to verify its deployment accuracy and stable hovering capabilities. Through the operation of multiple drones using a ground control system and real-time kinematics to precisely generate designated automatic flight paths, we conducted experiments to assess the feasibility of firebreak construction by using fire-extinguishing drones to prevent the spread of wildfires. A firebreak construction field test was conducted to evaluate the accuracy of continuous fire extinguisher deployment, hovering performance during deployment, accuracy of the RTK-designated paths, and GCS performance. The proposed system achieved 100% performance on all indicators, except the accuracy of the RTK-designated paths.

Direction of salmon aquaculture development in the world

The growth of the world's population implies the search for alternative sources of nutrition. One such area is the development of aquaculture. The world aquaculture, as well as other branches of agriculture, has gone through stages of its formation from farming by primitive methods to use of modern technologies. Salmon farming occupies an important place in industrial fish farming, and the volume of farmed products is increasing every year. This review summarises the main milestones in the salmon aquaculture industry, describes the trends that have guided breeding programmes and shows the genetic approaches that have been used in fish farming. The introduction of genetic methods in salmonid fish breeding depends on the level of development of the countries where fish farms are located. Abroad there is a great experience of investment policy aimed at creation and introduction of projects up to the final (operational) stage of introduction of molecular genetics methods into salmon breeding, including the use of genomic selection. While in Russia the concept of genetic improvements in aquaculture is only being discussed. In this review an important place is given to generalisation of the accumulated material on genetic methods of salmonid fish research, as well as directions of application of genetic markers in salmon farming. The issues of searching for candidate genes, quantitative trait loci (QTL) associated with gaining muscle weight and fish resistance to diseases are discussed. The directions and ways of solving breeding problems are shown. Intensive development of scientific approaches to aquaculture development abroad is a vector for setting goals to expand business investment in the development of molecular genetic approaches to salmonid fish breeding in Russia.

A Comparison of Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks.

There is growing interest in the kinematic analysis of human functional upper extremity movement (FUEM) for applications such as health monitoring and rehabilitation. Deconstructing functional movements into activities, actions, and primitives is a necessary procedure for many of these kinematic analyses. Advances in machine learning have led to progress in human activity and action recognition. However, their utility for analyzing the FUEM primitives of reaching and targeting during reach-to-grasp and reach-to-point tasks remains limited. Domain experts use a variety of methods for segmenting the reaching and targeting motion primitives, such as kinematic thresholds, with no consensus on what methods are best to use. Additionally, current studies are small enough that segmentation results can be manually inspected for correctness. As interest in FUEM kinematic analysis expands, such as in the clinic, the amount of data needing segmentation will likely exceed the capacity of existing segmentation workflows used in research laboratories, requiring new methods and workflows for making segmentation less cumbersome. This paper investigates five reaching and targeting motion primitive segmentation methods in two different domains (haptics simulation and real world) and how to evaluate these methods. This work finds that most of the segmentation methods evaluated perform reasonably well given current limitations in our ability to evaluate segmentation results. Furthermore, we propose a method to automatically identify potentially incorrect segmentation results for further review by the human evaluator. Clinical impact: This work supports efforts to automate aspects of processing upper extremity kinematic data used to evaluate reaching and grasping, which will be necessary for more widespread usage in clinical settings.

Digitaria species (Acha): panacea for malnutrition and food insecurity in Nigeria

Acha (Digitaria sp) is a powerhouse of nutrient, filling, versatile, light, and delicious, yet the most underutilized food security crop in Nigeria. The value of the nutritional content (amino acids, protein, starch, crude fibre and ash) present in acha grain is higher than that reported for other cereals such as rice, maize and sorghum and as such, a panacea for malnutrition’. The grain is tolerated by even those with frail digestive systems, including small children, the elderly, and those who are health-conscious. More so, it is a very fast-growing cereal grain and it is considerably cheaper than most staple food crops. Acha is said to be a good food security crop as it blends well with other recipes. It has a texture similar to grits or couscous with a sand colour and due to the primitive conventional processing methods, it contains a load of gritty sand particles. These gritty sand particles can be prevented by threshing over a clean, hard, non-sandy surface. The consumption of acha daily brings you back to the prehistoric era of longevity and wellness.

E-Waste: a Concise Update on Global Movement, Impacts, Management, and Site Remediation

The rapid rise of electronic waste (e-waste) worldwide has become a public health concern. Wealthy countries are disposing of their e-waste to other countries taking advantage of their less stringent environmental laws and regulations. China used to receive large amounts of e-waste through Hong Kong (a free port) but banned the entry of e-waste in 2013. Salvaging or recycling different parts of the e-waste using primitive and uncontrolled techniques generated a wide range of toxic chemicals (mainly heavy metals and persistent organic pollutants). Most studies concerning the environmental and health impacts of the emitted toxic chemicals were conducted in China. The principal aim of this short article is to review the various environmental problems and health impacts of e-waste recycling, policies, management, and remediation of contaminated sites. Out of the primitive methods used for recycling, the two most destructive ones causing harm to the environment and human health are (1) Open-burning of e-waste for disposing of the salvaged e-waste and (2) Acid-stripping of electronic boards for collecting precious metals (gold, silver, platinum). There is sufficient evidence showing the associations between the toxic chemicals in different media (i.e., water, soil/sediment, air) and local food items (i.e., fish, meat, vegetables), linking with body burdens (hair, milk, placenta) of workers and residents. The epidemiological data further demonstrated the abrupt rise of several significant diseases (i.e., respiratory disease, cardiovascular disease, malignant tumors) in Taizhou (China), one of the two e-waste recycling sites. Effective policies and vigorous enforcement in managing e-waste are essential. International cooperation is necessary to prohibit the transboundary movement of e-waste. Sites contaminated by e-waste recycling contain incredibly high concentrations of toxic pollutants, which should be removed using excavation, degradation (via microbes, nanoparticles, biochar), soil washing, etc. Planting appropriate plants with associated rhizospheric microbes would achieve longer-term stability.

Solar cycle prediction using a combinatorial deep learning model

Abstract The long-term prediction of the solar cycle is of great significance for aerospace, communication, and space missions. For a long time, many studies have used relatively primitive deep learning methods to predict the solar cycle, and most of them do not perform well in the long-term prediction. In this paper, we proposed XG-SN ensemble model. The model used extreme gradient boosting (XGBoost) ensemble learning method, combined with sample convolution and interaction net (SCINet) and neural basis expansion analysis for the interpretable time series (N-BEATS) to make predictions for known solar cycles. 13 months of smoothed monthly total sunspot numbers were selected as the dataset. The model performance was evaluated by mean absolute error (MAE), root mean square error (RMSE), and mean absolute time lag (MATL) between the predicted and actual values. The first two evaluation metrics measured the prediction deviation from the numerical dimension, and the last one measured the prediction deviation from the temporal dimension. The results show that the model achieves the MAE, RMSE, and MATL values of 13.19, 17.13, and 0.08, respectively, in Solar Cycle 13 to 24. Our model is able to better predict in most cycles, ensuring accurate prediction of peaks with little time lag.

Alluvial Gold Mining Technologies from Ancient Times to the Present

This article provides a comprehensive and chronological account of the technological advancements in alluvial gold mining. Gold has been a highly prized commodity throughout history and has played a significant role in humanity’s economic and cultural progress. The primitive methods of extracting gold from riverbeds were carried out in Ancient Anatolia and Ancient Greece. The earliest references to the extraction of gold from riverbeds can be traced back to the Argonautic expedition and the myth of the “Golden Fleece”. In this myth, fine gold particles were believed to be trapped within the skin of the fleece, thus alluding to the early practices of gold recovery from rivers. Since then, gold extraction and processing technologies have evolved. During the Roman Empire period, Emperor Augustus made significant advancements in hydraulic gold mining. He developed the “ground sluicing” method, which used water to wash away the overlying soil and expose the bedrock where gold deposits were located. Subsequently, the revolutionary pumps described by Georgius Bauer (Agricola) gained significant popularity during the Renaissance. Additionally, Spanish colonialists in America enhanced their techniques by observing the efficient methods employed by the indigenous populations. Finally, the “gold rush” era was perhaps the most crucial period in alluvial gold mining and took place in the rivers of America during the 19th–20th centuries. Today, technologies have evolved significantly, and exploring the evolution of primitive methods to the present is considered essential.

Kernelized gradient descent method for learning from demonstration

Learning from demonstration (LfD) has been widely studied as a convenient method for robot learning. In the LfD paradigm, the robot is required to learn relevant motion patterns from human demonstrations and apply them to various situations. Many advancements have been achieved in recent studies, however, there is a lack of studies on solutions for robots to learn the variability from demonstrations and adapt the reproduction to unseen scenarios in high-dimensional or long-trajectory cases. In this paper, a novel nonparametric kernelized gradient descent (KGD) method of LfD is proposed, which capitalizes on gradient descent and kernel-based approaches and produces a model with fewer open parameters than methods that employ basis functions. The proposed KGD method can accurately represent the complex demonstrations and utilize the variability of the demonstrations to adapt the trajectories smoothly to different unseen situations described by newly desired start-, via- and end-points precisely with high computational efficiency. Experiments were conducted to evaluate the performance of the proposed KGD method and compare it with commonly used probabilistic kernelized movement primitive (KMP) and mean-prior Gaussian process regression (MP-GPR) methods. The results indicated that the proposed KGD method performs better than KMP and MP-GPR in terms of the precision of the desired points, reproduction smoothness, and computation time. On account of the high efficiency and outstanding reproduction, KGD has the potential to be beneficial for better human–robot collaboration, facilitating assembly lines or improving robot learning. An important future challenge will be to extend the KGD towards considering the nonlinear constraints to learn more complex tasks.

A unified framework for visual domain adaptation with covariance matching

In the field of Machine Learning, it is widely acknowledged that training and test data should ideally stem from the same source and distribution. However, in the real world, this is not always feasible. Domain Adaptation (DA) techniques address this issue by adapting a classifier trained on annotated source domain data on unannotated target domain data while minimizing the impact of domain shift. Many recent DA approaches concentrate on learning a latent feature space that remains invariant to domain shift by mitigating various statistical and geometrical divergences. Although these methods have demonstrated effectiveness, they frequently overlook a crucial aspect of learning a domain-invariant discriminative latent feature space across diverse domains. To address this issue, we propose a novel framework called Unified Framework for Visual Domain Adaptation with Covariance Matching (UDACM) that aims to learn a domain invariant and class discriminative latent feature space by incorporating multiple objectives such as maximizing target domain variance, minimizing distribution and subspace divergence, performing manifold learning, preserving discriminative information of both the source and target domains, and measuring and matching covariance simultaneously. In our proposed framework, the measuring and matching of covariance play a crucial role in ensuring the learning of discriminative latent feature space by aligning the within-class and between-class covariance matrices of the source and target domains simultaneously. Detailed experiments demonstrate the outperformance of UDACM on baseline datasets such as CMU-PIE, Office+Caltech10, USPS+MNIST, VLCS, and Office–Home, compared to various established primitive, shallow, and deep domain adaptation methods on several image classification tasks.

Assessment of Post-Harvest Needs of Small and Medium Scale Gari Processors in Rivers State

Feeding over 7 million Rivers State dwellers requires a radical transformation in postharvest management of agricultural products. Garri, which is a product from cassava is one of the processed food widely consume in the state. However, primitive methods which result to a reasonable loss of the product are widely used in Rivers State. Therefore, in order to minimize or eliminate these losses within the system, the study was conducted to assess the needs of small and medium scale garri processors in Rivers State, Nigeria. Purposive and snow-ball sampling procedures were used to select 120 small and medium scale garri processors in the study area. A well-structured questionnaire was used to obtain information from the respondents and analysed using both frequencies, percentages, mean and PPMC statistical tools. The result revealed that r - distribution which represents the Pearson correlation coefficient significant level of the variables is statistically significant at 1% (0.01) between time constraint and perception of garri processors that, improved garri processing equipment is better than primitive tools with r = 0.258**most of the respondents (Above 90%) confirmed that all the listed improved garri processing and storage equipment are highly needed in the study area. The local tools used in the study area for gari processing includes knives (100%) stick presser (81.7%) and Aluminium frying pan (88.3%). All (100%) of the respondents showed favourable attitude towards the needs for improved processing equipment. The constraints to garri processors include lack of fund, health challenges, and lack of improved garri processing equipment, time consumption, small processing capacity and spoilage of cassava with each mean above grand mean (2.6). Therefore, improved post-harvest equipment are highly needed for processing garri in the study area to improve production efficiency and high-income generation for the processors. Government intervention is needed to erect mini garri processing centre in each community.

CONTRACT FARMING: A REMEDY OF POOR FARMERS IN INDIA

Cost of cultivation of small farmers is increasing due to higher price of input of farming but they are getting profitable price. It is common phenomenon that price of crops decrease during the pick season of farming. So farmers face tremendous problems in marketing of their articles. The problems will be more tragic in case of vegetables since farmers cannot enjoy the facility of cold storage. As a remedy of this great problem the concept of contract farming has raised in 2003-2004 in India. The process of contract farming in India involves scientific and optimum use of land and farm resources for maximum output of agriculture produce. Small farmers practicing primitive agricultural methods for cultivation and harvesting of crops. But with the liberalization of Indian economy, there has been a sudden spurt in contract farming in India. Practice of this farming may bring revolution to the poor farmers by successful implementation of it.

Sharp Feature-Preserving 3D Mesh Reconstruction from Point Clouds Based on Primitive Detection

High-fidelity mesh reconstruction from point clouds has long been a fundamental research topic in computer vision and computer graphics. Traditional methods require dense triangle meshes to achieve high fidelity, but excessively dense triangles may lead to unnecessary storage and computational burdens, while also struggling to capture clear, sharp, and continuous edges. This paper argues that the key to high-fidelity reconstruction lies in preserving sharp features. Therefore, we introduce a novel sharp-feature-preserving reconstruction framework based on primitive detection. It includes an improved deep-learning-based primitive detection module and two novel mesh splitting and selection modules that we propose. Our framework can accurately and reasonably segment primitive patches, fit meshes in each patch, and split overlapping meshes at the triangle level to ensure true sharpness while obtaining lightweight mesh models. Quantitative and visual experimental results demonstrate that our framework outperforms both the state-of-the-art learning-based primitive detection methods and traditional reconstruction methods. Moreover, our designed modules are plug-and-play, which not only apply to learning-based primitive detectors but also can be combined with other point cloud processing tasks such as edge extraction or random sample consensus (RANSAC) to achieve high-fidelity results.

Semantic Segmentation

Semantic segmentation was traditionally performed using primitive methods; however, in recent times, a significant growth in the advancement of deep learning techniques for the same is observed. In this paper, an extensive study and review of the existing deep learning (DL)-based techniques used for the purpose of semantic segmentation is carried out along with a summary of the datasets and evaluation metrics used for the same. The paper begins with a general and broader focus on semantic segmentation as a problem and further narrows its focus on existing DL-based approaches for this task. In addition to this, a summary of the traditional methods used for semantic segmentation is also presented towards the beginning. Since the problem of scene understanding is being vastly explored in the computer vision community, especially with the help of semantic segmentation, the authors believe that this paper will benefit active researchers in reviewing and studying the existing state-of-the-art as well as advanced methods for the same.

COVID-19 advising application development for Apple devices (iOS).

One of humanity's most devastating health crises was COVID-19. Billions of people suffered during this pandemic. In comparison with previous global pandemics that have been faced by the world before, societies were more accurate with the technical support system during this natural disaster. The intersection of data from healthcare units and the analysis of this data into various sophisticated systems were critical factors. Different healthcare units have taken special consideration to advance technical inputs to fight against such situations. The field of natural language processing (NLP) has dramatically supported this. Despite the primitive methods for monitoring the bio-metric factors of a person, the use of cognitive science has emerged as one of the most critical features during this pandemic era. One of the essential features is the potential to understand the data based on various texts and user inputs. The deployment of various NLP systems is one of the most challenging factors in handling the bulk amount of data flowing from multiple sources. This study focused on developing a powerful application to advise patients suffering from ailments related to COVID-19. The use of NLP refers to facilitating a user to identify the present critical situation and make necessary decisions while getting infected. This article also summarises the challenges associated with NLP and its usage for future NLP-based applications focusing on healthcare units. There are a couple of applications that reside for android-based systems as well as web-based chat-bot systems. In terms of security and safety, application development for iOS is more advanced. This study also explains the block meant of an application for advising COVID-19 infection. A natural language processing powered application for an iOS operating system is indeed one of its kind, which will help people who need to advise proper guidance. The article also portrays NLP-based application development for healthcare problems associated with personal reporting systems.

CSE INFO HUB

Whole world and administrators of educational institutionsin our country are concernedabout regularity of student attendance, student’s overall academic performance. Conventionalmethods used until now, were more time consuming and not much fruitful as parents could notkeep tab on their child’s progress. In this paper, basic problem of student management in schools/colleges is defined andthe main objective is to provide computer vision to it. As a prerequisite, various computerizedsystems being developed by using different techniques have been reviewed. This paper focuses on the advancement and modernization of the primitive methods which are being usedtill now in schools and colleges. The main objective is to build a student database system that will store records of students. It is purposed to reduce time spent on administrative tasks. The system is intended to accept process, generate students reports and grades and transcripts accurately. Thesystem is also intended to provide betterservices to users, provide meaningful, consistent, andtimely data and information and finally promotes efficiency by converting paper processes to electronic form. The system is developed using basic technologies such as Java, MySQL withhelp of Wamp Server. The system is free of errors and very efficient and less time consumingdue to the care taken to develop it.

The history of the oil industry in the Fergana Valley during the period of the Kokand Khanate and the colonial era of the Russian Empire

The article examines the history of oil production by primitive methods and by drilling oil wells in the Fergana Valley during the period of the Kokand Khanate and the era of colonialism of the Russian Empire, the development of the oil refining industry in conjunction with all sectors of the national economy. The article is devoted to the history of the development of the oil industry in the Ferghana Valley, the processes of oil production by drilling on the basis of archival documents from the funds of the National State Archives of Uzbekistan, scientific research by foreign and domestic researchers. The formation of the oil industry in the Fergana Valley was carried out in the middle of the 19th century under the Kokand Khanate. By the beginning of the 20th century, oil drilling began. As a result of the introduction of foreign capital into the oil industry, the introduction of new methods, technologies and means of oil production, the use of advanced experience in this industry, this industry has succeeded, reaching a high industrial scale. But during the First World War, the rate of oil production in the Ferghana Valley declined, as in the entire territory of the empire. This was due to the closure of foreign trade routes due to the war, the reduction in the amount of capital invested in this sector, the involvement of skilled oil workers in the war, and the result of the nationalization process after the coup d'état. In the study of the process of formation and formation of the oil industry in the region, comparative, historical and factor analysis was used for a detailed approach to the topic. The conclusions and proposals in the article are developed using current research papers, unique archival materials, and also include the relevant chronology, statistics, periodization and conclusions on the topic.