Search results for: machine learning; medicinal plants

Authors: Sandy Van Vuuren, Lerato Kose, Annah Moteetee

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Lesotho, a country surrounded by South Africa has one of the highest rates of sexually transmitted infections (STI’s) in the world. In fact, the country ranks third highest with respect to infections related to the human immunodeficiency virus (HIV). Despite the high prevalence of STI’s, treatment has been a challenge due to limited accessibility to health facilities. An estimated 77% of the population lives in rural areas and more than 60% of the country is mountainous. Therefore, many villages remain accessible only by foot or horse-back. Thus, the Basotho (indigenous people from Lesotho) have a rich cultural heritage of plant use. The aim of this study was to determine what plant species are used for the treatment of STI’s and which of these have in vitro efficacy against pathogens such as Candida albicans, Gardnerella vaginalis, Oligella ureolytica, and Neisseria gonorrhoeae. A total of 34 medicinal plants were reported by traditional practitioners for the treatment of STI’s. Sixty extracts, both aqueous and organic (mixture of methanol and dichloromethane), from 24 of the recorded plant species were assessed for antimicrobial activity using the minimum inhibition concentration (MIC) micro-titre plate dilution assay. Neisseria gonorrhoeae (ATCC 19424) was found to be the most susceptible among the test pathogens, with the majority of the extracts (21) displaying noteworthy activity (MIC values ≤ 1 mg/ml). Helichrysum caespititium was found to be the most antimicrobially active species (MIC value of 0.01 mg/ml). The results of this study support, to some extent, the traditional medicinal uses of the evaluated plants for the treatment of STI’s, particularly infections related to gonorrhoea.

Keywords: Africa, Candida albicans, Gardnerella vaginalis, Neisseria gonorrhoeae, Oligella urealytica

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Authors: Ahmad Alsheikh Kaddour

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Herbal medicine has been a long-standing phenomenon in Arab countries since ancient times because of its breadth and moderate temperament. Therefore, it possesses a vast natural and economic wealth of medicinal and aromatic herbs. This prompted ancient Egyptians and Arabs to discover and exploit them. The economic importance of the plant is not only from medicinal uses; it is a plant of high economic value for its various uses, especially in food, cosmetic and aromatic industries. It is also an ornamental plant and soil stabilization. The main objective of this research is to study the chemical changes that occur in the plant during the growth period, as well as the production of plant buds, which were previously considered unwanted plants. The research was carried out in the period 2021-2022 in the valley of Al-Shaflah (common caper), located in Qumhana village, 7 km north of Hama Governorate, Syria. The results of the research showed a change in the percentage of chemical components in the plant parts. The ratio of protein content and the percentage of fatty substances in fruits and the ratio of oil in the seeds until the period of harvesting of these plant parts improved, but the percentage of essential oils decreased with the progress of the plant growth, while the Glycosides content where improved with the plant aging. The production of buds is small, with dimensions as 0.5×0.5 cm, which is preferred for commercial markets, harvested every 2-3 days in quantities ranging from 0.4 to 0.5 kg in one cut/shrubs with 3 years’ age as average for the years 2021-2022. The monthly production of a shrub is between 4-5 kg per month. The productive period is 4 months approximately. This means that the seasonal production of one plant is 16-20 kg and the production of 16-20 tons per year with a plant density of 1,000 shrubs per hectare, which is the optimum rate of cultivation in the unit of mass, given the price of a kg of these buds is equivalent to 1 US $; however, this means that the annual output value of the locally produced hectare ranges from 16,000 US $ to 20,000 US $ for farmers. The results showed that it is possible to transform the cultivation of this plant from traditional random to typical areas cultivation, with a plant density of 1,000-1,100 plants per hectare according to the type of soil to obtain production of medicinal and nutritious buds, as well as, the need to pay attention to this national wealth and invest in the optimal manner, which leads to the acquisition of hard currency through export to support the national income.

Keywords: common caper, medicinal plants, propagation, medical, economic importance

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Yang Yung, Tracy Lin Huan

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Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic.

Keywords: machine learning algorithm, correlation clustering mode, cluster overlapping system, glaucoma, kernel density estimation, retinal therapeutic

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Authors: Elena Puica

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This paper aims to investigate the impact of Supply Chain Management (SCM) on the environment by applying a Machine Learning model while pointing out the efficiency of the technology used. The Machine Learning model was used to derive the efficiency and optimization of technology used in SCM and the environmental impact of SCM processes. The model applied is a predictive classification model and was trained firstly to determine which stage of the SCM has more outputs and secondly to demonstrate the efficiency of using advanced technology in SCM instead of recuring to traditional SCM. The outputs are the emissions generated in the environment, the consumption from different steps in the life cycle, the resulting pollutants/wastes emitted, and all the releases to air, land, and water. This manuscript presents an innovative approach to applying advanced technology in SCM and simultaneously studies the efficiency of technology and the SCM's impact on the environment. Identifying the conceptual relationships between SCM practices and their impact on the environment is a new contribution to the research. The authors can take a forward step in developing recent studies in SCM and its effects on the environment by applying technology.

Keywords: machine-learning model in SCM, SCM processes, SCM and the environmental impact, technology in SCM

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Authors: Cristina Vatamanu, Doina Cosovan, Dragos Gavrilut, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through semi-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: ensembles, false positives, feature selection, one side class algorithm

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Authors: Ashok Kumar, Parveen Parveen

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India is bestowed with an extremely high population of plant species with medicinal value and even has two biodiversity hotspots. Indian systems of medicine including Ayurveda, Siddha and Unani have historically been serving humankind across the world since time immemorial. About 1500 plant species have well been documented in Ayurvedic Nighantus as official medicinal plants. Additionally, several hundred species of plants are being routinely used as medicines by local people especially tribes living in and around forests. The natural resources for medicinal plants have unscientifically been over-exploited forcing rapid depletion in their genetic diversity. Moreover, renewed global interest in herbal medicines may even lead to additional depletion of medicinal plant wealth of the country, as about 95% collection of medicinal plants for pharmaceutical preparation is being carried out from natural forests. On the other hand, huge export market of medicinal and aromatic plants needs to be seriously tapped for enhancing inflow of foreign currency. Asparagus racemosus Willd., a member of family Liliaceae, is one of thirty-two plant species that have been identified as priority species for cultivation and conservation by the National Medicinal Plant Board (NMPB), Government of India. Though attention is being focused on standardization of agro-techniques and extraction methods, little has been designed on genetic improvement and selection of desired types with higher root production and saponin content, a basic ingredient of medicinal value. The saponin not only improves defense mechanisms and controls diabetes but the roots of this species promote secretion of breast milk, improved lost body weight and considered as an aphrodisiac. There is ample scope for genetic improvement of this species for enhancing productivity substantially, qualitatively and quantitatively. It is emphasized to select desired genotypes with sufficient genetic diversity for important economic traits. Hybridization between two genetically divergent genotypes could result in the synthesis of new F1 hybrids consisting of useful traits of both the parents. The evaluation of twenty seed sources of Asparagus racemosus assembled different geographical locations of India revelled high degree of variability for traits of economic importance. The maximum genotypic and phenotypic variance was observed for shoot height among shoot related traits and for root length among root related traits. The shoot height, genotypic variance, phenotypic variance, genotypic coefficient of variance, the phenotypic coefficient of variance was recorded to be 231.80, 3924.80, 61.26 and 1037.32, respectively, where those of the root length were 9.55, 16.80, 23.46 and 41.27, respectively. The maximum genetic advance and genetic gain were obtained for shoot height among shoot-related traits and root length among root-related traits. Index values were developed for all seed sources based on the four most important traits, and Panthnagar (Uttrakhand), Jodhpur (Rajasthan), Dehradun (Uttarakhand), Chandigarh (Punjab), Jammu (Jammu & Kashmir) and Solan (Himachal Pradesh) were found to be promising seed sources.

Keywords: asparagus, genetic, genotypes, variance

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Authors: Fatima Bello, Ibrahim Sani

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Medicinal plants are the most important sources of life saving drugs for the majority of world’s population. People of all continents have used hundreds to thousands of indigenous plants in curing and management of many diseases. Sesame (Sesamum indicum L.) is one of the most widely cultivated species for its nutritious and medicinal seeds and oil. This research was carried out to determine the flavonoid content and antioxidant potential of two varieties of sesame seeds oil. Oil extraction was done using Soxhlet apparatus. The percentage oil yield for white and brown seeds were 47.85% and 20.72%, respectively. Flavonoid was present in both seeds with concentration of 480 mg/g and 360 mg/g in white and brown sesame seeds, respectively. The antioxidant potential was determined at different oil volume; 1.00, 0.75, 0.50 and 0.25ml. The results for the white and brown sesame seed oils were 96.8 and 70.7, 91.0 and 65.2, 83.1 and 55.4, 77.9 and 50.2, respectively. The white seed oil has higher oil yield than the brown seed oil. Likewise, the white seed oil has more flavonoid content than the brown seed oil and also better reducing power than the brown seed oil.

Keywords: antioxidant potential, brown sesame seeds, flavonoid content, sesame seed oil, Sesamum indicum L., white sesame seeds

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Authors: Ratun Rahman, Rafid Islam, Akin Ahmed, Kamrul Hasan, Hasan Mahmud

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Malware is becoming more prevalent, and several threat categories have risen dramatically in recent years. This paper provides a bird's-eye view of the world of malware analysis. The efficiency of five different machine learning methods (Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, and TensorFlow Decision Forest) combined with features picked from the retrieval of Android permissions to categorize applications as harmful or benign is investigated in this study. The test set consists of 1,168 samples (among these android applications, 602 are malware and 566 are benign applications), each consisting of 948 features (permissions). Using the permission-based dataset, the machine learning algorithms then produce accuracy rates above 80%, except the Naive Bayes Algorithm with 65% accuracy. Of the considered algorithms TensorFlow Decision Forest performed the best with an accuracy of 90%.

Keywords: android malware detection, machine learning, malware, malware analysis

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

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Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

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Authors: Jawad Alzeer

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Natural products are well recognized as sources of drugs in several human ailments. To investigate the impact of variable extraction techniques on the cytotoxic effects of medicinal plant extracts, 5 well-known medicinal plants from Palestine were extracted with 90% ethanol, 80% methanol, acetone, coconut water, apple vinegar, grape vinegar or 5% acetic acid. The resulting extracts were screened for cytotoxic activities against three different cancer cell lines (B16F10, MCF-7, and HeLa) using a standard resazurin-based cytotoxicity assay and Nile Blue A as the positive control. Highly variable toxicities and tissue sensitivity were observed, depending upon the solvent used for extraction. Acetone consistently gave lower extraction yields but higher cytotoxicity, whereas other solvent systems gave much higher extraction yields with lower cytotoxicity. Interestingly, coconut water was found to offer a potential alternative to classical organic solvents; it gave consistently highest extraction yields, and in the case of S. officinalis L., highly toxic extracts towards MCF-7 cells derived from human breast cancer. These results demonstrate how the cytotoxicity of plant extracts can be inversely proportional to the yield, and that solvent selection plays an important role in both factors.

Keywords: plant extract, natural products, anti cancer drug, cytotoxicity

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Authors: Aldé Belgard Tchicaya Loemba, Baraka Kichonge, Thomas Kivevele, Juma Rajabu Selemani

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Plants used for medicinal purposes are extremely perishable, owing to moisture-enhanced enzymatic and microorganism activity, climate change, and improper handling and storage. Experiments have shown that drying the medicinal plant without affecting the active nutrients and controlling the moisture content as much as possible can extend its shelf life. Different traditional and modern drying techniques for preserving medicinal plants have been developed, with some still being improved in Sub-Saharan Africa. However, many of these methods fail to address the most common issues encountered when drying medicinal plants, such as nutrient loss, long drying times, and a limited capacity to dry during the evening or cloudy hours. Heat pump drying is an alternate drying method that results in no nutritional loss. Furthermore, combining a heat pump dryer with a solar energy storage system appears to be a viable option for all-weather drying without affecting the nutritional values of dried products. In this study, a solar-assisted heat pump dryer integrated with thermal energy storage is developed for drying moringa leaves. The study also discusses the performance analysis of the developed dryer as well as the proximate analysis of the dried moringa leaves. All experiments were conducted from 11 a.m. to 4 p.m. to assess the dryer's performance in “daytime mode”. Experiment results show that the drying time was significantly reduced, and the dryer demonstrated high performance in preserving all of the nutrients. In 5 hours of the drying process, the moisture content was reduced from 75.7 to 3.3%. The average COP value was 3.36, confirming the dryer's low energy consumption. The findings also revealed that after drying, the content of protein, carbohydrates, fats, fiber, and ash greatly increased.

Keywords: heat pump dryer, efficiency, moringa leaves, proximate analysis

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Authors: Meruert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova

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The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers.

Keywords: algorithm, artificial intelligence, education, machine learning

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Authors: Arthur D. Wiedemann, Christopher Fuller, Kyle A. Pascioni

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With the introduction of electric motors, small unmanned aerial vehicle designers have to consider trade-offs between acoustic noise and thrust generated. Currently, there are few low-computational tools available for predicting acoustic noise emitted by a propeller into the far-field. Artificial neural networks offer a highly non-linear and adaptive model for predicting isolated and interactive tonal noise. But neural networks require large data sets, exceeding practical considerations in modeling experimental results. A methodology known as physics guided machine learning has been applied in this study to reduce the required data set to train the network. After building and evaluating several neural networks, the best model is investigated to determine how the network successfully predicts the acoustic waveform. Lastly, a post-network transfer function is developed to remove discontinuity from the predicted waveform. Overall, methodologies from physics guided machine learning show a notable improvement in prediction performance, but additional loss functions are necessary for constructing predictive networks on small datasets.

Keywords: aeroacoustics, machine learning, propeller, rotor, neural network, physics guided machine learning

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Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Shweta Shriwas, Sumeet Dwivedi

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Knowledge of use of traditional medicine is as old as human civilization in almost every system of medicine. Traditional practitioner viz., vaidhayas, ojha, hakim have their own herbal therapy in the treatment of infertility among women’s. Infertility is very common in developed and developing countries due to busy life style of women’s. The present study was initiated with an aim to identify medicinal plants resources from traditional practitioners of Malwa region of Madhya Pradesh to treat infertility. An ethnomedicinal study of Malwa region viz., Indore, Dewas, Ratlam, Ujjain, Dhar, Mandsour and Neemuch of Madhya Pradesh, India comprising fifty-seven study site was conducted during Jan-217 to June-2017. During the course of present investigation, the traditional use of medicinal plants for infertility in women was revealed by traditional practitioner. The botanical name, family, local name, part used, habit along with mode of their administration and dose duration were enumerated.

Keywords: herbal medicine, infertility, traditional, Malwa, Madhya Pradesh

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Authors: Ahmed Al-Adaileh

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Renewable energy sources, domestic storage systems, controllable loads and machine learning technologies will be key components of future smart homes management systems. An energy management scheme that uses a Deep Learning (DL) approach to support the smart home management systems, which consist of a standalone photovoltaic system, storage unit, heating ventilation air-conditioning system and a set of conventional and smart appliances, is presented. The objective of the proposed scheme is to apply DL-based machine learning to predict various running parameters within a smart home's environment to achieve maximum comfort levels for occupants, reduced electricity bills, and less dependency on the public grid. The problem is using Reinforcement learning, where decisions are taken based on applying the Continuous-time Markov Decision Process. The main contribution of this research is the proposed framework that applies DL to enhance the system's supervised dataset to offer unlimited chances to effectively support smart home systems. A case study involving a set of conventional and smart appliances with dedicated processing units in an inhabited building can demonstrate the validity of the proposed framework. A visualization graph can show "before" and "after" results.

Keywords: smart homes systems, machine learning, deep learning, Markov Decision Process

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Authors: Ira V. Stancheva, Ely G. Zayova, Maria P. Geneva, Marieta G. Hristozkova, Lyudmila I. Dimitrova, Maria I. Petrova

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The study describes a highly efficient and low-cost protocol for rapid and mass in vitro propagation of medicinal and aromatic plant species (Hyssopus officinalis L., Lamiaceae). Hyssop is an important aromatic herb used for its medicinal values because of its antioxidant, anti-inflammatory and antimicrobial properties. The protocol for large-scale multiplication of this aromatic plant was developed using young stem tips explants. The explants were sterilized with 0.04% mercuric chloride (HgCl₂) solution for 20 minutes and washing three times with sterile distilled water in 15 minutes. The cultural media was full and half strength Murashige and Skoog medium containing indole-3-butyric acid. Full and ½ Murashige and Skoog media without auxin were used as controls. For each variant 20 glass tubes with two plants were used. In each tube two tip and nodal explants were inoculated. Maximum shoot and root number were obtained on ½ Murashige and Skoog medium supplemented with 0.1 mg L-1 indole-3-butyric acid at the same time after four weeks of culture. The number of shoots per explant and shoot height were considered. The data on rooting percentage, the number of roots per plant and root length were collected after the same cultural period. The highest percentage of survival 85% for this medicinal plant was recorded in mixture of soil, sand and perlite (2:1:1 v/v/v). This mixture was most suitable for acclimatization of all propagated plants. Ex vitro acclimatization was carried out at 24±1 °C and 70% relative humidity under 16 h illuminations (50 μmol m⁻²s⁻¹). After adaptation period, the all plants were transferred to the field. The plants flowered within three months after transplantation. Phenotypic variations in the acclimatized plants were not observed. An average of 90% of the acclimatized plants survived after transferring into the field. All the in vitro propagated plants displayed normal development under the field conditions. Developed in vitro techniques could provide a promising alternative tool for large-scale propagation that increases the number of homologous plants for field cultivation. Acknowledgments: This study was conducted with financial support from National Science Fund at the Bulgarian Ministry of Education and Science, Project DN06/7 17.12.16.

Keywords: Hyssopus officinalis L., in vitro culture, micro propagation, acclimatization

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: Shael Brown, Reza Farivar

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Persistence diagrams capture valuable topological features of datasets that other methods cannot uncover. Still, their adoption in data pipelines has been limited due to the lack of publicly available tools in R (and python) for analyzing groups of them with machine learning and statistical inference. In an easy-to-use and scalable R package called TDApplied, we implement several applied analysis methods tailored to groups of persistence diagrams. The two main contributions of our package are comprehensiveness (most functions do not have implementations elsewhere) and speed (shown through benchmarking against other R packages). We demonstrate applications of the tools on simulated data to illustrate how easily practical analyses of any dataset can be enhanced with topological information.

Keywords: machine learning, persistence diagrams, R, statistical inference

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Authors: Aishi Kundu, Elhan Raza

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"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

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Authors: Mesnan Silalahi, Dana Indra Sensuse, Indra Budi

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Many research have revealed the fact of the complexity of ontology building process that there is a need to have a new approach which addresses the socio-technical aspects in the collaboration to reach a consensus. Meta-design approach is considered applicable as a method in the methodological model in a socio-technical ontology engineering. Principles in the meta-design framework is applied in the construction phases on the ontology. A portal is developed to support the meta-design principles requirements. To validate the methodological model semantic web applications were developed and integrated in the portal and also used as a way to show the usefulness of the ontology. The knowledge based system will be filled with data of Indonesian medicinal plants. By showing the usefulness of the developed ontology in a web semantic application, we motivate all stakeholders to participate in the development of knowledge based system of medicinal plants in Indonesia.

Keywords: socio-technical, metadesign, ontology engineering methodology, semantic web application

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Authors: Md. Nuru Miah, A. F. M. Akhter Uddin

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Medicinal plants are grown naturally in a tropical environment in Bangladesh and used as drug and therapeutic agents in the health care system. According to Bangladesh Agricultural Research Institute (BARI), there are 722 species of medicinal plants in the country. Of them, 255 plants are utilized by the manufacturers of Ayurvedic and Unani medicines. Medicinal plants like Aloevera, Ashwagonda, shotomul,Tulsi, Vuikumra, Misridana are extensively cultivated in some selected areas as well; where Aloevera scored the highest position in production. In the early 1980, Ayurvedic and Unani companies procured 80 percent of medicinal plants from natural forests, and the rest 20 percent was imported. Now the scenario has changed; 80 percent is imported, and the rest 20 percent is collected from local products(Source: Astudy on sectorbased need assessment of Business promotion council-Herbal products and medicinal plants, page-4). Uttara Development Program Society, a leading Non- Government development organization in Bangladesh, has been implementing a value chain development project under promoting Agricultural commercialization and Enterprises of Pally Karma Sahayak Foundation (PKSF) funded by the International Fund for Agricultural Development (IFAD) in Natore Sadar Upazila from April 2017 to sustainably develop the technological interventions for products and market development. The ultimate goal of the project is to increase income, generate employment and develop this sector as a sustainable business enterprise. Altogether 10,000 farmers (Nursery owners, growers, input supplier, processors, whole sellers, and retailers) are engaged in different activities of the project. The entrepreneurs engaged in medicinal plant cultivation did not know and follow environmental and good agricultural practices. They used to adopt traditional methodology in production and processing. Locally the farmers didn’t have any positive initiative to expand their business as well as developvalue added products. A lot of diversified products could be possible to develop and marketed with the introduction of post-harvest processing technology and market linkage with the local and global buyer. Training is imparted to the nursery owners and herbal growers on production technologies, sowing method, use of organic fertilizers/compost/pesticides, harvesting procedures, and storage facilities. Different types of herbal tea like Rosella, Moringa, Tulshi, and Basak are being produced and packed locally with a good scope of its marketing in different cities of the country. The project has been able to achieve a significant impact in the development of production technologies, but still, there is room for further improvement in processing, packaging, and marketing level. The core intervention of the current project to develop some entrepreneurs for branding, packaging, promotion, and marketing while considering environment friendly practices. The present strategies will strengthen the knowledge and skills of the entrepreneurs for the production and marketing of their products, maintaining worldwide accepted compliance system for easy access to the global market.

Keywords: aloe vera, herbs and shrubs, market, interventions

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Authors: Matteo Calabrese, Martin Cimmino, Dimos Kapetis, Martina Manfrin, Donato Concilio, Giuseppe Toscano, Giovanni Ciandrini, Giancarlo Paccapeli, Gianluca Giarratana, Marco Siciliano, Andrea Forlani, Alberto Carrotta

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In this paper we describe a machine learning methodology for Predictive Maintenance (PdM) applied on woodworking industrial machines. PdM is a prominent strategy consisting of all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the challenges with PdM approach is to design and develop of an embedded smart system to enable the health status of the machine. The proposed approach allows screening simultaneously multiple connected machines, thus providing real-time monitoring that can be adopted with maintenance management. This is achieved by applying temporal feature engineering techniques and training an ensemble of classification algorithms to predict Remaining Useful Lifetime of woodworking machines. The effectiveness of the methodology is demonstrated by testing an independent sample of additional woodworking machines without presenting machine down event.

Keywords: predictive maintenance, machine learning, connected machines, artificial intelligence

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Authors: Areej Ali Baeshen

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Allelopathy is a complex phenomenon that depends on the concentration of allelochemicals. It has both inhibitory and stimulatory effects, which may be decided by concentration of allelochemicals present in extraction. In the present study, the allelopathic effects of Eruca sativa, Mentha peperina, and Coriandrum sativum water extract prepared by grinding fresh leaves of the medicinal plants in distilled water and three concentrations were taken from the crude extracts (100%, 50% and 25% in addition to 0% as control), and were tested for their effects on seed germination and some growth parameters of Zea mays. The experiment was conducted in sterilized Petri dishes under the natural laboratory conditions at temperature of 25°C, with a 24 h, 48 h, 72 h, 96 h and 120 h time interval for seed germination and 24 h, 48 h and 72 h for radicle length. The effects of different concentrations of aqueous extract were compared to distilled water (control, 0%). In maize, germination percentage was suppressed when plants was treated with 100% extracts, however, 50% and 25% of M. peprina increased germination percentage by 4 times more than the control. Moreover, 50% and 25% extracts of M. peperina and 50% of C. sativum increased maize radicle and plumule length by 3 to 4 times that of the control. Results of plumule fresh and dry weights revealed that concentrations of water extracts of 100% and 50% M. peperina, E. sativa 100% and E. sativa 50% reported almost similar plumule fresh weight as in control plants. The most interesting finding is the reduction in harmful salts and TDS which could be a good factor in saline soils of Saudi Arabia.

Keywords: Zea mays, Eruca sativa, Mentha peperina, Coriandrum sativum, medicinal plants, allelochemicals, aqueous extract

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Authors: Mais Khader, Xingjie Wei

Abstract:

This research sheds light on female entrepreneurs by providing new insights on the survival predictions of companies led by females in the UK. This study aims to build a predictive machine learning model of the survival of female-led & co-led small & medium enterprises (SMEs) in the UK over the period 2000-2020. The predictive model built utilised a combination of financial and non-financial features related to both companies and their directors to predict SMEs' survival. These features were studied in terms of their contribution to the resultant predictive model. Five machine learning models are used in the modelling: Decision tree, AdaBoost, Naïve Bayes, Logistic regression and SVM. The AdaBoost model had the highest performance of the five models, with an accuracy of 73% and an AUC of 80%. The results show high feature importance in predicting companies' survival for company size, management experience, financial performance, industry, region, and females' percentage in management.

Keywords: company survival, entrepreneurship, females, machine learning, SMEs

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

Abstract:

Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

Abstract:

Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: Naduni Ranasinghe

Abstract:

E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

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Authors: Samina Khalid, Shamila Nasreen

Abstract:

Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented.

Keywords: age related macular degeneration, feature selection feature subset selection feature extraction/transformation, FSA’s, relief, correlation based method, PCA, ICA

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Authors: Karthik Mittal

Abstract:

This study performs an unsupervised machine learning analysis to find clusters of related SNPs which highlight biological pathways that are important for the biological mechanisms of breast cancer. Studying genetic variations in isolation is illogical because these genetic variations are known to modulate protein production and function; the downstream effects of these modifications on biological outcomes are highly interconnected. After extracting the SNPs and their effect on different types of breast cancer using the MRBase library, two unsupervised machine learning clustering algorithms were implemented on the genetic variants: a k-means clustering algorithm and a hierarchical clustering algorithm; furthermore, principal component analysis was executed to visually represent the data. These algorithms specifically used the SNP’s beta value on the three different types of breast cancer tested in this project (estrogen-receptor positive breast cancer, estrogen-receptor negative breast cancer, and breast cancer in general) to perform this clustering. Two significant genetic pathways validated the clustering produced by this project: the MAPK signaling pathway and the connection between the BRCA2 gene and the ESR1 gene. This study provides the first proof of concept showing the importance of unsupervised machine learning in interpreting GWAS summary statistics.

Keywords: breast cancer, computational biology, unsupervised machine learning, k-means, PCA

Procedia PDF Downloads 120