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Rice (Oryza sativa) is India’s major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy.

In present research, the strength properties, impact resistance, and durability characteristics of high-strength concrete blended with nanosilica (NS) and reinforced with multi-walled carbon nanotubes (MWCNTs) are discussed. The proportion consists of nanosilica added in a constant addition of 1% and MWCNT added in a varied dosage of 0.025%, 0.05%, 0.1%, 0.15%, and 0.2% by weight of the cement. A total of 11 mixes were made including the control mix having no MWCNT. The other 10 mixes were categorized into two classes with one class having steel fibres incorporated as 1% of the total volume of the concrete along with the other ingredients such as 1% NS and different proportions of MWCNT. The other class was made without steel fibres retaining only the NS and different MWCNT proportions. Besides the standard compression and tension tests, to determine the energy absorbing capacity of the mix specimens, impact test was also performed. The strength tests were carried out for 3, 7, and 28-day curing. Also, durability tests were carried out with sorptivity, porosity, and mass loss of the specimens when exposed to aggressive HCL and H2SO4 acid. To validate the experiment results, microstructure studies such as scanning electron microscopy (SEM) were also conducted on the samples. Among all mixes, 28-day compressive strength (CS) of 0.2% MWCNT with 1% NS and 1% steel fibre mix was found to increase by 22% compared to control concrete.

The recent increasing consciousness for environment sustainability and health hazards is the motive for looking for alternative environment-friendly materials and manufacturing processes. Therefore, environmental compatibility and safety to the health manufacturing process are a worldwide concern. The use of excessive and toxic synthetic dyes in textile and apparel manufacturing processes lineages environment pollution and health hazards to the surroundings where they are sited. Consequently, penetrating for alternative eco-friendly dyes has been initiated in recent times by researchers and development motivators in order to reduce the excessive use of synthetic dyes. In this regard, natural indigo dye from the indigo plant is getting attention, and thus, it is preferred as good as synthetic indigo dye, owing to its inherited dyeing property. With natural indigo dye, it can be achieved eco-friendly dyeing without the application of mordant attaining commercially acceptable fastness properties. Owing to the popularity of blue jeans around the world, especially those that are naturally dyed indigo blue, indigo dye consumption is now rising. Furthermore, currently, natural indigo dye is demanding for functional textile finishing, cosmetics and food colouration, UV protection and deodorizing agents, and other applications. In general, because of its increasing demand and wide applications, natural indigo has become an important cash crop and is traded in the international market with a price range of 35 to 40 USD per kilogram as per its purity level. Indigo dye has therefore been revived with more applications than it had before the advent of man-made colourants. The revival of indigo presents a chance for underdeveloped nations with incorporated economic and social benefits. Ethiopia as a developing country and situated in tropical Africa will benefit from indigo revival because of its potentials with having a suitable climate, a young workforce, enough land resource, and its booming textile and apparel industries.

Agricultural productivity was negatively impacted by low soil fertility and uneven fertilizer application during crop cultivation in Ethiopia. Because of this, important crops frequently respond to fertilizer applications significantly below their achievable and potential yields. This study was carried out to determine the most sorghum yield-limiting nutrients in the Raya Kobo area of the Amhara Region in the 2020/21 crop season. Sorghum variety Girana-One was used as the test crop. Control, NPS, PSBZn, NPBZn, NSBZn, NPSB, NPSZn, NPSBZn, recommended NP, and NPSKBZn were treatments. Three replications of the experiment were used in a randomized complete block design. Before treatment application, a composite soil sample was collected at a depth of 0–20 cm to determine the soil’s physicochemical properties. To evaluate N and P uptakes, samples of sorghum stalk and grain were collected. SAS software was used to analyze the data. Results showed that, NPKSZnB produced a considerably greater grain yield (4620 kg·ha−1), whereas the control and N omitted plots produced the lowest grain yields (2759 kg·ha−1) and 2805 kg·ha−1, respectively. Nitrogen fertilizer missing plots showed a statistically significant yield drop compared to the other plots, and there was no statistically significant yield difference between the prescribed NP plots and the potassium, sulfur, boron, or zinc omitted plots. The plots treated with NPKSZnB had the highest agronomic efficiency for N (19.7 kg grain kg−1·N) and P (10.6 kg grain kg−1 P2O5). Therefore, research and development should therefore concentrate on nitrogen to achieve the best sorghum yield for the study location. Phosphorus might also be used to keep the fertility level within the ideal range.

CBR is a crucial metric used to assess the durability of base course materials and subgrade soils in various types of pavements. In this research, the machine learning (ML) approach has been implemented using random forest (RF), decision tree (DT), linear regression (LR), and artificial neural network (ANN) models to estimate CBR (California bearing ratio) values of the soil based on seven predictors such as maximum dry density, soil classification, optimum moisture content, liquid limit, plastic limit, plastic index, and swell, which can be easily determined from the laboratory. AASHTO M 145 was used to categorize 252 soil samples that formed the basis of an experimental data set. In this model study, the data were split into 20% test data and 80% training data. Standard statistical measures including coefficient of determination, correlations, and errors were used to assess the effectiveness of the models such as MSE (mean squared error), MAE (mean absolute error), and RMSE (root mean square error). From these evaluation metrics, the random forest algorithm gets a smaller error and larger relative error (R2) value to compare with other algorithms. Therefore, it can be concluded that a random forest algorithm based on the analysis findings can accurately forecast the soil’s CBR.

Background. Self-care practices are an important part of heart failure patient management and essential to control symptoms of the disease and its exacerbation. However, poor adherence to these self-care behaviors could be associated with an increase in hospitalization, morbidity, and mortality. Even if it is an important part of management for heart failure patients, yet information is not adequate in the study area about adherence to self-care recommendations and associated factors among heart failure patients. Purpose. To assess self-care recommendation adherence and associated factors among heart failure patients in West Gojjam Zone public hospitals. Methods. Institutional-based cross-sectional study was conducted on 304 selected heart failure patients attending follow-up at public hospitals in West Gojjam Zone from March 16 to April 16, 2021. Consecutive sampling technique based on patient arrival with proportional allocation to each hospital was employed to select the study participants. Data were collected through face-to-face interview and reviewing patients’ medical records. Data were entered into EpiData version 3.1 and analyzed using Statistical Package for Social Sciences (SPSS) version 25. Binary logistic regression model was fitted to assess the association between adherence to self-care recommendations and associated factors. P value < 0.05 with 95% confidence interval (CI) was considered to declare a statistically significant association in multivariable logistic regression. Results. In this study, 304 patients participated with a response rate of 97.4%. Only 32.9% of them had good adherence to self-care recommendations. Having good knowledge on heart failure (adjusted odds ratio AOR = 4.6 ; 95% CI: 1.82, 11.86), no depression ( AOR = 6.1 ; 95% CI: 1.92, 19.37), having strong social support ( AOR = 3.57 ; 95% CI: 1.56–8.33), age 30-49 years ( AOR = 3.37 ; 95% CI: 1.14, 9.89), and college and above level of education ( AOR = 6.17 ; 95% CI: 1.22, 31.25) were factors significantly associated with good adherence to self-care recommendations. Conclusion. This study showed that most of the heart failure patients had poor adherence to self-care recommendations. Policymakers and other stakeholders should develop and implement appropriate strategies to increase patients’ adherence level to self-care recommendations by emphasizing on addressing identified factors.

Background. An obstetric fistula is an abnormal opening between the vagina, rectum, and/or bladder. Obstetric fistula has a devastating impact on women’s physical, social, and psychological health. Despite the numerous health consequences in developing countries, including Ethiopia, there have been few studies on the determinants of time to recovery from obstetric fistula. Therefore, this study is aimed at addressing the gap. Methods. A retrospective cohort study was employed to include 328 randomly selected records of women admitted for obstetric fistula treatment at Mekelle Hamlin Fistula Center from January 2015 to 2020. Data collected from the medical records was coded and entered into SPSS software version 20 and exported to STATA 10 and R statistical software for data cleaning and data analysis. The Kaplan-Meier and log-rank tests were computed to explore the data. The log-logistic inverse Gaussian shared frailty model was employed using a 95% CI, and variables with a p value < 0.05 were declared as determinants of recovery time. Results. Of 328 fistula patients, 293 (89.33%) were physically cured. The Kaplan-Meier result showed that the overall mean and median survival time of time to recovery from obstetric fistula patients at Mekelle Hamlin Fistula Center is 42 and 33 days, respectively. In a log-logistic inverse Gaussian shared frailty model analysis, extensive fistula size (AHR : 1.282; 95% CI = 1.175-1.388), secondary and above education level (AHR : 0.830; 95% CI = 0.693-0.967), rural residence (AHR : 1.357; 95% CI = 1.236-1.479), and physiotherapy use ( AHR : 0.801 , 95% CI = 0662 -0.940, 95% CI = 1.175 -1.388) were statistically significant predictors of recovery from obstetrics fistula. Conclusion. Rural place of residence, home delivery, and large and extensive size of the fistula prolong the timing of healing from the obstetric fistula. However, having tall height, physiotherapy treatment, secondary and above-educated women, and RVF type of fistula has a short time of healing for obstetric fistula in Mekelle Hamlin Fistula Center. Therefore, we recommend that health professionals promote institutional delivery and physiotherapy, shorten the duration of catheterization, and manage urine incontinence. In addition, we recommend that the regional health bureau promotes female education and pregnancy after 18 years. The survival probability of patients with obstetric fistulas is better predicted by the log-logistic inverse Gaussian shared frailty model. Therefore, it would be good for future researchers to take this model into account.

Background. High compliance is crucial for the success of a mass drug administration program to achieve lymphatic filariasis elimination. However, the presence of persistently noncompliant individuals might delay the elimination target. Besides, although context-based research is essential to designing effective strategies, only a few studies have focused on identifying factors that play a role in noncompliance with mass drug administration in Africa. Therefore, this study was conducted to identify the factors associated with noncompliance to prevent mass drug administration using ivermectin-with-albendazole for the elimination of lymphatic filariasis in Northwest Ethiopia. Methods. A case-control study was conducted in Jawi District, Northwest Ethiopia. All individuals who are permanently living in the study area and registered on the annual chemotherapy registration book since 2015 were included in this study. A two-proportion formula was used to estimate the required sample size and 348 cases and 348 controls were selected by identification number on the village chemotherapy registration book using a systematic sampling technique. Data were collected by face-to-face interviews using a structured questionnaire developed through an intensive literature review. Then, data were entered and cleaned by using the EPI DATA software, and analyses were conducted using SPSS version 26. Finally, a logistic regression analysis technique was applied to identify the risk factors using adjusted odds ratio as measures of effect. Results. A total of 690 (99.1%) participants, 345 cases and 345 controls, were included in the study. Younger age (AOR = 1.60; 95%CI: 1.10, 2.33), female sex (AOR = 1.56; 95%CI: 1.24, 3.93), thought of not being susceptible to the disease (AOR = 2.36, 95%CI: 1.80, 4.32), lack of disease knowledge (AOR = 1.88; 95% CI: 1.38, 3.81), fear of drug side effect (AOR = 2.45; 95% CI:1.23, 4.86), and not participating in community drug distributors selection (AOR = 2.58; 95% CI: 1.70, 3.91) were found to be the risk factors significantly associated with noncompliance. Conclusion. Noncompliance with lymphatic filariasis mass drug administration therapy was associated with specific demographic, individual, program, and drug delivery characteristics. This finding has important implications for program effectiveness and would be used to accelerate the elimination of lymphatic filariasis in the study area and other endemic settings.

Background. Viral hepatitis are considered as the cause of solemn health problem for the human kind, particularly among pregnant women in the 21th century. Therefore, this study is aimed at determining the seroprevalence of HBV and HCV infection among pregnant women attending at Borumeda General Hospital, Dessie, Northeast Ethiopia. Methods. An institution-based cross-sectional study was conducted at Borumeda General Hospital from April to May, 2020. A consecutive total of 124 pregnant women who were attending at the antenatal clinic (ANC) of the hospital were included. A structured questionnaire was used to assess the associated factors and some sociodemographic characteristics. Five milliliters of venous blood was collected from each study participant, and a laboratory test using a rapid HBsAg and anti-HCV kit was done. The data were analyzed using SPSS software version 22. Results. The mean age of the study subjects was 25.81 (±5.967) years. The overall seroprevalence of either HBV or HCV infections among the study participants was 14 (11.3%). HBsAg and anti-HCV were positive among 10 (8.1%) and 4 (3.2%) study participants, respectively. There was no coinfection result between HBV and HCV among pregnant women. Pregnant women who had abortion history [AOR 5.723; 95% CI 1.100-29.785, P value = 0.038] and hospitalization history with IV medication [AOR 6.939; 95% CI 1.017-47.322, P value = 0.048] exhibited statistically significant association with HBV infection. Conclusions. Seroprevalence of HBV and HCV infections among pregnant women was high, and the rate of HBV particularly can be considered in the high endemic category of the WHO classification scheme. Continuous screening of pregnant mothers, provision of hepatitis B vaccine for females at the child-bearing age, and health education to create awareness about HBV and HCV should be implemented.

Using enhanced ant colony optimization, this study proposes an efficient heuristic scheduling technique for cloud infrastructure that addresses the issues with nonlinear loads, slow processing complexity, and incomplete shared memory asset knowledge that plagued earlier resource supply implementations. The cloud-based planning architecture has been tailored for dynamic planning. Therefore, to determine the best task allocation method, a contentment factor was developed by integrating these three objectives of the smallest waiting period, the extent of commodity congestion control, and the expense of goal accomplishment. Ultimately, the incentive and retribution component would be used to modify the ant colony calculation perfume-generating criteria that accelerate a solution time. In particular, they leverage an activity contributed of the instability component to enhance the capabilities of such a method, and they include a virtual desktop burden weight component in the operation of regional pheromone revamping to assure virtual computers’ immense. Experiences with the routing protocol should be used to explore or demonstrate the feasibility of our methodology. In comparison with traditional methods, the simulation results show that the proposed methodology has the most rapid generalization capability, and it has the shortest duration of the project, the most distributed demand, and the best utilization of the capabilities of the virtual computer. Consequently, their hypothetical technique of optimizing the supply of resources exceeds world competition.

Introduction. In Ethiopia, traditionally, the leaves of Calpurnia aurea have been utilized to treat peptic ulcer disease. Therefore, the objective of the present study was to examine the antiulcer activity of Calpurnia aurea hydromethanolic leaf extract and solvent fractions in rats. Methods. The ulcer-healing potential of the crude test extract was assessed in rats by adopting pyloric ligation-, acidified ethanol-, and acetic acid-induced ulcer methods; while, in solvent fractions, the acidified ethanol-induced ulcer model was used. In all models, three serial test doses (100, 200, and 400 mg/kg) were given and the antiulcer activity was investigated. Standard drugs like sucralfate (100 mg/kg), omeprazole (20 mg/kg), and cimetidine (100 mg/kg) have been used as a positive control; whereas distilled water (10 mL/kg) was used as the negative control. Parameters like ulcer index, total acidity, pH, gastric volume, and gastric mucin level were all measured. Results. In an acute toxicity study, the test extract at the limit test dose (2 g/kg) was safe following a single dose administration. In pyloric ligation-induced ulcers, the plant extract at 200 and 400 mg/kg significantly reduced the ulcer index, the volume of stomach secretion, and total acidity while raising gastric pH and mucus content (P<0.05). Likewise, in the acidified ethanol- and acetic acid-induced ulcer models, the extract at both test doses (200 and 400 mg/kg) also displayed a substantial reduction (P<0.05) in ulcer index. Among the fractions, the ethyl acetate fraction revealed remarkable cytoprotective activity at all test doses and the aqueous fraction at 400 mg/kg (P<0.05). In contrast, the effect of chloroform fraction was found to be negligible. The peak ulcer inhibition was noted at 400 mg/kg of ethyl acetate fraction (52.4%). Conclusion. The study showed that the crude extract and solvent fractions possess remarkable antiulcer activity.

Background. Maternal mortality is unacceptably high. About 295,000 women died during and following pregnancy and childbirth in 2017. The vast majority of these deaths (94%) occurred in low-resource settings, and most could have been prevented. Methods. This research is based on a cross-sectional study using 2016 EDHS data. The analysis included 7,590 women who had given birth in the five years prior to the survey. Clusters with high and low hot spots with institutional delivery were found using SatScan spatial statistical analysis. A multilevel multivariable mixed-effect logistic regression was utilized to discover characteristics associated with institutional delivery. Result. In this study, 33.25% of women who gave birth in the last 5 years preceding the survey delivered their babies at health institutions. The finding also indicated that the spatial distribution of institutional delivery was nonrandom in the country. Variables achieving statically significant association with utilization of institutional delivery were as follows: at the individual level, richness (AOR = 2.18, 95%CI: 1.39–3.41), higher education (AOR = 3.89, 95%CI: 1.51–10.01), a number of antenatal care visits of four and above (AOR = 6.57, 95%CI: 4.83–8.94), and parity of more than two children (AOR = 0.48, 95%CI: 0.34–0.68); at the community level, higher education (AOR = 1.70, 95%CI: 1.22–2.36) and urban residence (AOR = 5.30, 95%CI: 3.10–9.06) were variables that had achieved statically significant association for utilization of institutional delivery. Conclusions. This study identified a spatial cluster of institutional delivery with the Somali and Afar region having low utilization rates and Addis Ababa and Tigray regions having the highest utilization rates. The significant individual factors associated with institution delivery were woman antenatal care visits, household wealth index, maternal education, and parity, and the significant community ones were region, place of residence, and educational status. Therefore, to maximize health facility delivery in Ethiopia, the predictors of institutional delivery identified in this study should be given more attention by governmental and nongovernmental stakeholders.

The purpose of this study was to analyze the roles of instructors and students to implement cooperative learning in eastern Ethiopian higher learning institutions. Using a survey design, purposive and stratified random sampling techniques were used to select 1244 respondents (students, instructors, college deans, department heads, and cooperative learning coordinators) from three universities. Data were collected using questionnaires, interviews, and observations. The mean, standard deviation, and ANOVA were used for analysis of the data. The results revealed that instructors were not acting as facilitators and were not offering guidance on how students should collaborate. Almost every instructor placed an emphasis on the lecture method. As evidenced by the data, the majority of instructors have a solid understanding of cooperative learning. However, owing to heavy workloads, large class sizes, and other factors, cooperative learning was not implemented adequately. Students failed to take ownership of their own learning and failed to share their information, talents, and experiences with one another. The constraints to implementing cooperative learning were inadequate responsibility sharing, inequitable work assignments, and a lack of consideration for diverse ability grouping. As a result, instructors must understand how to arrange learning settings in such a way that positive interdependence, individual accountability, proper use of social skills, and group processing are promoted. Moreover, program leaders should strengthen their monitoring and support of the educational process to ensure that instructors and students practice cooperative learning. It is critical for university quality assurance experts to plan and deliver training on cooperative learning and its implementation.

Materials discovery is usually done using high-throughput computational screening. The use of costly and complex direct density functional theory (DFT) simulation methods has been commonly used to determine subtle trends in spin-state ordering and inorganic bonding of inorganic materials and, in general, to predict the electronic structure properties of transition metal complexes. A Gaussian process regression (GPR) framework consisting of four kernel functions is introduced for spin-state splitting estimation through inorganic chemistry-appropriate empirical inputs. To this end, the present study reviewed an extensive range of data values from earlier works. According to statistical analysis, the GPR model showed very good performance. The coefficients of determination were calculated to be 0.986 for the exponential and Matern kernel functions, suggesting the highest predictive power of these methods. Moreover, the sensitivity of output to inputs was measured. Artificial intelligence (AI) helped accurately predict the target values through various input ranges.

Higher education institutions are responsible for providing their graduates with relevant job skills that will allow them to compete in the labour market. With this in mind, the purpose of this study was to identify the factors associated with graduate unemployment in Ethiopia. The data were collected, analysed, and interpreted using a quantitative design. Using Cochran’s sampling formula, 359 graduates from five regions were selected as the sources of primary data. Multinomial logistic regression was used to analyse the data because it is a good model for computing the interaction between more than two independent and dependent variables. Cronbach’s alpha coefficient of 0.947 indicated that the instrument’s reliability is adequate. The study revealed that the independent variables that correlate with graduates’ unemployment are demographic characteristics, curriculum, institutional characteristics, graduate characteristics, and economic and labour market conditions. These findings imply that HEIs and governance bodies should reconsider the sector’s policy and strategic directions in terms of graduates’ employability output. As one of the major improvements, the study also recommends creating an enabling environment for employers to thrive, while higher education institutions adjust their curricula to meet the needs of employers.

Background. Tetanus is a bacterial disease caused by the Clostridium tetani, which is a highly fatal, noncommunicable, and toxin-mediated disease. Globally, maternal and neonatal tetanus is a public health problem due to low maternal tetanus toxoid immunization. Ethiopia has the highest neonatal mortality and morbidity related to tetanus due to low tetanus toxoid immunization and the high number of home deliveries. The main objective of this systematic review and meta-analysis was to estimate the pooled coverage of at least two doses of tetanus toxoid immunization, and the pooled effect sizes of associated factors in Ethiopia. Methods. Primary studies for this review were searched from the PubMed/MEDLINE online, ScienceDirect, Hinari, Google, and Google Scholar databases. Primary articles published from 2010 up to August 30, 2020, were included in this meta-analysis. Data were extracted in Microsoft Excel format and exported to STATA Version 14.0. A random-effects meta-analysis model was used to estimate the pooled coverage of two or more tetanus toxoid immunizations and its associated factors. Heterogeneity was evaluated by the I 2 test. Egger’s weighted regression test was used to assess publication bias. Results. We retrieved 212 records; of these, 199 articles were excluded for reasons. Finally, 14 studies were included in this meta-analysis. The pooled estimate of receiving at least two doses of tetanus toxoid immunization coverage in Ethiopia was 52.2% (95% CI: 42.47-61.93, I 2 = 98.4 % ). Antenatal care ( OR = 7.8 (95% CI: 3.2, 19.2), I 2 = 96.3 % ), media exposure ( OR = 8.3 (95% CI: 2.1, 33.3), I 2 = 98.1 % ), distance from the health facility ( OR = 2.64 (95% CI: 1.1, 6.6), I 2 = 94.1 % ), educational status of women ( OR = 4.7 (95% CI: 2.07, 9.56), I 2 = 94.2 % ), and educational status of husbands ( OR = 2.995 (95% CI: 1.194, 7.512), I 2 = 92.5 % ) were factors significantly associated with receiving at least two doses of tetanus toxoid immunization coverage in Ethiopia. Conclusions. The coverage of tetanus toxoid immunization among childbearing women was low in Ethiopia. Strengthening maternal health service utilization (antinatal care and institutional delivery) to the nearest health facility even in health posts and empowering education for both women and their husbands is recommended to increase tetanus toxoid immunization coverage in Ethiopia.

This paper incorporates the adaptive neurofuzzy inference system (ANFIS) technique to model the yield of bio-oil. The estimation of this parameter was performed according to pyrolysis conditions and biomass compositions of feedstock. For this purpose, this paper innovates two optimization methods including a genetic algorithm (GA) and particle swarm optimization (PSO). Primary data were gathered from previous studies and included 244 data of biodiesel oils. The findings showed a coefficient determination ( R 2 ) of 0.937 and RMSE of 2.1053 for the GA-ANFIS model, and a coefficient determination ( R 2 ) of 0.968 and RMSE of 1.4443 for PSO-ANFIS. This study indicates the capability of the PSO-ANFIS algorithm in the estimation of the bio-oil yield. According to the performed analysis, this model shows a higher ability than the previously presented models in predicting the target values and can be a suitable alternative to time-consuming and difficult experimental tests.

This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model.