9 research outputs found
A bootstrap aggregation approach for adequate crop fertilizer and nutrition recommendation
No full textAgriculture is the largest workforce of India and biggest contributor to the Indian economy. Improving agricultural practices with the help of modern computer science technologies have great scope. Helping the farmers to know about their soil fertility, crops which can be grown and fertilizers or nutrients required for their land will be valuable inputs for them. Too much or too little fertilizers may harm the soil, so right amount of fertilization is also important. In this paper we have discussed about the bootstrap aggregation regression method, which is an ensemble machine learning technique to recommend the optimum level of nutrients and fertilizers. Hence customized nutrients recommendation reports could be generated to suggest the fertilizers and nutrients with their adequate quantities. This will be really beneficial for farmers to maintain the soil health and helpful for better crop growth and yield. We consider the features and levels of soil parameters such as nitrogen, phosphorus, potassium (NPK), pH level, organic carbon, electric conductivity, humidity, rainfall and other micro nutrients for predicting the right amount of fertilizers and nutrients. We have also checked other regression methods to compare the results based on the previous work done in the same field
An AI solution for Soil Fertility and Crop Friendliness Detection and Monitoring
No full textAgriculture is the main occupation of India and more than 50% of people are dependent on agriculture. Research on agriculture will strengthen the economic growth of the country. Technologies play a vital role to bolster the agriculture. Since soil is the main fount of agriculture , there is a need for significant approach to help the farmer to test and monitor the soil and its properties ,which will boost the fertility of the soil thereby intensifying the crop growth, also if crop recommendations are imparted to farmers in a proper way, crop yield can be enhanced to meet the growing demand for the food. Proper awareness on soil will benefit the farmers to grow the right and healthy crop. To overcome the disadvantages of traditional soil testing practices we are proposing an approach which has Deep learning, an artificial intelligence(AI) technique and IOT features . This helps in getting fast and accurate result. Soil fertility can be calculated by parameters like pH level, temperature, Moisture content of the soil,temperature, humidity and NPK(nitrogen, phosphorus, and potassium) ,organic matter, carbon level. Weather and Climatic conditions along with the soil parameters will help to evaluate the soil fertility. The lacking nutrients in the soil and needed nutrients/fertilizers to boost the soil fertility can be suggested to the farmers and also the crops which can be suitably grown from the given soil sample and nutrients required for all the recommended crops to enhance the yield can be suggested to the farmers
An artificial intelligence solution for crop recommendation
No full textAgriculture is the major occupation in India. The development of India is in the hands of farmers. Farmers are said to be our nation’s backbone, so there is a need to support our farmers technologically so that the difficulties of traditional agricultural practices would be overcome and also there will be positive impact on the yield, harvest, healthy crop output and the income of the farmers. Farmer needs awareness about his soil and the methods to improve his soil to grow the healthy crops. We propose an approach which involves deep learning and some IoT features to help our farmers. Soil parameters such as nitrogen, phosphorous, potassium (NPK), pH, organic carbon, moisture content and few more things are considered for predicting the fertility of the soil and also to predict the right crops to be grown and nutrition required for it. We have developed a deep neural network model to predict the crop which can be suitably grown in the soil. We have also implemented the other machine learning classifiers on the same collected dataset to test the accuracies of each classifier and our deep neural network model
The new machine learning feature selection method used in fertilizer recommendation
No full textFertilizer recommendation is the crucial factor to be considered in automation of agricultural predictions. Fertilizer fill the necessary portion of any farming region. There are some micronutrients and macro nutrients which need to be given to crops for proper growth. If fertilization is not done to an optimum level, it may badly harm the soil quality and crop health ,so optimum fertilization is important. In this paper we discuss fertilizer and nutrient recommender, where we have used a new feature selection methodology. We have shown the difference between two implementation cases considering presence and absence of feature ranking and selection. Feature ranking and selection has clearly increased the efficiency of the fertilizer nutrient recommender in our work from 85% to 98%. Feature selection raking has been introduced with random forest approach
A bibliometric analysis of the 100 top-cited systematic review and meta-analysis in Orthodontics
No full textABSTRACT Objective: This bibliometric study aimed to analyze the citation metrics, journal and author characteristics, and subject domains of the 100 top-cited Systematic Reviews (SR) and Meta-Analysis (MA) in orthodontics. Material and Methods: An electronic database search was conducted for SR and MA in the Web of Science on 16th July 2023, without language and time restrictions. Of the 802 hits returned, the 100 top-cited orthodontic articles were shortlisted. They were analyzed for citation metrics, journal characteristics (journal, year of publication, impact factor-IF), author and affiliation characteristics (number, primary and corresponding author’s affiliation, and country), study domain, and keywords. Results: These articles were published from 1996 to 2021 in 20 journals, with an impact factor of 1.9 to 10.5, by 351 researchers affiliated with 104 universities. Their citations ranged from 45 to 344, and 34 poised to be classified as classic (≥ 100 citations). The maximum number of articles was published in the American Journal of Orthodontics and Dentofacial Orthopedics (n=38), the European Journal of Orthodontics (n=18), and the Angle Orthodontist (n=8). The authors for individual papers ranged from 1 to 10, with 5 being the most common (n=58). Europe had the highest contribution regarding the number of corresponding authors, institutions, and citations. Bone anchorage and orthodontic tooth movement/Biomechanics were the most frequently researched domains (n=11 each). The most common keyword used was Orthodontics (n=19), followed by Systematic Review (n=16) and Meta-analysis (n=9). Conclusion: In general, the top cited SR and MA were published in high-impact orthodontic journals, were multi-authored, and reflected the collaborative work from different universities
Laboratory Simulation Studies on Evaporative Fuel Loss from Storage Tanks
No full textThe fossil fuel is used for various activities; however, it is available in limited quantity and burning of fossil fuel causes global warming. The fuel loss from fuel tanks causes volatile organic emission. When two-wheeler is parked in hot sun during summer, there is evaporation loss of fuel from the fuel tanks. In this work, experimental work was carried out in laboratory, to understand fuel losses at various temperature such as 35, 40 and 45 degree C. From this work, it was observed that the air temperature significantly affects the fuel loss. The fuel loss increases with increase in air temperature and higher evaporation losses were observed at 45-degree C. To simulate the fuel storage tank with different level of fuel in the tank, experiments were conducted with beakers of different capacity such as 80, 100 and 200 mL which has the surface area of 101, 149 and 226 cm2. From the experiments, it was observed that the fuel evaporation loss increases with increase in surface area of the beaker. The surface is of 226 cm2 causes highest fuel evaporation loss as compared to other surface areas. From this work, we suggest that evaporative losses be minimized through appropriate mitigation measures
AN EFFICIENT METHOD FOR EYE TRACKING BY PASSWORD VERIFICATION
No full textIndividual Identification numbers are generally utilized for client verification and security. Secret key verification utilizing PINs expects clients to genuinely include the PIN, which could be frail against mystery word breaking by methods for acquiring individual or private or warm area. Individual range affirmation with tolerant look based range zone methodsgives up no consistent impressions and consequently offers a dynamically protected riddle state segment choice. Look based verification alludes to finding the consideration area across consecutive picture edges, and following eye community after some time. Eye following is accomplished by distinguishing a similar eye includes over various picture outlines and connecting them to a specific eye. The estimations are pursued for eye affirmation including various motivations behind the face district, eye development quickly moving, and iris snags to pick their comfort for the notable applications. Eye-stare identification has been generally explored and introduced. The eye can't move as quickly as 30 movements for every second. This prompted proposing an upgrade to the eye tracking framework being introduced. Picture preparing game plan isolates the iris and learns the specific community for an eye, in this way conveying controlled signs with the assistance of a reference place. The signs are controlled at that point utilized with a situation of a mechanized stage through wise camera. This upgrade improved the CPU handling time prerequisites. This presents a paper with a consistent application for look based range passage, and eye attestation to follow the range seeing verification using a sensible camera
Automated Detection of Polycystic Ovary Syndrome Using Convolutional Neural Networks on Ultrasound Images
No full textPolycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting millions of women worldwide, yet it remains frequently underdiagnosed due to symptom variability and limited diagnostic resources. This paper presents a Convolutional Neural Network (CNN)-based system for automated PCOS detection from ultrasound images. The model leverages deep learning for accurate feature extraction and classification, aiming to support clinicians and improve diagnostic accessibility. Experimental results demonstrate high accuracy, underscoring the potential of AI-driven solutions in advancing women’s healthcare. Beyond accuracy, the system offers scalability, reduced diagnostic time, and potential integration into telemedicine platforms, highlighting its role in bridging healthcare gaps and enabling earlier intervention
Enhancing Parkinson\u27s Disease Detection using AI Techniques
No full textAbstract: One of the severe illnesses that causes uncontrollable and unexpected outcomes is Parkinson\u27s disease(PD). People over 50 years of age are typically the ones who contract this illness. The patients\u27 symptoms progressively become worse leading to a variety of abnormalities such as body part rigidity and abnormalities in speech and gait. In addition, the patients have sadness, sleep deprivation, memory problems, mental illness, and numerous other health problems. Parkinson\u27s disease is caused by damage or death of neurons in the brain\u27s basal ganglia, but scientists and doctors are unable to pinpoint the causes of this damage or death. Therefore, timely disease diagnosis and treatment can help patients avoid unanticipated life implications. The biggest benefit of this era is the application of Artificial Intelligence(AI) and machine learning(ML) in the healthcare industry, which facilitates and expedites diagnosis and prediction. In this paper, we have proposed a solution for Parkinson’s disease prediction. We have done a comparative analysis in terms of performance by implementing various Machine Learning algorithms that can be used for Parkinson’s disease predictions. Random Forest performs better than a lot of other ML methods showing 99% accuracy.
