International Journal of Innovations in Science & Technology
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Tomato Disease Classification using Fine-Tuned Convolutional Neural Network
Get PDFTomatoes have enhanced vitamins that are necessary for mental and physical health. We use tomatoes in our daily life. The global agricultural industry is dominated by vegetables. Farmers typically suffer a significant loss when tomato plants are affected by multiple diseases. Diagnosis of tomato diseases at an early stage can help address this deficit. It is difficult to classify the attacking disease due to its range of manifestations. We can use deep learning models to identify diseased plants at an initial stage and take appropriate measures to minimize loss through early detection. For the initial diagnosis and classification of diseased plants, an effective deep learning model has been proposed in this paper. Our deep learning-based pre-trained model has been tuned twofold using a specific dataset. The dataset includes tomato plant images that show diseased and healthy tomato plants. In our classification, we intend to label each plant with the name of the disease or healthy that is afflicting it. With 98.93% accuracy, we were able to achieve astounding results using the transfer learning method on this dataset of tomato plants. Based on our understanding, this model appears to be lighter than other advanced models with such considerable results and which employ ten classes of tomatoes. This deep learning application is usable in reality to detect plant diseases.
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Report Generation of Lungs Diseases From Chest X-ray using NLP
Get PDFPulmonary diseases are very severe health complications in the world that impose a massive worldwide health burden. These diseases comprise of pneumonia, asthma, tuberculosis, Covid-19, cancer, etc. The evidences show that around 65 million people undergo the chronic obstructive pulmonary disease and nearly 3 million people pass away from it each year that make it the third prominent reason of death worldwide. To decrease the burden of lungs diseases timely diagnosis is very essential. Computer-aided diagnostic, are systems that support doctors in the analysis of medical images. This study showcases that Report Generation System has automated the Chest X-Ray interpretation procedure and lessen human effort, consequently helped the people for timely diagnoses of chronic lungs diseases to decrease the death rate. This system provides great relief for people in rural areas where the doctor-to-patient ratio is only 1 doctor per 1300 people. As a result, after utilizing this application, the affected individual can seek further therapy for the ailment they have been diagnosed with. The proposed system is supposed to be used in the distinct architecture of deep learning (Deep Convolution Neural Network), this is fine tuned to CNN-RNN trainable end-to-end architecture. By using the patient-wise official split of the OpenI dataset we have trained a CNN-RNN model with attention. Our model achieved an accuracy of 94%, which is the highest performance.
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Health Consultant Bot: Primary Health Care Monitoring Chatbot for Disease Prediction
Get PDFThis research paper presents a disease prediction chatbot that is intelligent enough to communicate with patients to predict their disease by detecting their symptoms through natural language processing. This system allows the user to describe their medical health condition in natural language, and by processing their natural language-based statement, our system detects the symptoms, predicts the disease, and provides basic precautions as well as a brief introduction about the disease. We have used IBM Watson Assistant to build this system. Watson assistant provides several machine learning algorithms to process user statements and symptoms extraction. In our system, symptoms were mapped by considering the community data which resulted in a predicted disease. Our system provides the relevant information about the predicted disease from the system\u27s database. In an experimental evaluation, we carried out a study having 156 subjects, who interact with the system in a daily use scenario. Results show the effectiveness and accuracy of our system to support the patient in taking good care of their health.
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Molecular characterization of Deciphering Fungal Community structure in Zea mays L. and Triticum Aestivum L
Get PDFRhizosphere fungi are strongly associated with plant growth and health by providing nutrients and antagonizing pathogens. Commercially, fungus has multipurpose applications in several sectors including beverages, food items and in medicines. Current study aimed to reveal the core fungal community structure of the two leading cereal crops that are Zea mays L. and Triticum aestivum L. The rhizosphere fungal community was explored via morphology, biochemistry and internal transcribe spacer (ITS) metagenomics. On the basis of morphology, the retrieved fungal strains were imprecisely classified into Ascomycota and Zygomycota. The species including Yeast, Botyritis californica, Rhizopus stolonifer, Alternaria tenuissima, Aspergillus terreus, Aspergillus flavus, Aspergillus nidulans, Aspergillus niger and Microsporum canis were identified on the basis of macroscopy and microscope. Moreover, the biochemical characterization depicted the role of fungi in promotion of plant growth. Majority of the isolates depicted catalase activity, indole production, phosphate solubilization, ammonia production, nitrogenase activity and urease activity. Metagenomics using amplicon sequencing of ITS region revealed the presence of 805 Operational Taxonomic Units (OTUs) with 647 OTUs in Zea mays and 620 OTUs in Triticum aestivum. The fungal phyla found in the rhizosphere of Zea mays L. and Triticum aestivum L. were Ascomycota, Basidiomycota, Zygomycota, Chytridiomycota, Incertae sedis fungi. Ascomycota accounted for 93% and 95% of classified fungi in rhizosphere of Zea mays L. and Triticum aestivum L. respectively. The dominant species found in the rhizosphere soil of Zea mays were Gibberella intricans, Curvularia lunata, Lepidosphaeria nicotiae, Edenia gomezpompae and Myrothecium verrucaria
Impact Assessment of Commercialization of Main Roads in Planned Housing Schemes: A Case Study of PIA Housing Scheme
Get PDFThe phenomenon of commercialization of residential properties is taking place rapidly. Although conversion of landuse from residential to commercial in built-up areas is not new in developing countries but planned housing schemes also start experiencing this land-use conversion, and now are no more predominantly residential. Furthermore, declaring a road commercial by the competent authorities, even without taking necessary improvements in infrastructure and consent of the community, further exaggerate the problem. The negative consequences of this phenomenon have a severe effects on residential areas and include environmental problems, traffic congestion, noise, and air pollution, and affect the residential character of the area in negative manner for which they were initially developed. The aim of the study is to describe briefly about remits of commercialization polices in Lahore and then to assess the functionality of commercialized residential roads through assessment criteria based upon the indicators established to assess capacity of road infrastructure before declaring it commercial. This includes road management plan, details of road network with condition of road, its width, Pedestrian and Public transport facilities, structure, including the primary, mixed-use, and secondary nodes. Perception of the users and residents regarding the change in the use of land is also weighed up. The findings of this research will draw the attention of responsible authorities to improve the design guidelines, which are essential to consider before commercializing the residential roads
Floristic Composition, Biological Spectrum and Distribution Pattern of Floral Biodiversity in Jalalabad Taisot Valley, Gilgit Baltistan
Get PDFJalalabad is a small village in Gilgit District in Pakistan, located around 20 km east of Gilgit city. Jalalabad village is one of the beautiful valleys located at 35°53.921 N latitude, 074°29.382 E longitude at an altitude of 1500. The present study was carried out from July-August 2021-2022 and was comprised of two main parts. The first part was floristic diversity, the second part was phytosociological studies. The collected specimens consist of (156) plant species that belonged to 119 genera and 49 families. The life forms of the collected species were 62 (72%) where Hemicryptophyte were dominant, 33 (22%) Therophytes, 14 (9%) Chaemophyte, 42 (27%) Phanerophyte and Geophytes were 4 (2%). The breakup of the habit categories shows that the herbs with 103 (66%) species were dominant to show the flora of the study area, followed by shrubs with 17 (10%) species which shows the flora of the study area. Subshrubs by 9 (6%) and trees 27 (22%) contained the flora of the study area. We studied three stands and in each stand, we placed twenty quadrate to recognize the dominant flora based on IVI. We recognized the dominant lifeform Hemicryptophytes and dominant taxa Thaymus linearis held at the highest value (64.259) based on IVI. The phytosociological studies provided all required information from each stand like dominant habit categories, dominant life forms, and dominant taxa in the study area.
Spatio-Temporal Dynamics of Nitrogen Dioxide (NO2) Concentration & its Impacts on Human Health (2010-2022)
Get PDFNitrogen is one of chemical gases which has drastic impact on human health. It is also renowned globally as a major component of climate change. Lahore city has been selected as the study area to conduct this research. The basic objective of this study is to assess the temporal and seasonal change of Nitrogen Dioxide (NO2) concentration in the study area and its effects on human health. For this purpose, the two-phase methodology has been adopted. In the first phase, primary and secondary datasets were collected through an online questionnaire and Environmental Protection Agency (EPA), respectively, while in the second phase, satellite imageries were acquired from NASA Earth Observatory (NEO). An online questionnaire survey was conducted for a better understanding and assessment of NO2 effects on inhabitants. The interpolation technique was applied to show a temporal change in Concentration of NO2 from 2010-2022 and for seasonal change in 2022. Findings of this research showed that NO2 levels are high during winters as compared to summers. Whereas, temporal analysis from 2010-2019 revealed that high dense columns of NO2 were found in 2019 & 2020 and less dense columns were found in 2019, whereas this concentration declined due to the arrival of COVID from 2020 to the end of 2021. The main reason of this decline is the lack of transport or industrial exhaust due to lockdown by COVID. The results of the questionnaire indicate that people encountered diverse health problems due to long- and short-term exposure to NO2. Moreover, this study helps to display the drastic impacts of NO2 concentration on human health and the natural environment
Reliability Awareness Multiple Path Installation in Software Defined Networking using Machine Learning Algorithm
Get PDFLink failure is still a severe problem in today\u27s networking system. Transmission delays and data packet loss cause link failure in the network. Rapid connection recovery after a link breakdown is an important topic in networking. The failure of the networking link must be recovered whenever possible because it could cause blockage of network traffic and obstruct normal network operation. To overcome this difficulty, backup or secondary channels can be chosen adaptively and proactively in SDN based on data traffic dynamics in the network. When a network connection fails, packets must find a different way to their destination. The goal of this research is to find an alternative way. Our proposed methodology uses a machine-learning algorithm called Linear Regression to uncover alternative network paths. To provide for speedy failure recovery, the controller communicates this alternate path to the network switches ahead of time. We train, test, and validate the learning model using a machine learning approach. To simulate our proposed technique and locate the trials, we use the Mininet network simulator. The simulation results show that our suggested approach recovers link failure most effectively compared to existing solutions
A Qualified review on ML and DL algorithms for Bearing Fault Diagnosis
Get PDFMoving machinery is the backbone of socio-economic development. The use of machines help in increasing the production of everyday used items, and tools, that generate electricity and mechanical energy, and provides easy and fast transportation and help by saving human efforts, energy, and time. The mechanical industry is totally dependent on the bearing and it is considered as bread and butter of the system. Bearing failure is about 40% of the total failures of induction motors which is why it is a crucial challenge to predict the failure and helps prevent future downtime events through maintenance schedules with the latest techniques and tools of. This paper presents a review of how DL techniques and algorithms outsmarted ML for bearing fault detection and diagnosis and summarizes the accuracy results generated by most common DL algorithms over classical ML algorithms.Additionally this paper reasons different criteria for which DL algorithms have been proved efficient for building productive model in the field of bearing fault detection. Furthermore, some of the most famous datasets by different universities have been discussed and accuracy results are provided by reviewing algorithms on the CWRU dataset by different researchers and comparison chart is listed in the results section
Assessment of climate change projections in the Chenab River Basin, Western Himalaya
Get PDFGeneral circulation models (GCMs) are vital to project potential changes in futureclimate under different emissions scenarios. Raw GCM output is not applicable atregional scale due to biases relative to observational data and coarse spatial scale forfuture climate predictions. Here, statistical downscaling method was employed to generatedaily maximum temperature (Tmax), minimum temperature (Tmin) and precipitation ofcoarse spatial resolution of GCM (0.5 degree) which fall within the boundary of CRB. In thisstudy, the fifth generation ECMWF atmospheric reanalysis (ERA5) data was used as observeddata to downscale and bias-correct GFDL-ESM2M data under RCP4.5 and RCP8.5 emissionscenarios for the near future (2020-2050), mid-century (2051-2080) and end of century (2081-2100) in the Chenab River Basin (CRB). The refined output from the GCM was furtheranalyzed to depict climate changes in the CRB. It was found that a consistent increase inmaximum temperature (Tmax) and minimum temperature (Tmin) was recorded under RCP4.5and RCP8.5 in the future scenarios. In the CRB, the magnitude of increase in predicted Tminwas higher than Tmax. However, precipitation showed an increasing trend in near future whiledecreasing trend in the mid-century and end of century under RCP4.5