Histopathological Image Classification Methods and Techniques in Deep Learning Field

A cancerous tumour in a woman's breast, Histopathology detects breast cancer. Histopathological images are a hotspot for medical study since they are difficult to judge manually. In addition to helping doctors identify and treat patients, this image classification can boost patient survival. This research addresses the merits and downsides of deep learning methods for histopathology imaging of breast cancer. The study's histopathology image classification and future directions are reviewed. Automatic histopathological image analysis often uses complete supervised learning where we can feed the labeled dataset to model for the classification. The research methods are frequentlytrust on feature extraction techniques tailored to specific challenges, such as texture, spatial, graph-based, and morphological features. Many deep learning models are also created for picture classification. There are various deep learning methods for classifying histopathology images

Comparative Analysis of common Edge Detection Algorithms using Pre-processing Technique

Edge detection is the process of segmenting an image by detecting discontinuities in brightness. So far, several standard segmentation methods have been widely used for edge detection. However, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image preprocessing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard edge detection methods. The proposed preprocessing approach involves median filtering to reduce the noise in image and then Edge Detection technique is carried out. And atlast Standard edge detection methods can be applied to the resultant preprocessing image and its Simulation results are show that our preprocessed approach when used with a standard edge detection method enhances its performance

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Not AvailableThe present investigation was conducted during the kharif season of 2007, 2008 and 2009 to obtain the knowledge of nature and magnitude of genetic parameters and its utilization in development of superior varieties of pigeonpea [Cajanus cajan (L.) Millsp.]. The genetic parameters studied namely, genotypic and phenotypic variability, genotypic and phenotypic coefficient of variation, heritability (h2) and genetic advance. Besides, these parameters, correlation coefficient and path analysis were also studied for seed yield and its component traits in 21 diverse genotypes of short duration pigeonpea. The results indicated that the genotypes showed significant variability for all the traits studied. UPAS 120 yielded the highest seed yield/plant (39.21 g), followed by ICPL 88034 (35.66 g) and PA 134 (35.65 g). The high yield of UPAS 120 was attributed by high number of seeds/pod and pod length. Similarly, high yield of ICPL 88034 was contributed by primary branches/plant, pod length and 100-seed weight. The range of PCV was observed from 4.56 to18.59 % for the traits under study which provides a picture of the extent of phenotypic variability in the population. The PCV was noted moderate for the characters like seed yield/plant (18.59%), pods/plant (18.04%) and primary branches/plant (12.22%). Genotypic coefficient of variation ranged from 3.24% to 17.84%. Maximum GCV was observed for seed yield/plant (17.84%), followed by pods/plant (17.80%) and primary branches/plant (10.94%). Seed yield/plant was found to be significant positively associated with seeds/pod, pod length and plant height at genotypic level. The estimate of broad sense heritability was the highest for pods/plant (97%), followed by days to 50% flowering (94%), grain yield/plant (92%), days to maturity (90%), primary branches/plant (80%) and plant height (78%). The estimated genetic advance was recorded the moderate magnitude for pods/plant (36%) and grain yield/plant (35%). Seed yield/plant was found to be significant positively associated with seeds/pod, pod length and plant height at genotypic level. Seeds/pod exhibited the highest magnitude of direct effects on seed yield, followed by primary branches/plant and pod length. The component characters namely, pod length and seeds/pod showed positive and significant correlation (0.529 and 0.794) with seed yield/plant and also exhibited positive and strong direct effects (0.531 and 0.266) on seed yield/plantNot Availabl

Not Available

Not AvailableThe present investigation was conducted during the kharif season of 2007, 2008 and 2009 to obtain the knowledge of nature and magnitude of genetic parameters and its utilization in development of superior varieties of pigeonpea [Cajanus cajan (L.) Millsp.]. The genetic parameters studied namely, genotypic and phenotypic variability, genotypic and phenotypic coefficient of variation, heritability (h2) and genetic advance. Besides, these parameters, correlation coefficient and path analysis were also studied for seed yield and its component traits in 21 diverse genotypes of short duration pigeonpea. The results indicated that the genotypes showed significant variability for all the traits studied. UPAS 120 yielded the highest seed yield/plant (39.21 g), followed by ICPL 88034 (35.66 g) and PA 134 (35.65 g). The high yield of UPAS 120 was attributed by high number of seeds/pod and pod length. Similarly, high yield of ICPL 88034 was contributed by primary branches/plant, pod length and 100-seed weight. The range of PCV was observed from 4.56 to18.59 % for the traits under study which provides a picture of the extent of phenotypic variability in the population. The PCV was noted moderate for the characters like seed yield/plant (18.59%), pods/plant (18.04%) and primary branches/plant (12.22%). Genotypic coefficient of variation ranged from 3.24% to 17.84%. Maximum GCV was observed for seed yield/plant (17.84%), followed by pods/plant (17.80%) and primary branches/plant (10.94%). Seed yield/plant was found to be significant positively associated with seeds/pod, pod length and plant height at genotypic level. The estimate of broad sense heritability was the highest for pods/plant (97%), followed by days to 50% flowering (94%), grain yield/plant (92%), days to maturity (90%), primary branches/plant (80%) and plant height (78%). The estimated genetic advance was recorded the moderate magnitude for pods/plant (36%) and grain yield/plant (35%). Seed yield/plant was found to be significant positively associated with seeds/pod, pod length and plant height at genotypic level. Seeds/pod exhibited the highest magnitude of direct effects on seed yield, followed by primary branches/plant and pod length. The component characters namely, pod length and seeds/pod showed positive and significant correlation (0.529 and 0.794) with seed yield/plant and also exhibited positive and strong direct effects (0.531 and 0.266) on seed yield/plantNot Availabl

An experimental investigation on phytoremediation performance of water lettuce (Pistia stratiotes L.) for pollutants removal from paper mill effluent

The present study describes the phytoremediation performance of water lettuce (Pistia stratiotes L.) for physicochemical pollutants elimination from paper mill effluent (PME). For this, pot (glass aquarium) experiments were conducted using 0% (BWW: borewell water), 25%, 50%, 75%, and 100% treatments of PME under natural day/light regime. Results of the experiments showed that the highest removal of pH (10.75%), electrical conductivity (EC: 63.82%), total dissolved solids (TDS: 71.20%) biological oxygen demand (BOD: 85.03%), chemical oxygen demand (COD: 80.46%), total Kjeldahl's nitrogen (TKN: 93.03%), phosphorus (P: 85.56%), sodium (Na: 91.89%), potassium (K: 84.04%), calcium (Ca: 84.75%), and magnesium (Mg: 83.62%), most probable number (MPN: 77.63%), and standard plate count (SPC: 74.43%) was noted in 75% treatment of PME after treatment by P. stratiotes. PCA showed the best vector length for TKN, Na, and Ca. The maximum plant growth parameters including, total fresh biomass (81.30 ± 0.28 g), chlorophyll content (3.67 ± 0.05 mg g-1 f.wt), and relative growth rate (0.0051 gg-1 d-1) was also measured in 75% PME treatment after phytoremediation experiments. The findings of this study make useful insight into the biological management of PME through plant-based pollutant eradication while leftover biomass may be used as a feedstock for low-cost bioenergy production. Practitioner points: Biological treatment of paper mill effluent using water lettuce is presented. Best reduction of physicochemical and microbiological pollutants was attained in 75% treatment. Maximum production of chlorophyll, plant biomass, and highest growth rate was also observed in 75% treatment

An Innovation development of deep-sea bacterial monitoring and classification based on artificial intelligence microbiological model

The current sea monitoring equipments are being used for a variety of purposes around the world. Currently used vehicles have some drawbacks. The first is the high fuel cost. The Vehicle engines cost more fuel as they have to release more power and environment and pollution. As well as not being able to stay under the sea for long days, there will often be a need for vehicles to come to the surface to refuel. The second is the vibrations and noise of these vehicles. The vibrations caused by these can be detrimental to the biodiversity of the ocean. Also, the noise makes it easier for enemies to identify our vehicles. Similarly when these vehicles go under water, water waves form on the surface. With this in mind, radar can detect what a vehicle under the sea looks like. In this paper, an artificial intelligence based microbiological model was proposed to monitor the sea level. With this biological model can greatly reduce fuels. It can get more capacity than normal vehicles. As fuel consumption decreases, so it does environmental pollution and since it operates quietly and without high vibrations, there is no threat to the biodiversity of the ocean

Phytoremediation of Composite Industrial Effluent using Sacred Lotus (<i>Nelumbo nucifera</i> Gaertn): A Lab-Scale Experimental Investigation

This study investigates the phytoremediation of composite industrial effluent (CIE) released from multiple industries within the SIIDCUL cluster, Haridwar, India, using the sacred lotus (Nelumbo nucifera Gaertn) plant. Batch-mode phytoremediation experiments were conducted using three selected concentrations (0%: borewell water as control, 50%, and 100%) of CIE for 45 days. Results show that the N. nucifera plant significantly reduced loads of physicochemical and heavy metal pollutants of CIE. In particular, the maximal removal of total dissolved solids (TDS: 89.56%), biochemical oxygen demand (BOD: 78.20%), chemical oxygen demand (COD: 79.41%), total Kjeldahl’s nitrogen (TKN: 86.48%), phosphorus (P: 76.37%), cadmium (Cd: 70.37%), copper (Cu: 85.82%), chromium (Cr: 68.61%), iron (Fe: 72.86%), lead (Pb: 76.92%), and zinc (Zn: 74.51%) pollutants was noted in the 50% CIE concentration treatment. Heavy metal bioaccumulation and translocation factor values (>1) for root and leaf parts show that the N. nucifera plant was a hyperaccumulator. However, the contents of heavy metals were higher in the root than the leaf part of the N. nucifera plant. Moreover, the selected plant growth attributes such as fresh plant biomass (760.70 ± 8.77 g/plant; without flowers), chlorophyll content (4.30 ± 0.22 mg/g fwt.), plant height (154.05 ± 4.55 cm), root length (70.35 ± 2.42 cm), leaf spread (41.58 ± 0.26 cm), number of leaves (10.00 ± 1.00 per plant), and number of flowers (16.00 ± 2.00) were also maximal in the 50% CIE concentration. This study provides a sustainable approach towards the effective biotreatment of noxious mixed effluent using plant-based green technology

Combined Use of Sewage Sludge and Plant Growth-Promoting Rhizobia Improves Germination, Biochemical Response and Yield of Ridge Gourd (<i>Luffa acutangula</i> (L.) Roxb.) under Field Conditions

This research investigated the combined use of sewage sludge (SS) and plant growth-promoting rhizobia (PGPR) for Ridge gourd (Luffa acutangula (L.) Roxb.) cultivated under field conditions. The different treatments of SS and PGPR such as 0% (soil as control), 5% SS, 5% SS + PGPR, 10% SS, and 10% SS + PGPR were applied to assess their impacts on seedling growth, biochemical response, and yield performance of Ridge gourd. The results showed that the highest seedling emergence (92.3 ± 2.1%), fresh biomass (9.6 ± 0.3 g), growth rate (1.4 ± 0.1 g/day), seedling length (15.5 ± 0.3 cm), root length (10.4 ± 0.3 cm), total chlorophyll (3.2 ± 0.1 mg/g), crop yield (13.8 ± 0.1 kg/plant), and average crop yield per harvest (2.8 ± 0.1 kg/plant) were observed in 10% SS + PGPR treatment. The enzyme activities of superoxide dismutase (SOD; µg/g) and catalase (CAT: µg/g) were significantly lowered after PGPR inoculation in higher SS treatments. The results of principal component (PC) and Euclidian clustered distance analyses showed a positive influence of SS dose on soil nutrient availability and Ridge gourd’s growth, biochemical responses, and yield performance. Moreover, the elemental analysis showed that the bioaccumulation factor (BAF < 0.90) and health risk index (HRI < 0.40) of selected metal elements (Cd, Cr, Cu, Fe, Mn, and Zn) were within the permissible limits, indicating consumption of Ridge gourd fruits was safe. The outcomes of this study suggest the potential use of SS and PGPR for improved Ridge gourd production and contribution towards sustainable development goal (SDG) 12 on responsible consumption and production of vegetable crops

Combined Use of TiO₂ Nanoparticles and Biochar Produced from Moss (<i>Leucobryum glaucum</i> (Hedw.) Ångstr.) Biomass for Chinese Spinach (<i>Amaranthus dubius</i> L.) Cultivation under Saline Stress

Salinity-induced soil degradation poses a significant challenge to agricultural productivity and requires innovative crop-management strategies. In this study, the synergistic effect of biochar and TiO2 nanoparticles (NPs) obtained from moss (Leucobryum glaucum (Hedw.) Ångstr.) biomass on the growth, yield, biochemical, and enzymatic response of Chinese spinach (Amaranthus dubius L.) grown under salinity stress was investigated. Purposely, A. dubius was grown under different combinations of arable soil, biochar, TiO2 NPs, and saline soils. The produced biochar and TiO2 NPs were characterized using microscopy image analysis, X-ray diffraction patterns (XRD), energy-dispersive X-ray spectroscopy (EDX), zeta potential, particle size distribution, and Fourier-transform infrared spectroscopy (FTIR). The results showed that saline stress caused a significant (p A. dubius compared to control treatments. However, the combined application of biochar and TiO2 NPs significantly (p 2/plant), chlorophyll (2.36 mg/g), carotenoids (2.85 mg/g), and relative water content (82.10%). Biochar and TiO2-NP application helped to reduce the levels of stress enzymes such as catalase (2.93 µmol/min/mg P), superoxide dismutase (SOD: 2.47 EU/g P), peroxidase (POD: 40.03 EU/min/g P), and ascorbate peroxidase (3.10 mM/mg P) in saline soil. The findings of this study suggest that the combination of nanotechnology and biochar derived from unconventional biomass can be a viable option to mitigate salinity-related challenges and enhance crop yield

Health Risk Assessment of Hazardous Heavy Metals in Two Varieties of Mango Fruit (<i>Mangifera indica</i> L. var. Dasheri and Langra)

The spatial assessment of four heavy metals (Cd, Cr, Pb, and As) in two mango fruit (Mangifera indica L.) varieties (Dasheri and Langra) collected from the Saharanpur district, Uttar Pradesh, India, was investigated in this study. The samples of ripe mango fruits were collected from the orchards of 12 major towns in the Saharanpur district from May to June 2022. Heavy metal analysis using atomic absorption spectroscopy (AAS) showed the presence of all selected heavy metals. Specifically, the concentration (mg/kg dry weight basis) range of Cd (0.01–0.08), Cr (0.11–0.82), Pb (0.02–0.15), and As (0.01–0.14) did not exceed the safe limits. The geospatial variation in the heavy metal concentration was significantly (p < 0.05) different as indicated by the inverse distance weighting (IDW) interpolation and analysis of variance (ANOVA) results. The multivariate statistical analysis using principal component (PC) and agglomerative hierarchical cluster (AHC) analyses revealed that the Saharanpur city location had the highest levels of selected heavy metals out of the 12 sampling locations. In this, the Dasheri variety was identified to have higher heavy metal concentrations in comparison to the Langra variety. Moreover, the health risk study using the target hazard quotient (THQ) confirmed that the levels did not exceed the safe health risk index (HRI) limit of 1. However, the health risk assessment for the child group showed relatively high HRI values (<0.35) compared to those of the adult group (<0.09). Therefore, considering the importance of the Saharanpur district in massive mango fruit production, this study provides vital information regarding the biomonitoring of heavy metals in the two most consumed varieties