The well-being of cotton crops is of utmost importance for maintaining agricultural productivity, and the early detection of diseases plays a critical role in achieving this objective. This study introduces a comprehensive approach for creating a machine learning-based system capable of identifying diseases in cotton plants through the analysis of leaf images. The research encompasses stages such as acquiring the dataset, pre-processing the data, training the model, developing an ensemble model, evaluating the models, and analyzing the results. Several machine-learning models are trained and evaluated to determine how well they can classify cotton leaves as "Healthy" or "Diseased." These models include Random Forest, Support Vector Machine (SVM), Multi-Class SVM, and an Ensemble model. This investigation yields a practical and visually informative system for disease detection, which can contribute to disease prevention, thereby enhancing both crop yield and quality. This work underscores the significance of continuous improvement by periodically updating the models and explores the potential of advanced techniques such as deep learning.

Objective: Non-neoplastic, non-inflammatory cysts of the central nervous system (CNS) may cause symptoms as a result of changes in pressure, rupture or secondary inflammation. The cyst-related details, such as its age, site, cyst wall, and its contents, also provide an insight into their embryology and histogenesis. This paper reviews the clinic-pathological features of cysts of CNS. Methods: In this study, a prospective analysis of the cysts of CNS diagnosed between November 2019 and October 2023 who presented to our outpatient department and managed at Command Hospital Lucknow and Base Hospital Delhi Cantt were reviewed. Written informed consent was taken from all patients. Cystic degeneration in tumors or inflammatory lesions was excluded from the study. Results: There were 35 cysts in the study period of 04 years. Majority of intracranial cysts presented with signs of raised intracranial pressure. Patients with epidermoid cysts had additional cerebellar signs, visual disturbances and deafness. Cord compression is the main presentation of all spinal cysts. Conclusion: Cysts of CNS are usually maldevelopment in nature. A few are acquired. The radiological identification of these cysts and pathological confirmation is necessary for prognostic purposes.

Paddy cultivation demands site-specific care from transplanting to post-harvest. Soil health cards offer farmers insights for effective fertilizer use, tailored to their soil’s condition. The research was led in the years 2023-2024 to know knowledge of soil health card by paddy growers in the north coastal of Andhra Pradesh. Samples were collected from six mandals across Rajam, Therlam, Bobbili from Vizianagaram district, and Ponduru, Hiramandalam, and Ecterla from Srikakulam district, totalling 180 respondents from 12 villages. Some Paddy growers have a comprehensive knowledge of SHCs and their importance in enhancing soil health and crop productivity, while others have limited awareness or misunderstandings. The research highlights obstacles to the utilization of SHCs, such as insufficient means of communication, and doubts regarding the effectiveness of SHCs. Findings revealed that the majority of respondents displayed a medium level of knowledge that is (45.00%) followed by paddy growers with low levels (27.77%) and (27.33%) paddy growers had a high level of knowledge about soil health cards.

The study examines the perception and adoption of Soil Health Card (SHC) practices among paddy growers in the North Coastal region of Andhra Pradesh for the years 2023- 2024. The Soil Health Card Scheme, launched by the Indian government to support sustainable agriculture, provides farmers with tailored recommendations for crop-specific fertilizers, and detailed insights into soil nutrition and improve crop productivity. Despite its potential advantages, the extent of SHC adoption and its impact on paddy cultivation in this region remain insufficiently explored. Using a mixed-methods approach, the study combines quantitative surveys and qualitative interviews with paddy farmers who had Soil Health Cards across the North Coastal regions like Srikakulam and Vizianagaram districts. It examines factors influencing SHC perception and adoption, including demographic characteristics, awareness levels, and service accessibility. The research also evaluates the scheme’s effects on crop productivity, economic returns, and soil fertility management. Findings reveal that while many paddy growers have Soil Health Cards and (40%) report a moderate perception of the scheme, the actual adoption rate is relatively low at (43.8%). Barriers such as delays in card issuance and soil sample processing contribute to farmers’ reluctance to fully implement the scheme, despite their awareness of its benefits.

Distal tibia fractures represent a significant challenge to most of the surgeons even today. They constitute 1-10% of all lower extremity fractures. Operative treatment is indicated for most tibia fractures caused by high energy trauma, which allows early mobilization, avoids shortening and other complications associated with prolonged immobilization. Conventional ORIF have been associated with complications like infection and delayed or non-union due to devitalization of bony fragments and additional damage to the soft tissues. Intramedullary nails often do not provide enough stability.

The process of examining the data flow over the internet to identify abnormalities in wireless network performance is known as network traffic analysis. When analyzing network traffic data, traffic classification becomes an important task. The traffic data classification is used to determine whether data in network traffic is in real-time or not. This analysis controls network traffic data in a network and allows for efficient network performance improvement. Real-time and non-real-time data are effectively classified from the given input data set using data mining clustering and classification algorithms. The proposed work focuses on the performance of traffic data classification with high clustering accuracy and low Classification Time (CT). This research work is carried out to fill the gap in the existing network traffic classification algorithms. However, the traffic data classification remained unaddressed for performing the network traffic analysis effectively. Then, we proposed an Enhanced Self-Learning-based Clustering Scheme (ESLCS) using an enhanced unsupervised algorithm and adaptive seeding approach to improve the classification accuracy while performing the real-time traffic data distribution in wireless networks. Test-bed results demonstrate that the proposed model enhances the clustering accuracy and True Positive Rate (TPR) effectively as well as reduces the CT time and Communication Overhead (CO) substantially to compare with the peer-existing routing techniques.

Since two or more person's signatures may appear to be identical, but a person's signature may differ reliant on the state, so, signature verification is a problematic research subject. The goal of this study is to see how well an Artificial-Neural-Network and a Local-Binary-Pattern feature set work together to create a Writer-Independent Offline-Signature verification system. The performance of system is evaluated using two datasets of signature, each with 260 and 100 writers. Authentic signatures of a person, as well as skilled-forgery, nonskilled-forgery, and random-forgery signs, are used to test the performance of the developed system, and authentic signatures, as well as skilled-forgery, nonskilled-forgery, and random-forgery signs, are taken into account in the development of the desired system. In this study, a false-acceptance-rate of 23.00 percent, 11.00 percent, and 0.00 percent was obtained for skilled-forgery signs, nonskilled-forgery signs, and random-forgery signs, respectively, while a false-rejection-rate of 0.00 percent was obtained for 15 reference signatures using a database of 260 writer’s signatures.

Verification of a signature is a problematic research topic since two or more people’s sign may appear to be identical, but a person’s sign may differ depending on the state. The purpose of this research is to examine how effectively an Artificial-Neural-Network and a Local-Binary-Pattern feature set can be combined to construct a Writer-Independent-Offline-Signature verification arrangement. The system’s performance is assessed using two signature datasets, each with 260 and 100 writers. Authentic signatures of a person, as well as skilled-forgery, nonskilled-forgery, and random-forgery signs, are used to test the performance of the developed system, and authentic signatures, as well as skilled-forgery, nonskilled-forgery, and random-forgery signs, are taken into account in the development of the desired system. In this study, a false-acceptance-rate of 21.00 percent, 11.00 percent, and 1.00 percent was obtained for skilled-forgery signs, nonskilled-forgery signs, and random-forgery signs, respectively, while a false-rejection-rate of 0.00 percent was obtained for 11 reference signatures using a database of 260 writer’s signatures.

Signature verification is a difficult research area since two individual's signatures may seem alike, but an individual's signature might change depending on the situation. The accuracy of the signature verification framework is mostly determined by the classifier and feature extraction scheme employed in the classification process. Keeping this in mind, the purpose of this research is to examine how well a support vector machine with polynomial kernel classifier and a Local Binary Pattern feature set can be coupled to create a writer-independent offline signature verification system. Two signature databases with 100 and 260 writers are employed to assess the system's performance. Genuine signatures, as well as random forgery are taken into account in the development of the desired system, and genuine signatures, as well as simulated forgery, unskilled forgery, and random forgery signatures are used to evaluate the developed system's performance. In a simulation investigation, the false acceptance rate for random, unskilled, and simulated forged signatures is achieved 0.00 %, 7.00 %, and 18.00 %, respectively, but the false rejection rate is achieved 0.00 % by utilizing the Local Binary Pattern feature set.

The verification of a signature is an exigent research area as the signatures of two individuals may have similarity whereas the signatures of a person may vary at various circumstances. The accuracy of signature verification framework relies mainly upon the classifier used for the classification process and the feature extraction scheme. Keeping this perspective in sight, the goal of this study is to see how well a decision tree classifier combined with a Local Binary Pattern feature set can be utilized to construct an offline writer-independent signature verification system. To evaluate the system’s performance, two signature databases of 100 and 260 writers are used. Genuine signatures as well as random forgery signatures are utilized for the development of the desired system, while genuine signatures, as well as random forgery, unskilled forgery, and simulated forgery are used to test the performance of the developed system. In simulation study, false acceptance rate of 1.00%, 7.00% and 11.00% for random, unskilled, and simulated forgery signatures, respectively is obtained whereas the false rejection rate of 0.00% is achieved using Local Binary Pattern features.

Measurement of soil properties continuously at each location throughout the globe is impossible for digitally mapping the global soil resources. It is necessary to have a strong system that can predict soil properties at a given location in short time and without extra expenditure. Developing models to predict the physical properties with the help of one out of several strongly related properties to each other is an attempt of this study. Bulk density which is an important factor influencing soils other properties and easy to estimate has been used as an independent variable .The study indicates a strong negative correlation (r= -0.936 to 0.999) between bulk density and different physical properties. Data shows that by using the regression equation developed in this study predicted value of different physical properties are almost 99.961 to 100 percent similar to observed values only 0.039 to 0 percent deviations in observed than predicted value was recorded. Hence these equations can be used to predict the physical properties for different textured soil in case the estimation is not practicable and may be functional to digital mapping of global soil resources.

BACKGROUND:Mucormycosis (MM) is a deadly opportunistic fungal infection and a large surge in COVID-19-associated mucormycosis (CAM) is occurring in India. AIM:Our aim was to delineate the clinico-epidemiological profile and identify risk factors of CAM patients presenting to the Emergency Department (ED). DESIGN:This was a retrospective, single-centre, observational study. METHODS:We included patients who presented with clinical features or diagnosed MM and who were previously treated for COVID-19 in last 3 months of presentation (recent COVID-19) or currently being treated for COVID-19 (active COVID-19). Information regarding clinical features of CAM, possible risk factors, examination findings, diagnostic workup including imaging and treatment details were collected. RESULTS:Seventy CAM patients (median age: 44.5 years, 60% males) with active (75.7%) or recent COVID-19 (24.3%) who presented to the ED in between 6 May 2021 and 1 June 2021, were included. A median duration of 20 days (interquartile range: 13.5-25) was present between the onset of COVID-19 symptoms and the onset of CAM symptoms. Ninety-three percent patients had at least one risk factor. Most common risk factors were diabetes mellitus (70%) and steroid use for COVID-19 disease (70%). After clinical, microbiological and radiological workup, final diagnosis of rhino-orbital CAM was made in most patients (68.6%). Systemic antifungals were started in the ED and urgent surgical debridement was planned. CONCLUSION:COVID-19 infection along with its medical management have increased patient susceptibility to MM.

Aim: To analyse the post-operative wound complications in patients getting neoadjuvant chemotherapy in respect to those patients who underwent surgery without any neoadjuvant chemotherapy. Material and method: The present prospective observational study was conducted in GMCH AMRITSAR Punjab.It consisted of 50 female patients admitted with breast carcinomadivided into group of 25 each, one group that received neoadjuvant chemotherapy and the other group which underwent surgery first. Results: The mean age of the patients was 50.16±11.57 years. no significant difference was observed in age, wound complications, healing time between the two groups. Flap necrosis was seen in 4% cases and 12% cases in group 1(primary surgery) and 2(NACT) respectively. Seroma was seen in 16% and 8% cases in group 1 and 2 respectively. 12% cases of group 1 and 20% cases of group 2 presented with wound infection. Patients with DM presented with more complication in both the groups. The difference between both the groups was significant which shows that diabetic patient without NACT have more risk of developing wound complications than with NACT.No significant difference was observed between the mean of healing time in both the groups . Conclusion:In conclusion, our study revealed that a factor, like diabetes mellitus was associated with an increased risk of wound complications for patients undergoing mastectomy. However, neoadjuvant chemotherapy was not associated with an increased risk of wound complications. Introduction Locally advanced breast cancers are an extremely heterogeneous group, ranging from neglected, relatively slow-growing, large primary tumours to small breast tumours presenting European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 08, Issue 03, 2021 2478 with extensive, nodal metastases. LABCs are usually defined by size >5 cm(T3), or primary cancer that involve skin/chest wall/inflammatory breast carcinoma (T4) independent of node status and/or cancer that are associated with fixed or matted axillary lymph nodes (N2a) or internal mammary lymph nodes(N2b/N3b). Surgical treatment for breast cancer includes breast conservation surgery and mastectomy with or without axillary dissection depending on disease stage. Often, the surgical treatment of breast cancer is accompanied by adjuvant or neoadjuvant therapy, including hormonal therapy, chemotherapy, and/or radiation therapy. In practice, the neoadjuvant approach is used routinely for patients with inoperable locally advanced breast cancer, including those with inflammatory breast cancer, those with large fixed or erosive lesions, not amenable to mastectomy and those with advanced nodal disease that is fixed, bulky or causing arm edema.It can downgrade the tumours, making it resectable with margin clearance. By reducing tumour burden in both the breast and the axilla, women may achieve complete resections with less extensive operations. Various clinical trials have shown that preoperative cytotoxic chemotherapy may have undesirable effects on surgical outcomes. It can delay the wound healing and increase the susceptibility to infections, thus raising concerns about the possible increased incidence of complications post-operatively. Delayed healing could be attributed to various side effects of preoperative chemotherapeutics, neutropenia being the most common one. Additionally, surgery associated complications may delay the initiation of adjuvant therapies, principally radiotherapy, which has been shown to reduce local recurrence rates, thus compromising the oncologic outcomes.Thus, in present study, an attempt has been made to analyse the postoperative wound complications in patients getting neoadjuvant chemotherapy in respect to those patients who underwent surgery without any neoadjuvant chemotherapy. Material and Methods The present prospective observational study was conducted in GMCH AMRITSAR Punjab. After approval from institutional ethical committee. It consisted of 50 female patients admitted with breast carcinoma, (with or without receiving neoadjuvant chemotherapy) in Department of General Surgery, GMCH AMRITSAR. Patients were divided into group of 25 each, one group that received neoadjuvant chemotherapy and the other group which underwent surgery first. Inclusion and Exclusion Criteria Female patients diagnosed with breast carcinoma, with or without neoadjuvant chemotherapy, giving valid written informed consent for surgery. Female recurrent breast carcinoma patients and Metastatic breast carcinomas were excluded from the study. Patients attending the surgical OPD were first examined clinically and relevant investigations like USG of both breasts with axilla, Mammogram and FNAC, besides routine investigations were done. Then according to NCCN (National Comprehensive Cancer Network) guidelines, breast cancer patients operated without neoadjuvant chemotherapy and patients with locally advanced breast cancer received neoadjuvant chemotherapy first, followed by surgery. Neoadjuvant chemotherapy regimens were given according to the standard protocol followed in our hospital. In the postoperative period, the patients were treated in the ward according to European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 08, Issue 03, 2021 2479 standard protocol like antibiotics, i.v fluids, analgesics and the various parameters, particularly the wound related complications were examined and tabulated. All wounds were examined post-operatively after 72 hours. lf there was any evidence of wound infection, and then antibiotic with a gram positive aerobic coverage was given intravenously. Wound swab for culture / sensitivity were sent thereafter, and antibiotics were changed according to the sensitivity reports. After discharge, patients were asked to visit OPD for regular follow up till 30 days or till complete healing of wound, whichever was longer. RESULTS AND OBSERVATIONS Out of 50 patients, 36% belonged to the age group 51 to 60 years, making it the largest age group. 16 patients were in the 41 to 50 years age group. The mean age of the patients was 50.16±11.57 years. The mean age of patients, between both the groups (group1= 53.12±9.5years and group 2= 51.6±10.6 years)showed no significant difference.(graph 1) Total 36% of patients developed some form of wound complication. 40 % of patients, who received NACT and 32 % of patients, who underwent primary surgery presented with wound complications. The difference between the two groups was statistically non-significant.(graph 2) Table 1 shows that in each group 6 patients were hypertensive followed by 5 patients each who had diabetes. Obesity, Hyperthyroidism, CAD, HIV/HCV and MR were present in 1 patient from both groups. There was no significant difference observed in comorbidity. Table 2 shows the different wound complications in different groups. Flap necrosis was seen in 4% cases and 12% cases in group 1(primary surgery) and 2(NACT) respectively. Seroma was seen in 16% and 8% cases in group 1 and 2 respectively. 12% cases of group 1 and 20% cases of group 2 presented with wound infection. No significant difference was observed among wound complications between both the groups. On comparing wound complications with diabetes mellitus it was observed that patients with DM presented with more complication in both the groups. The difference between both the groups was significant which shows that diabetic patient without NACT have more risk of developing wound complications than with NACT.(Table 3) In patients with NACT 16 patients underwent 3 cycles whereas 9 patients underwent 6 cycles. Out of 16 patients with 3 cycles, 7 patients presented with wound complications as compared to 3 patients with 6 cycles. The difference between the two was non significant(Table 4). The mean healing time for patients in group 1 was 15.84 days and that for group 2 was 18 days. No significant difference was observed between the mean of healing time in both the groups however, in group 2(NACT) the healing time was more than in group 1(primary surgery).(Table 5) Graph 3 shows the comparison of gap between last NACT cycle and surgery in weeks. Total 14 patients had a gap of 3 weeks between last NACT cycle and surgery, out of which 7 presented with wound complications and 11 had a gap of 4 weeks between last NACT cycle and surgery out of which 3 patients presented with wound complications. Graph 4 shows the European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 08, Issue 03, 2021 2480 comparison of NACT regimen and wound complications. No statistically significant association was seen between the two regimens.

Removing the smog from digital images is a challenging pre-processing tool in various imaging systems. Therefore, many smog removal (i.e., desmogging) models are proposed so far to remove the effect of smog from images. The desmogging models are based upon a physical model, it means it requires efficient estimation of transmission map and atmospheric veil from a single smoggy image. Therefore, many prior based restoration models are proposed in the literature to estimate the transmission map and an atmospheric veil. However, these models utilized computationally extensive minimization of an energy function. Also, the existing restoration models suffer from various issues such as distortion of texture, edges, and colors. Therefore, in this paper, a convolutional neural network (CNN) is used to estimate the physical attributes of smoggy images. Oblique gradient channel prior (OGCP) is utilized to restore the smoggy images. Initially, a dataset of smoggy and sunny images are obtained. Thereafter, we have trained CNN to estimate the smog gradient from smoggy images. Finally, based upon the computed smog gradient, OGCP is utilized to restore the still smoggy images. Performance analyses reveal that the proposed CNN-OGCP based desmogging model outperforms the existing desmogging models in terms of various performance metrics.

Functional genomics enhances the understanding of fundamental fungal biology and aids in improving the production of valuable bioactive fungal compounds. Available gene manipulation approaches to study filamentous fungi are generally inefficient, time-consuming, and laborious. A robust genomic technology CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein) is a simple, effective, and precise genome editing tool that has revolutionized gene editing and systematic research on filamentous fungi. This chapter provides an insight into the concepts and recent developments of CRISPR-Cas9 tools for the precise change of endogenous genes and for the complete knockout of their expression, transcriptional regulation and multiplex genome editing. In addition, the current and potential applications of the CRISPR-Cas9 system for decoding and confirmation of the fungal pathogenesis, metabolite engineering, biocontrol, chromatin dynamics, multiple signaling cascades, and cell imaging are highlighted. The chapter also describes various challenges faced in the development and applications of the CRISPR-Cas9 system. Further, the dilemmas associated with the potential applications of these tools in filamentous fungi are explored.