Enhanced Disease Detection for Potato Crop Using CNN with Transfer Learning

As the fourth most popular basic food in the world,potatoes are widely available. In addition, the worldwidemarket is causing the demand to rise daily. Diseases likeearly and late blight have a significant impact on the quantity and quality of potatoes. Determining which potato leaves are afflicted with a certain illness becomes more challenging when interpreting these diseasesmanually. Thankfully, it is possible to identify potato leafdiseases by examining the leaf conditions. This proposedstudy presents a technique that employs deep learning toidentify the two types of diseases and generates an accurate classifier using heavy designs for convolutionalneural networks, such as GoogleNet, Resnet15, VGG16,and Xception. We achieved 97% accuracy in the first 40 CNN epochs, demonstrating the practicality of the deep neural network approach

Psychiatric comorbidity in substance abusing population in Garhwal hills of Uttarakhand

Background: Psychiatric morbidity occurs more frequently in patients with substance abuse than in the general population. Routine evaluation and treatment of psychiatric morbidity can be helpful in improving care of substance abusing population but such data are relatively meager from developing countries.Methods: This study was conducted in the Out-patient facility of the Department of Psychiatry, Veer Chandra Singh Garhwali Government Institute of Medical Science & Research, Srinagar (Uttarakhand), starting from 23 September 2015. One hundred consecutive treatment seeking subjects fulfilling international classification of diseases and related health problems, 10th Revision (ICD-10), criteria for mental and behavioral disorders due to psychoactive substance use were included in the study. All the participants were required to sign an informed consent approved by the institutional ethical committee before being enrolled in the study. All the subjects included in the study were administered a semi-structured Proforma to elicit the clinical and socio-demographic variables.  Results: One hundred patients consisting of 95 men (95%) and 05 women (05%) were included. The average age of the sample was 39.68 years (SD=11.97). As for the socio-demographic variables other than age, 87% of the patients were married, 62% patients were living in nuclear families and 66% belonged to the rural areas. 79% patients were educated up to high school and above and only 06% were illiterate. 36 % of the subjects screened positive for psychiatric morbidity. Psychiatric morbidity was significant higher in unmarried people with less education (primary or less) and those living in nuclear families.  Conclusions: Psychiatric comorbidity was found in 36% of the study subjects

Assessment of prescribing trends for rational use of drugs

Background: Nowadays irrational use of drugs is a major problem inspite of extensive programs being carried out on rational use of medicines. Therefore, in present study we evaluated OPD prescriptions for rationality and their adherence to prescription format.Methods: A prospective, observational study was carried out in 511 outdoor patients for a period of three months. Quality of prescription writing was assessed for completeness of information and legibility. Rationality was analyzed using WHO core prescribing indicators.Results: Basic information of patient and name of department were written in all the prescriptions. Diagnosis was mentioned in 76.33% cases. Dosage forms, dose, frequency and duration of treatment were mentioned in 97.26%, 73%, 80.04% and 80.23% of prescriptions respectively. About 73.78% prescriptions were legible. Doctor’s name, signature and registration number were present in 80.82%, 82.97% and 15.66%. Total number of drugs in 511 cases was 1074. Average number of drugs/ prescriptions was 2.1±0.8. Drugs were prescribed by generic name in 25.14% cases; drugs from EDL were 57.36%. Antimicrobial agents, injectable drugs and FDCs were prescribed in 25.83%, 12.13% and 39.14% cases. The most commonly prescribed drugs were analgesics, GIT and cardiovascular drugs.Conclusions:This study shows possible areas of improvement in prescription practice that is generic prescribing, use of essential medicines, restraint in use of irrational fixed dose combinations and better quality of prescribing in terms of inclusiveness of information, legibility and doctor’s details.

Comparison in Thermal Conductivity of Hollow Concrete blocks filled with Straw Bales & Tyre Waste

The thermal conductivity of straw bales is an intensively discussed topic in the international straw bale community. Straw bales are, by nature, highly heterogeneous and porous. They can have a relatively large range of density and the baling process can influence the way the fibers are organized within the bale. In addition, straw bales have a larger thickness than most of the insulating materials that can be found in the building industry. Measurement apparatus is usually not designed for such thicknesses, and most of the thermal conductivity values that can be found in the literature are defined based on samples in which the straw bales are resized. During this operation, the orientation of the fibers and the density may not be preserved. This paper starts with a literature review of straw bale thermal conductivity measurements and presents a measuring campaign performed with a specific Guarded Hot Plate, designed to measure samples up to 40 cm thick. The influence of the density is discussed thoroughly. Representative values are proposed for a large range of straw bales to support straw-bale development in the building industry This paper comprises the tests performed to determine the thermal conductivity of hollow concrete blocks using straw bales filled in hollow concrete block. The purpose of this study is to examine the possibility of using straw bales in hollow concrete block. The straw bales were used to make concrete block in the masonry units. This study examines the thermal behavior of concrete construction elements (bricks, slabs) made filled with different amounts of straw bales particles (80%, 70% and 60%) according with different thickness of concrete. Once the bricks, slabs were obtained, three different closed test cells were built and subjected to several heating/cooling periods. By recording the temperature difference between inside and outside the wall of concrete block, it was found that the thermal behavior depend on the filling percentage of tyre waste particles. This study is based on the human and atmospheric comfort in building structures in different environment condition

Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization

Modern ML applications increasingly rely on complex deep learning models and large datasets. There has been an exponential growth in the amount of computation needed to train the largest models. Therefore, to scale computation and data, these models are inevitably trained in a distributed manner in clusters of nodes, and their updates are aggregated before being applied to the model. However, a distributed setup is prone to Byzantine failures of individual nodes, components, and software. With data augmentation added to these settings, there is a critical need for robust and efficient aggregation systems. We define the quality of workers as reconstruction ratios $\in (0,1]$, and formulate aggregation as a Maximum Likelihood Estimation procedure using Beta densities. We show that the Regularized form of log-likelihood wrt subspace can be approximately solved using iterative least squares solver, and provide convergence guarantees using recent Convex Optimization landscape results. Our empirical findings demonstrate that our approach significantly enhances the robustness of state-of-the-art Byzantine resilient aggregators. We evaluate our method in a distributed setup with a parameter server, and show simultaneous improvements in communication efficiency and accuracy across various tasks. The code is publicly available at https://github.com/hamidralmasi/FlagAggregato

The Effects of Extended Depletion Region on Noise Modeling of HEMT ’s

In this paper we present a high frequency noise model for short channel HEMTs.This model takes into account the effect of depletion region that extends into gate to drain spacing.The effect of this high field extension region on the noise performance of three HEMT structures is analytically calculated and compared with measured data

Accelerated Neural Network Training with Rooted Logistic Objectives

Many neural networks deployed in the real world scenarios are trained using cross entropy based loss functions. From the optimization perspective, it is known that the behavior of first order methods such as gradient descent crucially depend on the separability of datasets. In fact, even in the most simplest case of binary classification, the rate of convergence depends on two factors: (1) condition number of data matrix, and (2) separability of the dataset. With no further pre-processing techniques such as over-parametrization, data augmentation etc., separability is an intrinsic quantity of the data distribution under consideration. We focus on the landscape design of the logistic function and derive a novel sequence of {\em strictly} convex functions that are at least as strict as logistic loss. The minimizers of these functions coincide with those of the minimum norm solution wherever possible. The strict convexity of the derived function can be extended to finetune state-of-the-art models and applications. In empirical experimental analysis, we apply our proposed rooted logistic objective to multiple deep models, e.g., fully-connected neural networks and transformers, on various of classification benchmarks. Our results illustrate that training with rooted loss function is converged faster and gains performance improvements. Furthermore, we illustrate applications of our novel rooted loss function in generative modeling based downstream applications, such as finetuning StyleGAN model with the rooted loss. The code implementing our losses and models can be found here for open source software development purposes: https://anonymous.4open.science/r/rooted_loss

Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data

Real-world datasets exhibit imbalances of varying types and degrees. Several techniques based on re-weighting and margin adjustment of loss are often used to enhance the performance of neural networks, particularly on minority classes. In this work, we analyze the class-imbalanced learning problem by examining the loss landscape of neural networks trained with re-weighting and margin-based techniques. Specifically, we examine the spectral density of Hessian of class-wise loss, through which we observe that the network weights converge to a saddle point in the loss landscapes of minority classes. Following this observation, we also find that optimization methods designed to escape from saddle points can be effectively used to improve generalization on minority classes. We further theoretically and empirically demonstrate that Sharpness-Aware Minimization (SAM), a recent technique that encourages convergence to a flat minima, can be effectively used to escape saddle points for minority classes. Using SAM results in a 6.2\% increase in accuracy on the minority classes over the state-of-the-art Vector Scaling Loss, leading to an overall average increase of 4\% across imbalanced datasets. The code is available at: https://github.com/val-iisc/Saddle-LongTail.Comment: NeurIPS 2022. Code: https://github.com/val-iisc/Saddle-LongTai