LSTM-SDM: An integrated framework of LSTM implementation for sequential data modeling[Formula presented]

LSTM-SDM is a python-based integrated computational framework built on the top of Tensorflow/Keras and written in the Jupyter notebook. It provides several object-oriented functionalities for implementing single layer and multilayer LSTM models for sequential data modeling and time series forecasting. Multiple subroutines are blended to create a conducive user-friendly environment that facilitates data exploration and visualization, normalization and input preparation, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. We utilized the LSTM-SDM framework in predicting the stock market index and observed impressive results. The framework can be generalized to solve several other real-world time series problems

Growth status, curd yield and crop duration of late season cauliflower varieties

Cauliflower is an important winter season vegetable crop having year-round demand in Nepal. Due to longer crop duration in late winter season, there was a production of poor-quality curds and lower yield faced by the farmers in Terai region of Nepal. An experiment was conducted to identify the short duration late season varieties at Rampur, Chitwan Nepal during November 2016 to March, 2017. These varieties were Freedom, Titan, Ravella, Amazing, Artica, Bishop, Casper, Indam 9803 and NS 106 (introduced from USA, Europe and India), and Snow Mystique and Snowball 16 (introduced from Japan). The experiment was laid out in Randomized Complete Block Design (RCBD) with four replications. The highest plant height (71.9 cm) and canopy diameter (74.5 cm) at last harvest of cauliflower was mostly produced by Titan followed by NS 106, Snow Mystique, Bishop and Indam 9803. Similarly, significantly shorter period for final curd initiation of 65 days after transplanting was observed in Freedom and shorter period for final curd maturation of 77 days after transplanting was also recorded in Freedom than other varieties. Significantly, higher curd yield of 54.8 t/ha was produced by Bishop than other varieties. In conclusion, Bishop was the best hybrid variety while other suitable varieties were NS 106, Titan, Artica and Snow Mystique for better growth and higher curd yield in Chitwan condition. Similarly, Freedom was identified as short duration varieties which can minimize the negative effects in late winter season due to higher temperature

Screening of health beneficial microbes with potential probiotic characteristics from the traditional rice-based alcoholic beverage, haria

Fermented foods are natural habitats of various food-grade microorganisms which not only fortify the food material with bioactive molecules, but they could directly exert health beneficial effect to the consumers. The present study aimed to screen the microbial consortium of haria (a traditional alcoholic rice beverage), for therapeutic potentiality. Twenty-nine fermented beverage samples were collected from different areas of Bankura District (West Bengal, India).Initially, 45 dominant bacterial isolates were purified from the collected samples. From these, 3 microorganisms were screened out based on growth and acidification kinetics: these proved to be Bifidobacterium sp. (MKK4), Pediococcus lolli (MKK21), and Lactobacillus sp. (MKK37) isolates. Finally, based on a cumulative probiotic score, MKK4 (Bifidobacterium sp.) was selected for further studies. The ubiquitous presence of this strain in the collected samples was confirmed through PCR-DGGE fingerprinting. This strain was considerably stable in simulated acid and bile solutions; it also exhibited strong auto-aggregation and cell surface hydrophobicity of 82% and 53%, respectively. Under conditions of nutrient depletion, the isolate was capable to form biofilm (66.3%). The selected bacterium showed strong antimicrobial activities against Shigella dysenteriae MB14, Salmonella typhi E 1590, Micrococcus luteus ATCC 9341, Staphyloccus aureusMB13, Vibrio cholerae K510, and Escherichia coli ATCC 25922 isolates. These results suggest that the food-borne Bifidobacterium sp. MKK4 can be used as potent probiotic agent

Local Intrinsic Density Based Community Detection Using Branch-and-Bound and Minimum Spanning Tree

Community detection in complex networks has emerged as a fundamental research area during the last decade. Traditional community detection is primarily based on static techniques, which are neither practicable regarding limited resources nor appropriate for the evolving complex networks. Dynamically updating the community structures, getting accurate and real-time results are vital constraints for analyzing evolving networks, hence in our research, we take these possibilities into consideration. Our work proposes a novel community detection approach BBmst inspired by the original branch-and-bound (BB) concept for label propagation and a Minimum Spanning Tree (MST) to measure the dissimilarity between nodes in graphs. These proposed methods consist of four phases: finding of the local intrinsic density called core or granule of the community, label propagation with BB, an MST is employed to segregate the detected communities and finally a modularity-based community merging is performed. The performance of our proposed method is assessed against a few well-known community detectors on real-world social networks concerning various validity measures. Results demonstrated our proposed method superiority over current community detectors and showed a considerable trade-off between cluster accuracy and quality

Agriculture Education

Class 8 (Nepali Date: 2053); Language: Nepal

Plant and Salamander Inspired Network Attack Detection and Data Recovery Model

The number of users of the Internet has been continuously rising, with an estimated 5.1 billion users in 2023, which comprises around 64.7% of the total world population. This indicates the rise of more connected devices to the network. On average, 30,000 websites are hacked daily, and nearly 64% of companies worldwide experience at least one type of cyberattack. As per IDC’s 2022 Ransomware study, two-thirds of global organizations were hit by a ransomware attack that year. This creates the desire for a more robust and evolutionary attack detection and recovery model. One aspect of the study is the bio-inspiration models. This is because of the natural ability of living organisms to withstand various odd circumstances and overcome them with an optimization strategy. In contrast to the limitations of machine learning models with the need for quality datasets and computational availability, bio-inspired models can perform in low computational environments, and their performances are designed to evolve naturally with time. This study concentrates on exploring the evolutionary defence mechanism in plants and understanding how plants react to any known external attacks and how the response mechanism changes to unknown attacks. This study also explores how regenerative models, such as salamander limb regeneration, could build a network recovery system where services could be automatically activated after a network attack, and data could be recovered automatically by the network after a ransomware-like attack. The performance of the proposed model is compared to open-source IDS Snort and data recovery systems such as Burp and Casandra

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%

Narrative Paragraph Generation for Photo Stream Using Neural Networks

Humans have the innate ability to perceive an image just by looking at it, for us images are not just a collection of objects but a network of interconnected object relationships. The problem arises when a machine tries to inspect an image, hence we try to convert image data to textual data. Despite major achievements in the image captioning field, there is a lack of models that provide concise captions of a given image, moreover, already existing models are so much bigger in size that the number of learning parameters is very high. The objective of this paper is to fill that gap, hence we provide an image captioning model that will be utilizing a small and good CNN architecture which is relatively new in the research field and is not used much. Our model incorporates an advanced Deep Convolution Neural Network to extract image features and an Attention GRU with a local attention network to generate captions. We have also identified a class imbalance problem with this popular dataset so we tried to rectify this problem by adding some images of some specific classes, hence improvising the dataset as well. The model has been trained on this improvised Flickr Dataset.</p

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Not AvailableCurry products are ethnic spicy Indian food products prepared with meat, vegetables, spices, condiments etc. Different curry products prepared from pork, traditionally in Assam state of North Eastern India have been documented in this study. Formulation and procedure followed for preparation of five pork curry products viz., pork - thekera tenga curry, pork - fermented bamboo shoot curry, pork - el- ephant apple curry, pork - banana flower curry and pork - banana stem curry was gathered by undertaking survey and interacting with local processors of the region. These products were analyzed for standard quality parameters like physicochemical characteristics, mi- crobiological quality and sensory acceptability. Storage stability of the products under aerobic refrigeration (4±1 oC) was also evaluated. Products were stable up to 20 days of storage under aerobic refrigeration condition. The study revealed that ingredients used in curry preparation like thekera tenga, fermented bamboo shoot and elephant apple contribute significantly to sensory attributes and also help in enhancing storage stability of the pork curry products.Not Availabl