A secure food supply chain solution: blockchain and IoT-enabled container to enhance the efficiency of shipment for strawberry supply chain

The supply chain systems in the food industry are complex, including manufacturers, dealers, and customers located in different areas. Currently, there is a lack of transparency in the distribution and transaction processes of online food trade. The global food supply chain industry has enormous hurdles because of this problem, as well as a lack of trust among individuals in the sector and a reluctance to share information. This study aims to develop a blockchain-based strawberry supply chain (SSC) framework to create a transparent and secure system for tracking the movement of strawberries from the farm to the consumer. Using Ethereum smart contracts, the proposed solution monitors participant interactions, triggers events, and logs transactions to promote transparency and informed decision-making. The smart contracts also govern interactions between vendors and consumers, such as monitoring the status of Internet of Things (IoT) containers for food supply chains and notifying consumers. The proposed framework can be extended to other supply chain industries in the future to increase transparency and immutability

Financing Decisions and the Role of CSR in Donation-Based Crowdfunding - Evidence from Pakistan and Indonesia

Donation-based crowdfunding and corporate social responsibility (CSR) activities have potential symbiotic ramifications to raise funds, but campaigners are confronted with challenges and competition to accomplish their charitable target. For instance, CSR activities could warrant the possibility of using crowdfunding to raise money. On the other hand, a company\u27s CSR objectives can be achieved by using crowdfunding to micro-fund various social initiatives. Current research investigates the relationship between fundraisers in donation-based crowdfunding activities, which become potential CSR activities. Exclusively, the study analyzes the correlation among the value raised at the end of fundraising activity, the amounts targeted by the fundraiser, and CSR-Type activities on the project\u27s success in donation-based crowdfunding. Based on this, a research taxonomy has been established for a comparative analysis between Pakistan and Indonesia. Secondary data is collected from donation-based platforms and analyzed through Ordinary Least Square (OLS) regression and the models are validated using a robustness check. The outcomes show that a higher value raised (V) correlates more positively with project success in Pakistan (164) as compared with Indonesia (122). The Target fund (T) has a significant and negative association with the project\u27s success in the Pakistani market, however, the significant and negative effect on the project’s success in the Indonesian market. Lastly, CSR-related activities such as education, environment, community, and health have a positive relationship with project success in Pakistan, except for the product which has a negative, however significant relationship. In contrast, for Indonesia, CSR-type activities such as education, environment, community, product, and health have a positive and significant relationship with the project\u27s success. This study contributes to the donation-based crowdfunding literature to develop a vivid understanding of different CSR activities and their impact on the project\u27s success. The current study is one of the first to examine the significance of CSR activities and will enrich the body of knowledge regarding crowdfunding in diverse economies

Topic Modeling based text classification regarding Islamophobia using Word Embedding and Transformers Techniques

Islamophobia is a rising area of concern in the current era where Muslims face discrimination and receive negative perspectives towards their religion, Islam. Islamophobia is a type of racism that is being practiced by individuals, groups, and organizations worldwide. Moreover, the ease of access to social media platforms and their augmented usage has also contributed to spreading hate speech, false information, and negative opinions about Islam. In this research study, we focused to detect Islamophobic textual content shared on various social media platforms. We explored the state-of-the-art techniques being followed in text data mining and Natural Language Processing (NLP). Topic modelling algorithm Latent Dirichlet Allocation is used to find top topics. Then, word embedding approaches such as Word2Vec and Global Vectors for word representation (GloVe) are used as feature extraction techniques. For text classification, we utilized modern text analysis techniques of transformers-based Deep Learning algorithms named Bidirectional Encoders Representation from Transformers (BERT) and Generative Pre-Trained Transformer (GPT). For results comparison, we conducted an extensive empirical analysis of Machine Learning algorithms and Deep Learning using conventional textual features such as the Term Frequency-Inverse Document Frequency, N-gram, and Bag of words (BoW). The empirical based results evaluated using standard performance evaluation measures show that the proposed approach effectively detects the textual content related to Islamophobia. In the corpus of the study under Machine Learning models Support Vector Machine (SVM) performed best with an F1 score of 91%. The Transformer based core NLP models and the Deep Learning model Convolutional Neural Network (CNN) when combined with GloVe performed best among all the techniques except SVM with BoW. GPT, SVM when combined with BoW and BERT yielded the best F1 score of 92%, 92% and 91.9% respectively, while CNN performed slightly poor with an F1 score of 91%

Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification

In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex in nature. Concurrently, DL (deep learning) methodologies can show prominent outcomes in the classification of DFU because of their efficient learning capacity. Though traditional systems have tried using DL-based models to procure better performance, there is room for enhancement in accuracy. Therefore, the present study uses the AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) method to classify DFU. A DFUC dataset is considered, and several processes are involved in the present study. Initially, the proposed method starts with pre-processing, excluding inconsistent and missing data, to enhance dataset quality and accuracy. Further, for classification, the proposed method utilizes the process of RIW (random initialization of weights) and log softmax with the ASGO (Adaptive Sub-gradient Optimizer) for effective performance. In this process, RIW efficiently learns the shift of feature space between the convolutional layers. To evade the underflow of gradients, the log softmax function is used. When logging softmax with the ASGO is used for the activation function, the gradient steps are controlled. An adaptive modification of the proximal function simplifies the learning rate significantly, and optimal proximal functions are produced. Due to such merits, the proposed method can perform better classification. The predicted results are displayed on the webpage through the HTML, CSS, and Flask frameworks. The effectiveness of the proposed system is evaluated with accuracy, recall, F1-score, and precision to confirm its effectual performance

Source rock geochemical assessment and estimation of TOC using well logs and geochemical data of Talhar Shale, Southern Indus Basin, Pakistan

Assessment of organic carbon content (TOC) by geophysical logs has been a challenging task in the formation evaluation of shale gas. This research is conducted to estimate the unconventional hydrocarbon (shale-gas) potential of Talhar Shale in the Southern Indus Basin (SIB), Pakistan. In this study, total organic carbon content (%) was estimated through well logs by different methods and then correlated with well cuttings/core data to determine the best method for estimation of TOC content especially when well cuttings/core data are not available. The Talhar Shale’s thermal maturity, as well as the organic content, were assessed from geochemical analyses. Talhar Shale of Dangi-01 well has good to very good source potential whereas Chak7A-01 well has fair-good. According to Ven Krevalen cross-plot, Talhar Shale of Dangi-01 well has type III kerogen; it can only produce gas while Chak7A-01 has type II/III kerogen which produces both oil and gas. The TOC was estimated using two methods i.e., Schmoker’s and Hester’s and Multivariate Fitting methods. The estimated TOC is then correlated with well cuttings data and concluded that the Multivariate Fitting method is selected as an optimized method for estimation of TOC because it shows strong correlation values of 0.93 and 0.91 in both wells respectively for Talhar Shale SIB, Pakistan

Highly efficient catalytic degradation of low-density polyethylene using a novel tungstophosphoric acid/kaolin clay composite catalyst

In order to take advantage of Bronsted acidity of tungstophosphoric acid(TPA) and Lewis acidity of kaolin, TPA loaded kaolin catalysts with varying percentages of TPA (10-50wt%) have been prepared by wet impregnation method. Fourier Transform Infra-Red Spectrometer, X-ray diffractometer, Brunauer-Emmett-Teller surface area analyzer, and Scanning Electron Microscope characterizations were performed to confirm the successful loading of TPA onKaolin. Catalytic cracking of low-density polyethylene (LDPE), by employing our TPA loaded Kaolin as the catalyst, produced a higher percentage of fuel oil (liquid and gaseous hydrocarbons) with negligible amount of semisolid wax (1.0 wt.%), significantly lower compared to the thermal cracking which produced ~22wt.% solid black residue. Moreover, GCMS analysis of oil showed that thermal cracking produces mainly higher hydrocarbons(C22) as compared to the catalytic cracking where larger fraction oflowerhydrocarbons were obtained. We purpose that the higher performance of our catalysts was due to the presence of both Bronsted and Lewis acid sites, which increase their catalytic efficiency and degraded the LDPE at the relatively lower temperatures. Our results suggest that prepared materials were effectivecatalysts with low cost and easily scalable production method; suitable for large-scale highperformance catalytic cracking of LDPE based materials

Colon histology slide classification with deep-learning framework using individual and fused features

Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data

Increasing Trend of Silver Nanoparticles as Antibacterial and Anticancer Agent

Silver nanoparticles (AgNPs) synthesis from plants that already have been reported for medicinal purposes demonstrated better efficacy for curing diseases. Recently, a number of researches have been reported where AgNPs act as promising antibacterial and anticancer agent. Biosynthesized silver nanoparticles (AgNPs) are a type of environmentally friendly, cost-effective, and biocompatible substance that has gotten a lot of attention in treatment of cancer and inhibition of pathogenic microbes. In this chapter, a comprehensive report on the recent development of AgNPs as nanomedicine synthesized from plant extracts. The role and mechanism of AgNPs as antibacterial and anticancer agent was reported that leads towards development of targeted nannomedicines to treat infectious diseases and world most challenging disease like cancer. Reported literature give imminence importance of AgNPs and demonstrated more potency to treat cancer and bacterial infections

A Hybrid Duo-Deep Learning and Best Features Based Framework for Action Recognition

Human Action Recognition (HAR) is a current research topic in the field of computer vision that is based on an important application known as video surveillance. Researchers in computer vision have introduced various intelligent methods based on deep learning and machine learning, but they still face many challenges such as similarity in various actions and redundant features. We proposed a framework for accurate human action recognition (HAR) based on deep learning and an improved features optimization algorithm in this paper. From deep learning feature extraction to feature classification, the proposed framework includes several critical steps. Before training fine-tuned deep learning models – MobileNet-V2 and Darknet53 – the original video frames are normalized. For feature extraction, pre-trained deep models are used, which are fused using the canonical correlation approach. Following that, an improved particle swarm optimization (IPSO)-based algorithm is used to select the best features. Following that, the selected features were used to classify actions using various classifiers. The experimental process was performed on six publicly available datasets such as KTH, UT-Interaction, UCF Sports, Hollywood, IXMAS, and UCF YouTube, which attained an accuracy of 98.3%, 98.9%, 99.8%, 99.6%, 98.6%, and 100%, respectively. In comparison with existing techniques, it is observed that the proposed framework achieved improved accuracy