bluuebunny/crossref_metadata_2025_split huggingface dataset:
Endogenous irrigation in arid Zimbabwe: farmer perceptions of livelihood benefits and barriers to scaling
Authors: Moline Chauruka  , Annelieke Duker  , Pooja Prasad  , Pieter Van der Zaag  
Published: October 2023

In Zimbabwe, farmer-led irrigation is far more widespread than planners and policy makers realise. Along the Shashani sand river, in the arid to semi-arid lands of south-western Zimbabwe, diverse farmer-initiated irrigation ventures exist. This qualitative case study focuses on bucket irrigation, in which very small vegetable fields of up to 450 m2 are fenced by tree branches, and irrigated with water from scoop holes in sandy river beds. Farmers initiate and operate their fields with no external assistance. This study presents the benefits of bucket irrigation as an often-overlooked form of farmer-led irrigation development. Through this qualitative and strongly observational study, 26 bucket irrigation farmers and 4 non-irrigators were interviewed using semi-structured interviews where farmers’ perceptions and experiences were captured. We investigate what drives and sustains bucket irrigation, its significance to rural livelihoods under harsh economic and climatic conditions, and the barriers towards scaling this type of farmer-led irrigation development. The results show that drivers for bucket irrigation stem from economic hardship and are gendered. Women are motivated to irrigate mainly by the need to produce vegetables for household consumption, whereas men pursue irrigation due to a lack of employment. Bucket irrigators experience enhanced food security, and have more secure income, contributing to improved wellbeing. Furthermore, despite the desire to scale, the farm size is mainly constrained by fencing and energy for transporting water, which is a result of a persistent lack of financial capital to invest in irrigation technologies. We conclude that bucket irrigation acts as an important livelihood strategy, and that it significantly enhances farmers’ resilience to economic and climatic shocks. Bucket irrigation should not be overlooked in policies that advocate scaling of irrigation. Bucket irrigators have the potential to expand and benefit significantly if supported with innovative financial mechanisms that enable investments in the required technology and knowledge.
Occurrence of multidrug-resistant Escherichia coli and antibiotic resistance genes in a wastewater treatment plant and its associated river water in Harare, Zimbabwe
Authors: Hilary Takawira  , Joshua Mbanga  
Published: October 2023

Wastewater treatment plants (WWTPs) have been identified as point sources of antibiotic-resistant bacteria (ARB) and antibiotic-resistance genes (ARG). Due to variations in antibiotic use and prescribing patterns in different countries, it is imperative to establish the presence of ARB and ARGs in water environments on a country-by-country basis. This study investigated the occurrence of 11 antibiotic-resistance genes (QNRB, DFR14, CTX-M, KPC, Sul1, QNRA, Sul2, ERMB, ERMA, SHV, NDM), and antibiotic-resistant Escherichia coli in a WWTP and its associated river water in Harare, Zimbabwe. 24 water samples were collected across 3 sites: upstream and downstream of the WWTP; final effluent of the WWTP. The samples were collected weekly for 8 weeks. Pure cultures of the E. coli isolates were obtained by membrane filtration (0.45 µm) and repeated streaking on Tryptone Bile X-glucuronide followed by biochemical tests (indole test; citrate test; motility, indole, and ornithine). Antibiotic resistance profiling was done for 12 antibiotics using the disc diffusion method. Total genomic DNA was extracted from the 21 water samples and the occurrence of 11 antibiotic-resistant genes investigated using conventional PCR. 86 E. coli isolates were obtained from the sampled sites: 28 from the upstream site, 26 from the WWTP effluent, and 32 from the downstream site. The results from chi-squared analysis showed a significant association (p < 0.05) between the sampling site and the percentage of antibiotic-resistant E. coli for all 12 antibiotics investigated. The percentage of E. coli isolates resistant to the tested antibiotics varied from 29% (ertapenem) to 80.2% (ciprofloxacin). 81 (94.2%) E. coli isolates were resistant to antibiotics from ≥3 classes. Eight (8/11, 72.7%) ARGs were detected in the WWTP effluent and river water samples. Results indicate that the investigated WWTP and associated river water are reservoirs of ARGs and antibiotic-resistant E. coli, which is a public health concern.
CEP-DTHP : A Complex Event Processing using the Dual-Tier Hybrid Paradigm Over the Stream Mining Process
Authors: Panpaliya Mayur , Ranjan Nihar , Algude Arun 
Published: October 2023

CEP is a widely used technique for the reliability and recognition of arbitrarily complex patterns in enormous data streams with great performance in real time. Real-time detection of crucial events and rapid response to them are the key goals of sophisticated event processing. The performance of event processing systems can be improved by parallelizing CEP evaluation procedures. Utilizing CEP in parallel while deploying a multi-core or distributed environment is one of the most popular and widely recognized tackles to accomplish the goal. This paper demonstrates the ability to use an unusual parallelization strategy to effectively process complicated events over streams of data. This method depends on a dual-tier hybrid paradigm that combines several parallelism levels. Thread-level or task-level parallelism (TLP) and Data-level parallelism (DLP) were combined in this research. Many threads or instruction sequences from a comparable application can run concurrently under the TLP paradigm. In the DLP paradigm, instruc-tions from a single stream operate on several data streams at the same time. In our suggested model, there are four major stages: data mining, pre-processing, load shedding, and optimization. The first phase is online data mining, following which the data is materialized into a publicly available solution that combines a CEP engine with a library. Next, data pre-processing encompasses the efficient adaptation of the content or format of raw data from many, perhaps diverse sources. Finally, parallelization approaches have been created to reduce CEP processing time. By providing this two-type parallelism, our proposed solution combines the benefits of DLP and TLP while addressing their constraints. The JAVA tool will be used to assess the suggested technique. The performance of the suggested technique is compared to that of other current ways for determining the efficacy and efficiency of the proposed algorithm.
Enhancing Data Security in Healthcare IoT: An Innovative Blockchain-based Solution
Authors: Patel Ochchhav , Patel Hiren 
Published: October 2023

The Internet of Things (IoT) has revolutionized the healthcare industry by enabling the seamless integration of medical devices, sensors, and data-driven applications. However, the large influx of sensitive healthcare data and the proliferation of linked devices have caused grave worries about data security and privacy. Traditional centralized security systems are unable to handle the changing threats and problems in the IoT healthcare setting. This study suggests a novel strategy for boosting data security in the healthcare industry that makes use of blockchain technology. The main goal of this research is to develop and deploy a trustworthy framework that safeguards private healthcare information in IoT networks. Blockchain, as a distributed and decentralized ledger, offers inherent security features such as immutability, transparency, and cryptographic mechanisms. In this research, it is suggested that healthcare data be gathered via the IoT and stored in the Interplanetary File System (IPFS) using Ethereum-based blockchain technology for data security. The suggested method creates a reliable environment for managing healthcare data by exploiting the special features of blockchain. The json and jpeg files are utilized five times on a distributed database housed on IPFS and a centralized database hosted on Firebase, and the upload and download times are recorded. For IoT-based healthcare systems, we have also investigated the cost and length of time required to implement smart contracts on blockchain platforms like Rinkeby, Binance, and Matic. This research suggests implementing the Blockchain platform in an IoT-based healthcare system to provide data confidentiality, integrity, and accessibility.
Survey and Review on Various Topology and Geographical based Routing Protocol Parameters to Ensure the QOS Parameters of VANET
Authors: Ahamed V. M. Niaz , K. Sivaraman 
Published: October 2023

Vehicular Ad Hoc Network (VANET) is a type of wireless network that allows communication between vehicles and infrastructure. One of the critical considerations in VANET is Quality of Service (QoS) parameters, which determine the network's performance. The effective management of QoS parameters is essential for VANET's reliable and efficient operation. In this research paper, we aim to explore topology-based and geographical-based routing protocol parameters to ensure QoS parameters in VANET. The former uses the network topology to make routing decisions, while the latter uses the location information of vehicles. We will first provide an overview of VANET and QoS parameters. Then, we will delve into the key parameters of topology-based and geographical-based routing protocols and how they affect QoS. We will also survey and review the existing routing protocols and parameter values used in these protocols. The findings of this research paper will provide insights into the effective management of QoS parameters in VANET and contribute to the development of more efficient routing protocols.
Machine Learning Techniques to Evaluate the Approximation of Utilization Power in Circuits
Authors: M. P. Varghese , Muthumanickam T. 
Published: October 2023

The need for products that are more streamlined, more useful, and have longer battery lives is rising in today's culture. More components are being integrated onto smaller, more complex chips in order to do this. The outcome is higher total power consumption as a result of increased power dissipation brought on by dynamic and static currents in integrated circuits (ICs). For effective power planning and the precise application of power pads and strips by floor plan engineers, estimating power dissipation at an early stage is essential. With more information about the design attributes, power estimation accuracy increases. For a variety of applications, including function approximation, regularization, noisy interpolation, classification, and density estimation, they offer a coherent framework. RBFNN training is also quicker than training multi-layer perceptron networks. RBFNN learning typically comprises of a linear supervised phase for computing weights, followed by an unsupervised phase for determining the centers and widths of the Gaussian basis functions. This study investigates several learning techniques for estimating the synaptic weights, widths, and centers of RBFNNs. In this study, RBF networks—a traditional family of supervised learning algorithms—are examined. Using centers found using k-means clustering and the square norm of the network coefficients, respectively, two popular regularization techniques are examined. It is demonstrated that each of these RBF techniques are capable of being rewritten as data-dependent kernels. Due to their adaptability and quicker training time when compared to multi-layer perceptron networks, RBFNNs present a compelling option to conventional neural network models. Along with experimental data, the research offers a theoretical analysis of these techniques, indicating competitive performance and a few advantages over traditional kernel techniques in terms of adaptability (ability to take into account unlabeled data) and computing complexity. The research also discusses current achievements in using soft k-means features for image identification and other tasks.
PCO-IB: Churn Analysis P2P Networks Using A Peer Co-Operative Intensive Based Schema
Authors: Yaga Reddemma , Rao Ch. D. V. Subba 
Published: October 2023

The Peer-to-Peer networks used technology of distributed computing. The P2P network is essential for network communication. P2P networks are utilized in many applications due to these benefits. For example, record sharing, broadcast communications, and media streaming. There are a lot of nodes connected to the P2P network. Peers of network frequently join and leave the network at the same time. In the P2P network, this kind of paradigm is called churn. Numerous new examination works uncovered that stir is the primary issue looked by the present P2P organization. Content availability, data accuracy, and overhead were significantly reduced by the churn process. An Incentive-Based (IB) schema was proposed in this paper in order to circumvent the limitations of the P2P network for multimedia transmission. The IB schema that has been proposed encourages fair communication and cooperation among the nodes. Multimedia transmission efficiency in real-time P2P networks is maximized by the IB schema. In this paper, IB outline for the most part centered around the upgrade of the P2P organizations. The proposed construction is carried out utilizing Organization Test system. In P2P networks, the proposed IB schema improved multimedia transmission performance.
Revelation of Significant Fake Rhetorical in Wrapping Bygone Utilizing Significant Learning Procedures
Authors: Pughazendi N. , Suganya T. S. , S. Soorya , Chitra D. 
Published: October 2023

The developing computation control has made the profound learning calculations so powerful that making an unclear human synthesized video famously called a profound fake has got to be exceptionally straightforward. Scenarios where these practical confront swapped profound fakes are utilized to form political trouble, fake psychological warfare occasions, vindicate porn, and shakedown people groups are effortlessly imagined. In this work, we depict a modern profound learning-based strategy that can viably recognize AI-generated fake recordings from genuine videos. Our strategy can naturally be recognizing the substitution and reenactment of deep fakes. We are attempting to utilize Manufactured Intelligence (AI) to battle Fake Intelligence(AI). Our framework uses a res-next neural convolution system to extract frame-level highlights and promote the use of these highlights to prepare the long-term memory (LSTM)-based repetitive neural network (RNN) to classify whether the video is subject to art. control or not , i.e whether the video is profoundly fake or genuine. To imitate the genuine time scenarios and make the show perform way better on genuine time information, we assess our strategy on an expansive sum of adjusted and blended data-set arranged by blending the different accessible data-set like Face-Forensic, Deep Fake location challenge, and Celeb-DF. We moreover focus on how our framework can accomplish competitive results utilizing exceptionally straightforward and strong approaches.
Efficient Disease Identification Method for Crop Leaf using Deep Learning Techniques
Authors: Kumar Punith , Champa H. N. 
Published: October 2023

Many prime grain-producing nations have implemented steps to limit export of grains as COVID-19 has expanded over the globe; food security has sparked significant worry from a number of stakeholders. One of the most crucial concerns facing all nations is how to increase grain output. However, the diseases occur in crops remain a challenge for countless farmers, therefore it is critical to understand their severity promptly and precisely to guide the them in taking additional measures to lessen the chances of plants being affected furthermore.
This paper describes a deep learning model for the identification of crop diseases that can achieve high accuracy with low processing power. The model, called the inception v3 network, has been tested on a tomato leaf dataset and has obtained a average identification accuracy of 98.00% and further the ensemble of two inception v3 models with slight diversity achieved an accuracy of 98.11%. The results suggest that this model could be useful in improving food security by helping farmers quickly and accurately identify crop diseases and take appropriate measures to prevent further spread.
Enhancing Rice Plant Disease Recognition and Classification Using Modified Sand Cat Swarm Optimization with Deep Learning
Authors: Anandhi D. Felicia Rose , Sathiamoorthy S. 
Published: October 2023

Rice plant diseases play a critical challenge to agricultural productivity and food safety. Timely and accurate recognition and classification of these ailments are vital for efficient management of the disease. Classifying and recognizing rice plant disease by implementing Deep Learning (DL) has emerged as a powerful approach to tackle the challenges associated with automated disease diagnosis in rice crops. DL, a subfield of artificial intelligence, concentrates to train neural networks with several layers for automated learning of the complex patterns and illustrations from data. In the context of rice plant diseases, DL methods can effectually extract meaningful features from images and accurately classify them into different disease categories. Therefore, this study introduces a new Modified Sand Cat Swarm Optimization with Deep Learning based Rice Plant Disease Detection and Classification (MSCSO-DLRPDC) technique. The main objective of the MSCSO-DLRPDC technique focalize on the automated classification and recognition of rice plant ailments. To achieve this, the MSCSO-DLRPDC methodology involves two levels of pre-processing such as median filter-based noise removal and CLAHE-based contrast enhancement. Besides, Multi-Layer ShuffleNet with Depthwise Separable Convolution (MLS-DSC) methodology is utilized for feature extraction purposes. Moreover, the Multi-Head Attention-based Long Short-Term Memory (MHA-LSTM) methodology is utilized for the process of rice plant disease detection. At last, the MSCSO method is utilized for the tuning process of the MHA-LSTM approach. The MSCSO approach inspired by the collective behaviour of sand cats and the mutation operator, is implemented for optimizing the parameters of the MHA-LSTM network. To demonstrate the enhanced accomplishment of the MSCSO-DLRPDC method, a broad set of simulations were carried out. The extensive outputs show the greater accomplishment of the MSCSO-DLRPDC method over other methods. The proposed approach has the capability in assisting farmers and agricultural stakeholders in effectively managing rice plant diseases, contributing to improved crop yield and sustainable agricultural practices.
An Ensemble Learning Approach for Fast Disaster Response using Social Media Analytics
Authors: Ayyadurai M. , Sujatha K. , Guru R. Pavithra , Sasirekha D. , Umamageswari A. , Deepa S. 
Published: October 2023

Natural disaster happens, as a result of natural hazards that cause financial, environmental or human losses. Natural disasters strike unexpectedly, affecting the lives of tens of thousands of people. During the flood, social media sites were also heavily used to disseminate information about flooded areas, rescue agencies, food and relief centres. This work proposes an ensemble learning strategy for combining and analysing social media data in order to close the gap and progress in catastrophic situation. To enable scalability and broad accessibility of the dynamic streaming of multimodal data namely text, image, audio and video, this work is designed around social media data. A fusion technique was employed at the decision level, based on a database of 15 characteristics for more than 300 disasters around the world (Trained with MNIST dataset 60000 training images and 10000 testing images). This work allows the collected multimodal social media data to share a common semantic space, making individual variable prediction easier. Each merged numerical vector(tensors) of text and audio is sent into the K-CNN algorithm, which is an unsupervised learning algorithm (K-CNN), and the image and video data is given to a deep learning based Progressive Neural Artificial Search (PNAS). The trained data acts as a predictor for future incidents, allowing for the estimation of total deaths, total individuals impacted, and total damage, as well as specific suggestions for food, shelter and housing inspections. To make such a prediction, the trained model is presented a satellite image from before the accident as well as the geographic and demographic conditions, which is expected to result in a prediction accuracy of more than 85%.
Supply Chain Tracing and Anti-Counterfeiting with Distributed Ledger Technology
Authors: Patil Swati Bula , Rathi Snehal Rahul , Shelke Ganesh C. , Wankhede Shalini Vaibhav , Kodmelwar Manohar K. , Pathak Kishor Renukadas , Birare Komal M. 
Published: October 2023

In recent times, there has been a rampant proliferation of counterfeit products that has left a trail of devastation in the manufacturing sectors. The repercussions of this extend to companies, impacting their brand reputation, revenue streams and overall profitability. Industries like agriculture, banking, electronics, and high-value deliveries uses the emergence of blockchain technology as a powerful tool to discern between authentic and counterfeit items. Its potential as a means to curtail the influx of fake products in the market is substantial. Blockchain technology, at its core, operates as a decentralized and distributed digital ledger system, meticulously recording transactions within interconnected blocks across multiple databases. The inherent security of this technology ensures the immutability of these blocks, rendering them invulnerable to alteration or hacking. By leveraging blockchain technology, consumers can independently verify the authenticity of a product, eliminating the need for reliance on third-party intermediaries. Incorporating recent technological advancements, the utilization of Quick Response (QR) codes offers a robust approach to combat the proliferation of counterfeit goods. The integration of blockchain technology with QR codes serves as a means to uphold the integrity of products. This innovative system securely stores product details and unique codes in the form of blocks, where QR codes play a pivotal role in collecting and matching these unique codes with entries in the blockchain database. If the QR code matches with entries in the database, the user receives a confirmation of the product's authenticity; otherwise, an alert is triggered, signaling the presence of a counterfeit product.
Intelligent Destination Recommender and Community Builder
Authors: Jeswani Ashish , Shinde Radhika , Panaskar Roma , Kanade Varad , Kolapkar Vrushali 
Published: October 2023

Recommendation engines make use of machine learning techniques and generally deal with ranking and rating of products/users. With the help of this system we aim to suggest different destinations to users based on their interest and previous visits. Along with recommendations we also aspire to enable users to build travel communities for people sharing similar interests .This shall help travelers with planning ,meeting like-minded people,safety and enthralling experience.
As per the analysis done on pre-existing systems we discerned that enabling users to build a community of travelers visiting the same destination is an eccentric attribute proposed.
This distinctive attribute of building communities shall be implemented using the basics of clustering algorithms in Machine learning.