An Enhanced Multi-Level Authentication Electronic Voting System

Originally, manual voting systems are surrounded with issues like results manipulation, errors and long result computation time, ineligible voters, void votes among others. Electronic voting system helped in overcoming the challenges with manual voting system, to engendered other problems of phishing, men in the middle attack alongside voter’s impersonation. By these challenges, the integrity of an election results in a distributed system has become another top concern for e-voting system based on reliability. To achieve an improved voters’ authentication and result validation with excellent user experience, here, a Facial Recognition Electronic Voting System that is power-driven by Blockchain Technology was developed. The entire election engineering activities are decentralised with improved security features to enhance transparency, verifiability, and accountability for each vote count. The self-service voting system was built by smart contract and implemented on the Ethereum network. The obtained reports and evaluations reflected a non-editable and self-sufficiently certifiable system for voting. It also has a competitive edge over fingerprint enabled e-voting system. Aside it’s excellent usability and general acceptance, the developed method discarded to a larger extend, intended fraudulent actions from election activities by eliminating the involvement of a middleman while facilitating privacy, convenience, eligibility and satisfactory voters’ righ

Dataset on biochemical inhibiting activities of selected phytochemicals in Azadirachta indica L as potential NS2B–NS3 proteases inhibitors

The anti-NS2B–NS3 proteases activities of Azadirachta indica L. were investigated via the data obtained from selected bioactive compounds from Azadirachta indica L. The work was investigated using insilico approach and the series of computational software were used to execute the task. The software used were Spartan 14, material studio, Padel, Pymol, Autodock tool, Autodock vina and discovery studio. The obtained descriptors from 2D and 3D of the optimized compounds were screened and they were used to develop QSAR model using material studio software. Also, biological interaction between the selected bioactive compounds from Azadirachta indica L. and NS2B–NS3 proteases (PDB ID: 2fom) were accomplished using docking method and the calculated binding affinity as well as the residues involved in the interaction were reported. More so, the ADMET features for [(5S,6R,7S,8R,9S,10R,11S,12R,13S,17R)-17-(2,5-dihydroxy-2,5-dihydrofuran-3-yl)-11,12-dihydroxy-6‑methoxy-4,4,8,10,13-pentamethyl-1,16-dioxo-6,7,9,11,12,17-hexahydro-5H-cyclopenta[a]phenanthren-7-yl] 3-methylbut-2-enoate (Compound 6) and (10R,13S,14S,17S)-17-[1-(3,4-dihydroxy-5,5-dimethyloxolan-2-yl)ethyl]-4,4,10,13,14-pentamethyl-1,2,5,6,9,11,12,15,16,17-decahydrocyclopenta[a]phenanthren-3-one (compound 12) with lowest binding affinity were investigated and reported

Enhancing poultry health management through machine learning-based analysis of vocalization signals dataset

Population expansion and rising consumer demand for nutrient-dense meals have both contributed to an increase in the consumption of animal protein worldwide. A significant portion of the meat and eggs used for human consumption come from the poultry industry. Early diagnosis and warning of infectious illnesses in poultry are crucial for enhancing animal welfare and minimizing losses in the breeding and production systems for poultry. On the other hand, insufficient techniques for early diagnosis as well as infectious disease control in poultry farms occasionally fail to stop declining productivity and even widespread death.Individual physiological, physical, and behavioral symptoms in poultry, such as fever-induced increases in body temperature, abnormal vocalization due to respiratory conditions, and abnormal behavior due to pathogenic infections, frequently represent the health status of the animal. When birds have respiratory problems, they make strange noises like coughing and snoring. The work is geared towards compiling a dataset of chickens that were both healthy and unhealthy.100 day-old poultry birds were purchased and split into two groups at the experimental site, the poultry research farm at Bowen University. For respiratory illnesses, the first group received treatment, whereas the second group did not. After that, the birds were separated and caged in a monitored environment. To eliminate extraneous sounds and background noise that might affect the analysis, microphones were set a reasonable distance away from the birds. The data was gathered using 24-bit samples at 96 kHz. For 65 days, three times per day (morning, afternoon, and night) of audio data were continually collected. Food and water are constantly provided to the birds during this time. During this time, the birds have constant access to food and water. After 30 days, the untreated group started to sound sick with respiratory issues. This information was also noted as being unhealthy. Chickens' audio signals were recorded, saved in MA4, and afterwards converted to WAV format.This dataset's creation is intended to aid in the design of smart technologies capable of early detection and monitoring of the status of birds in poultry farms in a continuous, noninvasive, and automated way

Poultry fecal imagery dataset for health status prediction: A case of South-West Nigeria

Feces is one quick way to determine the health status of the birds and farmers rely on years of experience as well as professionals to identify and diagnose poultry diseases. Most often, farmers lose their flocks as a result of delayed diagnosis or a lack of trustworthy experts. Prevalent diseases affecting poultry birds may be quickly noticed from image of poultry bird's droppings using artificial intelligence based on computer vision and image analysis. This paper provides description of a dataset of both healthy and unhealthy poultry fecal imagery captured from selected poultry farms in south-west of Nigeria using smartphone camera. The dataset was collected at different times of the day to account for variability in light intensity and can be applied in machine learning models development for abnormality detection in poultry farms. The dataset collected is 19,155 images; however, after preprocessing which encompasses cleaning, segmentation and removal of duplicates, the data strength is 14,618 labeled images. Each image is 100 by 100 pixels size in jpeg format. Additionally, computer vision applications like picture segmentation, object detection, and classification can be supported by the dataset. This dataset's creation is intended to aid in the creation of comprehensive tools that will aid farmers and agricultural extension agents in managing poultry farms in an effort to minimize loss and, as a result, optimize profit as well as the sustainability of protein sources