Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%

Handwritten Signature Verification using Deep Learning

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%

Diagnosis of Blood Cells Using Deep Learning

In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and algorithms that allow the machine to learn by simulating neurons in the human body. Most in-depth learning research focuses on finding high-level methods. The strippers analyze a large data set using linear and nonlinear transformations. The method of deep learning is used in the detection of several diseases including blood cell diseases and their classification using the radiography of blood cells to help decision makers to know the type of blood cell and its associated diseases and the results will be presented in detail and discussed. This thesis is using python language and deep learning to detect blood cell diseases and their classifications. The proposed deep learning model was trained, validated and the tested. The accuracy of proposed model was 98.00

AI-Driven Innovations in Agriculture: Transforming Farming Practices and Outcomes

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. Additionally, it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations

Optimization of admixture and three-layer particleboard made from oil palm empty fruit bunch and rubberwood clones

Empty fruit bunch (EFB) is a biomass that is widely available and has the potential to be used as industrial raw material especially in wood-based industries. This study focuses on producing a particleboard by incorporating EFB with two different rubberwood clones: Prang Besar (PB) 260 and RRIM 2002, respectively. PB 260 is a commercially planted clone and wood from matured (>25 year-old) trees are used by wood-based panel manufacturers. RRIM 2002 is a new clone planted at the Malaysian Rubber Board (MRB) research trial plots and consists of only 4-year-old trees. Two types of particleboards (admixture and three-layer) with different ratios were produced. The Japanese Industrial Standard (JIS-5908 2003 particleboard) was used to evaluate mechanical and dimensional stability properties of the particleboards. From the study, it was found that admixture particleboards showed superior properties compared to three-layer particleboards. Layering EFB and rubberwood significantly decreased board performance for all properties (except internal bonding). The optimum ratios of EFB and both rubberwood clones are found to be 1:1 (50% EFB: 50% rubberwood). Meanwhile, increasing the rubberwood clones ratio to 70% lowered board performance especially for EFB (30%):RRIM 2002 clone (70%) boards which showed the lowest values for all properties for both admixture and three-layer board

Effect of ethnomedicinal plants used in folklore medicine in Jordan as antibiotic resistant inhibitors on Escherichia coli

<p>Abstract</p> <p>Background</p> <p><it>Escherichia coli </it>occurs naturally in the human gut; however, certain strains that can cause infections, are becoming resistant to antibiotics. Multidrug-resistant <it>E. coli </it>that produce extended-spectrum β lactamases (ESBLs), such as the CTX-M enzymes, have emerged within the community setting as an important cause of urinary tract infections (UTIs) and bloodstream infections may be associated with these community-onsets. This is the first report testing the antibiotic resistance-modifying activity of nineteen Jordanian plants against multidrug-resistant <it>E. coli</it>.</p> <p>Methods</p> <p>The susceptibility of bacterial isolates to antibiotics was tested by determining their minimum inhibitory concentrations (MICs) using a broth microdilution method. Nineteen Jordanian plant extracts (<it>Capparis spinosa </it>L., <it>Artemisia herba-alba Asso, Echinops polyceras </it>Boiss., <it>Gundelia tournefortii </it>L, <it>Varthemia iphionoides </it>Boiss. & Blanche, <it>Eruca sativa Mill</it>., <it>Euphorbia macroclada </it>L., <it>Hypericum trequetrifolium </it>Turra, <it>Achillea santolina </it>L., <it>Mentha longifolia </it>Host, <it>Origanum syriacum </it>L., <it>Phlomis brachydo</it>(Boiss.) Zohary, <it>Teucrium polium </it>L., <it>Anagyris foetida </it>L., <it>Trigonella foenum-graecum </it>L., <it>Thea sinensis </it>L., <it>Hibiscus sabdariffa </it>L., <it>Lepidium sativum </it>L., <it>Pimpinella anisum </it>L.) were combined with antibiotics, from different classes, and the inhibitory effect of the combinations was estimated.</p> <p>Results</p> <p>Methanolic extracts of the plant materials enhanced the inhibitory effects of chloramphenicol, neomycin, doxycycline, cephalexin and nalidixic acid against both the standard strain and to a lesser extent the resistant strain of <it>E. coli</it>. Two edible plant extracts (<it>Gundelia tournefortii L</it>. and <it>Pimpinella anisum L</it>.) generally enhanced activity against resistant strain. Some of the plant extracts like <it>Origanum syriacum </it>L.(Labiateae), <it>Trigonella foenum- graecum </it>L.(Leguminosae), <it>Euphorbia macroclada </it>(Euphorbiaceae) and <it>Hibiscus sabdariffa </it>(Malvaceae) did not enhance the activity of amoxicillin against both standard and resistant <it>E. coli</it>. On the other hand combinations of amoxicillin with other plant extracts used showed variable effect between standard and resistant strains. Plant extracts like <it>Anagyris foetida </it>(Leguminosae) and <it>Lepidium sativum </it>(Umbelliferae) reduced the activity of amoxicillin against the standard strain but enhanced the activity against resistant strains. Three edible plants; Gundelia <it>tournefortii </it>L. (Compositae) <it>Eruca sativa </it>Mill. (Cruciferae), and <it>Origanum syriacum </it>L. (Labiateae), enhanced activity of clarithromycin against the resistant <it>E. coli </it>strain.</p> <p>Conclusion</p> <p>This study probably suggests possibility of concurrent use of these antibiotics and plant extracts in treating infections caused by <it>E. coli </it>or at least the concomitant administration may not impair the antimicrobial activity of these antibiotics.</p

CLIMB-COVID: continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance.

Funder: Wellcome TrustIn response to the ongoing SARS-CoV-2 pandemic in the UK, the COVID-19 Genomics UK (COG-UK) consortium was formed to rapidly sequence SARS-CoV-2 genomes as part of a national-scale genomic surveillance strategy. The network consists of universities, academic institutes, regional sequencing centres and the four UK Public Health Agencies. We describe the development and deployment of CLIMB-COVID, an encompassing digital infrastructure to address the challenge of collecting and integrating both genomic sequencing data and sample-associated metadata produced across the COG-UK network