Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

This study employs evolutionary optimization and Artificial Intelligence algorithms to determine an individual’s age using a single-faced image as the basis for the identification process. Additionally, we used the WIKI dataset, widely considered the most comprehensive collection of facial images to date, including descriptions of age and gender attributes. However, estimating age from facial images is a recent topic of study, even though much research has been undertaken on establishing chronological age from facial photographs. Retrained artificial neural networks are used for classification after applying reprocessing and optimization techniques to achieve this goal. It is possible that the difficulty of determining age could be reduce

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

Background: Fast dissolving oral drug delivery system is solid dosage form which disintegrates or dissolves within second when placed in the mouth without need of water or chewing. In present investigation, an attempt has been made to develop oral fast dissolving film of calcium channel blocker lacidipine. Method: Five formulas were prepared by solvent casting method using HPMC (METOLOSE)® as a film forming polymer and evaluated for their physical characteristics such as thickness, weight variation, folding endurance, drug content, disintegration time and in vitro drug release. The compatibility of the drug in the formulation was confirmed by FTIR and DSC studies. Result and Conclusion: The optimized formula F1 showed minimum in vitr

In this work preparation of antireflection coating with single layer of MgO using pulsed laser deposition (PLD) method which deposit on glass substrate with different thicknesses (90 and 100) nm annealed at temperature 500 K was done.
The optical and structural properties (X-ray diffraction) have been determined. The optical reflectance was computed with the aid of MATLAB over the visible and near infrared region. Results shows that the best result obtained for optical performance of AR'Cs at 700 shots with thickness 90 nm nanostructure single layer AR'Cs and low reflection at wavelength 550 nm.

Phenytoin selective electrodes were constructed based on penytoin-phosphotungstate (Ph-PT) complex with different plasticizers; di-butyl phosphate (DBP), tri-butyl phosphate (TBP), di-butyl phthalate (DBPH),and o-nitro phenyl octyl ether (NPOE) phthalate. The electrodes based on DBPH, ONPOE plasticizers gave Narnistain slope which are, 56.4 and 55.3mV/decade with detection limit of 1.9x10-5 M , 1.8x10-5 and concentration range 10-1 to 10-4 M and pH range 3.0 – 8.0. The electrodes based on TBP and DBP showed non-Nernistain slopes, 40.2,40.5 mV/decade for both plasticizers. Interfering of some cations was investigated and shows no interfering with electrodes response. Potentiometric methods were used for measuring phenytion in