Social-sine cosine algorithm-based cross layer resource allocation in wireless network

Cross layer resource allocation in the wireless networks is approached traditionally either by communications networks or information theory. The major issue in networking is the allocation of limited resources from the users of network. In traditional layered network, the resource are allocated at medium access control (MAC) and the network layers uses the communication links in bit pipes for delivering the data at fixed rate with the occasional random errors. Hence, this paper presents the cross-layer resource allocation in wireless network based on the proposed social-sine cosine algorithm (SSCA). The proposed SSCA is designed by integrating social ski driver (SSD) and sine cosine algorithm (SCA). Also, for further refining the resource allocation scheme, the proposed SSCA uses the fitness based on energy and fairness in which max-min, hard-fairness, proportional fairness, mixed-bias and the maximum throughput is considered. Based on energy and fairness, the cross-layer optimization entity makes the decision on resource allocation to mitigate the sum rate of network. The performance of resource allocation based on proposed model is evaluated based on energy, throughput, and the fairness. The developed model achieves the maximal energy of 258213, maximal throughput of 3.703, and the maximal fairness of 0.868, respectively

Size Control and Magnetic Property Trends in Cobalt Ferrite Nanoparticles Synthesized Using an Aqueous Chemical Route

Cobalt ferrite (CoFe2O4) is an engineering material which is used for applications such as magnetic cores, magnetic switches, hyperthermia based tumor treatment, and as contrast agents for magnetic resonance imaging. Utility of ferrites nanoparticles hinges on its size, dispersibility in solutions, and synthetic control over its coercivity. In this work, we establish correlations between room temperature co-precipitation conditions, and these crucial materials parameters. Furthermore, post-synthesis annealing conditions are correlated with morphology, changes in crystal structure and magnetic properties. We disclose the synthesis and process conditions helpful in obtaining easily sinterable CoFe2O4 nanoparticles with coercive magnetic flux density (H-c) in the range 5.5-31.9 kA/m and M-s in the range 47.9-84.9 A.m(2)Kg(-1). At a grain size of similar to 54 +/- 2 nm (corresponding to 1073 K sintering temperature), multi-domain behavior sets in, which is indicated by a decrease in H-c. In addition, we observe an increase in lattice constant with respect to grain size, which is the inverse of what is expected of in ferrites. Our results suggest that oxygen deficiency plays a crucial role in explaining this inverse trend. We expect the method disclosed here to be a viable and scalable alternative to thermal decomposition based CoFe2O4 synthesis. The magnetic trends reported will aid in the optimization of functional CoFe2O4 nanoparticle

Structural and magnetic properties of nanocrystalline BaFe12O19 synthesized by microwave-hydrothermal method

Nanocrystalline BaFe12O19 powders were prepared by microwave-hydrothermal method at 200 °C/45 min. The as-synthesized powders were characterized by using X-ray diffraction (XRD), thermogravimetry (TG) and differential thermal analysis (DTA). The present powders were densified at different temperatures, i.e., 750, 850, 900 and 950 °C for 1 h using microwave sintering method. The phase formation and morphology studies were carried out using XRD and field emission scanning electron microscopy (FE-SEM). The average grain sizes of the sintered samples were found to be in the range of 185–490 nm. The magnetic properties such as saturation magnetization and coercive field of sintered samples were calculated based on magnetization curves. A possible relation between the magnetic hysteresis curves and the microstructure of the sintered samples was investigated

Detection of Bundle Branch Blocks using Machine Learning Techniques

The most effective method used for the diagnosis of heart diseases is the Electrocardiogram (ECG). The shape of the ECG signal and the time interval between its various components gives useful details about any underlying heart disease. Any dysfunction of the heart is called as cardiac arrhythmia. The electrical impulses of the heart are blocked due to the cardiac arrhythmia called Bundle Branch Block (BBB) which can be observed as an irregular ECG wave. The BBB beats can indicate serious heart disease. The precise and quick detection of cardiac arrhythmias from the ECG signal can save lives and can also reduce the diagnostics cost. This study presents a machine learning technique for the automatic detection of BBB. In this method both morphological and statistical features were calculated from the ECG signals available in the standard MIT BIH database to classify them as normal, Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB). ECG records in the MIT- BIH arrhythmia database containing Normal sinus rhythm, RBBB, and LBBB were used in the study. The suitability of the features extracted was evaluated using three classifiers, support vector machine, k-nearest neighbours and linear discriminant analysis. The accuracy of the technique is highly promising for all the three classifiers with k-nearest neighbours giving the highest accuracy of 98.2%. Since the ECG waveforms of patients with the same cardiac disorder is similar in shape, the proposed method is subject independent. The proposed technique is thus a reliable and simple method involving less computational complexity for the automatic detection of bundle branch block. This system can reduce the effort of cardiologists thereby enabling them to concentrate more on treatment of the patients

MET Signaling: Novel Targeted Inhibition and Its Clinical Development in Lung Cancer

MET is a versatile receptor tyrosine kinase within the human kinome which is activated by its specific natural ligand hepatocyte growth factor (HGF). MET signaling plays an important physiologic role in embryogenesis and early development, whereas its deregulation from an otherwise quiescent signaling state in mature adult tissues can lead to upregulated cell proliferation, survival, scattering, motility and migration, angiogenesis, invasion, and metastasis in tumorigenesis and tumor progression. Studies have shown that MET pathway is activated in many solid and hematological malignancies, including lung cancer, and can be altered through ligand or receptor overexpression, genomic amplification, MET mutations, and alternative splicing. The MET signaling pathway is known to be an important novel target for therapeutic intervention in human cancer. A number of novel therapeutic agents that target the MET/HGF pathway have been tested in early-phase clinical studies with promising results. Phase 3 studies of MET targeting agents have just been initiated. We will review the MET signaling pathway and biology in lung cancer and the recent clinical development and advances of MET/HGF targeting agents with emphasis on discussion of issues and strategies needed to optimize the personalized therapy and further clinical development

A High Secured Steganalysis using QVDHC Model

Data compression plays a vital role in data security as it saves memory, transfer speed is high, easy to handle and secure. Mainly the compression techniques are categorized into two types. They are lossless, lossy data compression. The data format will be an audio, image, text or video. The main objective is to save memory of using these techniques is to save memory and to preserve data confidentiality, integrity. In this paper, a hybrid approach was proposed which combines Quotient Value Difference (QVD) with Huffman coding. These two methods are more efficient, simple to implement and provides better security to the data. The secret message is encoded using Huffman coding, while the cover image is compressed using QVD. Then the encoded data is embedded into cover image and transferred over the network to receiver. At the receiver end, the data is decompressed to obtain original message. The proposed method shows high level performance when compared to other existing methods with better quality and minimum error

Classification of Diabetic Retinopathy using Convolutional Neural Network

Diabetic Retinopathy is a scenario in medical field which leads to the rise of damage of blood vessels in the retina which is due to diabetes mellitus. The suitable detection for this kind of problems and care to be done immediately in order to prohibit loss of sight in a person. Presently, diagnosing Diabetic Retinopathy manually is a time- consuming process where they require experienced clinicians to examine the digital-colored fundus images. Here, we have proposed a machine learning technology using Convolutional Neural Network (CNN) approach which has emerged as an operative productive tool in medical image examination for the classification and detection of Diabetic Retinopathy (DR) in real-world. The different layers which are used to detect the brain tumor are conv2D, Activation, MaxPooling2D, Dense and Flatten. The set used here considers 750 retinal images, with 600 training images and the test set considers 150 images with the accuracy of 82.75% which ran for 80 epochs

Fruit Grade Classification and Disease Detection using Deep Learning Techniques

Ensuring optimal food quality and agricultural productivity hinges on effective fruit quality assessment and disease detection. Introducing a comprehensive strategy employing deep learning techniques to address critical aspects of fruit quality assessment and disease detection in agriculture. The methodology is structured into two distinct phases, each designed to optimize the accuracy and efficiency of the overall system. In the initial phase, image acquisition, preprocessing, and precise Region of Interest (ROI) detection using the Expectation-Maximization (EM) method lay the foundation for fruit classification with the AlexNet architecture. Rigorous training and testing procedures ensure the model's efficacy. The subsequent phase extends the initial process, with a heightened focus on feature extraction facilitated by DenseNet201. Thorough performance analysis, incorporating multiple metrics, assesses the accuracy and effectiveness of the system. This framework aspires to establish a robust solution for automated fruit grading and disease detection. By harnessing the capabilities of deep learning models, the goal is to accurately classify fruits and identify potential diseases, contributing significantly to agricultural practices and food quality management. The anticipated outcomes aim to set the groundwork for future advancements in the agricultural sector, providing a technological solution that enhances efficiency in fruit quality assessment and disease detection, ultimately benefiting food quality and crop yield