Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is increased.In this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift velocity.The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)algorithm.Unlike the previous work,the probability of bit error is expressed in a closed-form expression.It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver.The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision loss.Traditional methods of diagnosing and treating the disease are time-consuming and expensive.However,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic retinopathy.These advanced techniques offer higher accuracy and performance.ComputerAided Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease diagnoses.Overall,these technological advancements hold great potential for improving the management of diabetic retinopathy.The study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these classes.The robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its robustness.The outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance understanding.This study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of DR.The manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.

Opto-Video Encryption Based on Logistic Adjusted Sine map in FrFT

In the last few years,videos became the most common form of information transmitted over the internet,and a lot of the traffic is confidential and must be protected and delivered safely to its intended users.This introduces the challenges of presenting encryption systems that can encode videos securely and efficiently at the same time.This paper presents an efficient opto-video encryption system using Logistic Adjusted Sine map(LASM)in the Fractional Fourier Transform(FrFT).In the presented opto-video LASM-based FrFT scheme,the encoded video is split into distinct frames and transformed into optical signals utilizing an optical supply.Each of the developed optical video frames is ciphered by utilizing the LASM in optical FrFT system using two-phase modulation forms on the video frame,the first in the time-domain and the second in the FrFT domain.In the end,the ciphervideo frame is spotted utilizing a CCD digital camera and transformed into a digital structure that can be managed using a computer.We test the proposed opto-video LASM-based FrFT scheme using various security tools.The outcomes demonstrate that the presented scheme can effectively encrypt and decrypt video signals.In addition,it encrypts videos with a high level of encryption qualitywithout sacrificing its resistance to noise immunity.Finally,the test outcomes demonstrate that the presented scheme is immune to known attacks.