Chromosomes are thread‐like structures located in the cell nucleus that contains the human body blueprint. Chromosome analysis is also known as karyotyping is the test taken to detect the abnormalities identified in the human chromosome. The two types of widely known chromosome abnormalities are structural and numerical abnormalities. Manual karyotyping is complex, time‐consuming, and error‐prone. To overcome these complexities, automated chromosome karyotype architecture is proposed using the deep convolutional neural network (DCNN) architecture. Training the DCNN architecture from scratch needs a huge dataset and to overcome this problem a generative adversarial networks is used to create adversarial samples that resemble the images in the actual dataset. The time‐consuming hyperparameter tuning in the DCNN architecture is overcome using the hybrid moth‐flame optimization integrated with the hill‐climbing strategy (HMFOHC). The HMFOHC algorithm is mainly utilized in this article to minimize the huge number of parameters associated with the DCNN architecture. The efficiency of the proposed methodology is evaluated using two datasets namely the BioImLab chromosome dataset and hospital dataset. The proposed HMFOHC optimized DCNN architecture is mainly utilized for multiclass classification where it differentiates five numerical chromosome abnormalities, namely Trisomy 13, Trisomy 18, Trisomy 21, Trisomy XXY syndrome, and Monosomy X. The proposed model offers an accuracy, F1‐score, and kappa coefficient value of 98.65%, 98.86%, and 0.9894, respectively. The results obtained show that the proposed model achieves higher classification accuracy when compared with the different state‐of‐art techniques such as deep learning, random forest, and CNN. The inference time of the proposed methodology is 12.5 s which is relatively lower than the state‐of‐art techniques. The proposed approach can help cytogenetics forensic experts make better decisions and save time by automating manual karyotyping. Research Highlights Manual karyotyping is complex, time‐consuming, and error‐prone. To overcome these complexities, automated chromosome karyotype architecture is proposed using the DCNN architecture The proposed method is mainly applied for multiclass classification where the different types of numerical abnormalities are addressed such as Trisomy 13, Trisomy 18, Trisomy 21, Trisomy XXY syndrome, and Monosomy X.

Different intellectual property (IP) cores, including processor and memory, are interconnected to build a typical system-on-chip (SoC) architecture. Larger SoC designs dictate the data communication to happen over the global interconnects. Network-onChip(NoC) architectures have been proposed as a scalable solution to the global communication challenges in nanoscale systemson-chip (SoC) design. We proposed an idea on building customizing synthesis network-on-chip with the better flow partitioning and also considered power and area reduction as compared to the already presented regular topologies. Hence to improve the performance of SoC, first, we did a performance study of regular interconnect topologies MESH, TORUS, BFT and EBFT, we observed that the overall latency and throughput of the EBFT is better compared to other topologies, The next best in case of latency and throughput is BFT. Experimental results on a variety of NoC benchmarks showed that our synthesis results were achieved reduction in power consumption and average hop count over custom topology implementation.

Keylogger, a tool intended to record every keystroke made on the machine and offers the attacker the ability to steal large amounts of sensitive information without the permission ofthe owner ofthe message. The primary objective of this project is to detect keylogger applications and prevent data loss and sensitive information leakage. This project aims to identifY the set of permissions and storage levels owned by each of the applications and hence differentiate applications with proper permissions and keylogger applications that can abuse permissions. The keyloggers are detected using Black-box technique. Black-box approach is based on behavioral characteristics which can be applied to all keyloggers and it does not rely on the structural characteristics of the keylogger. This project aims to develop detection system on mobile phones based on machine learning algorithm to detect keylogger applications.

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At the pixel level, several chaos-based image encryption and decryption models have been developed throughout the years. However, due to long-haul, privacy, and security considerations, it has a number of drawbacks, including increased network complexity and low scalability, and actual text cracking avoidance is more challenging. In this study, we have proposed a novel approach for image encryption and decryption. The confusion and diffusion process is performed using fractional-order chaotic maps with the hybrid radiation heat transfer algorithm based sunflower optimization (HRHT-SO) algorithm. The proposed method works for both color and grayscale images. The HRHT-SO algorithm is the combination of both heat transfer search (HTS) and sunflower optimization (SO) algorithms. During the search process, the SO algorithm improves the radiation stage of the HTS model. Further, the DNA sequence improves the performance of image encryption and decryption using fractional-order chaotic maps with the HRHT-SO algorithm. The experimental investigations are carried out by using various parameters such as key sensitivity analysis, keyspace analysis, histogram analysis, correlation coefficient analysis, entropy analysis, PSNR, NPCR, and UACI analysis thereby analyzing the efficiency and complexity of the encryption scheme.Hence, the proposed method outperforms superior performances in all the experiments. K E Y W O R D Sconfusion and diffusion process, DNA sequence, hybrid radiation heat transfer algorithm based sunflower optimization algorithm, image encryption and decryption INTRODUCTIONThe rapid expansion of computer networks frequently leads to cases of information leakage as a result of infinite storage and network transmission activities. In multimedia communication, the major concern is secured digital image transmission and storage. 1 The communication network transports multimedia information and must be protected against unauthorized access. The diffusion and confusion techniques ensure data protection in cryptography. Image security is provided by using image encryption, which is one of the most important cryptography schemes. 2When utilizing a cryptosystem to encrypt an image, numerous image attributes are taken into account, including strong adjacent pixel correlation, increased redundancy, and larger size. For these reasons, conventional asymmetric and symmetric methods for image encryption (IE) are ineffective.