Alkadhim Journal for Computer Science

The clarity of medical images is crucial nowadays, as it forms the basis for diagnosing the patient\'s situation, determining the phase of the disease, and prescribing appropriate treatment. The objective of the present study is to enhance the clarity of colored medical images by increasing the thickness to eliminate soft edges and areas that may not be visible when determining the edges. The process involves converting the image from RGB to HSV, displaying the resulting image in the HSV color space, setting the edges in this space, and then incorporating them back into the image. Finally, the resulting image is converted from the HSV color space back to RGB. This algorithm can be implemented using Matlab2020a to optimize images. The proposed method is versatile and can be applied to all types of medical images, including (MRI, Ultrasound, X-ray, etc.), whether colored or grayscale, and regardless of size or the specific part of the human body. The results show a significant increase in the resolution of the resulting images, indicating improved consistency.

In recent years, secure communication between the interconnected components of the internet of things has become an important and worrying issue due to some attacks on the IoT. The Internet of Things (IOT) is the integration of things with the world of the Internet, where this integration takes place by adding devices or programs to be smart and, as a result, they will be able to communicate with one another and participate in all elements of life quite efficiently. Accordingly, we\'ve developed an authentication protocol for the IoT ecosystem; it\'s primary function is to ensure the safety of data exchange between the many devices that make up the IoT. Our proposed protocol is based on the elliptic curve cipher (ECC) algorithm, which greatly aids in protecting IoT components from physical assault. Our informal protocol analysis demonstrates that our solution not only protects users\' privacy by concealing their devices\' identities but also thwarts impersonation, counterattacks, and tracking and suggestion attacks directed at IoT devices. Security characteristics of the proposed protocol are also explicitly examined with the help of the ( SCYTHER) program. In addition, the effectiveness of the suggested protocol is evaluated by determining both its excess costs and its communication costs. Therefore, it appears that the protocol is vastly superior than the many other equivalent protocols by assessing its performance and security.

ChatGPT is one of the conversational AI tools that changed and still change the human life in different aspects wherein the academic and the scientific aspects seem to be affected more seriously. This paper, therefore, is an attempt to delve into some minutes of this influence in these two sensitive aspects. And this can be achieved through pinpoint its aggressions that are mainly caused by its capacities and limitations as far as scientific communication and studies are concerned. On its surface face, this AL seems to enrich scientific texts with clarity and coherence, to have a role in generation of new ideas and research methods, along with the dangers it creates to the honesty of academic efforts. The study, therefore, seems grim and pessimistic in this respect contrary to the immense praise and acceptance it receives at the universal level. It tries to call for a stricter human censorship over academic and scientific works to draw them away, as much as possible, from the injuries of this AI application. And to support this caution, it shows some of ChatGPT features and restrictions that turn it dangerous in the academic fields. And to be objective and accurate, the study refers to some of its positive aspects like its ability to help in creating coherence and clarity to scientific articles, research methods, and use in fighting plagiarism. As thus, the study highlights an urgent need for a necessary methods, restrictions, and strategies that can ensure a safe use of ChatGPT's advantages and avoiding its weaknesses whenever it is used in scientific writing.

A subset of cutting-edge information technology is the Internet of things (IoT). IoT refers to a network of physical objects with sensors attached that are linked to the Internet via LAN and WAN networking techniques. It is now commonly used to sense the environment and gather data in a variety of settings, including smart cities, healthcare, intelligent transportation, smart homes, and other structures. The IoT network architecture, core technology, and significant applications were outlined in this overview. The sensing layer, transport layer, and application layer are separated in the IoT network architecture. The essential technologies are embedded systems, network connectivity, sensor, and radio frequency identification (RFID) technology. IoT implementation in logistics still faces challenges despite the potential advantages. The utilization of technology in the IoT context is a topic with many open studies, which are also examined in this paper.

Cloud computing is one of the most rapidly evolving technologies today; it provides numerous advantages that increase its affordability and dependability for use in the company. This paper goes over the concepts of cloud computing, such as its characteristics, deployment model, and service model, discusses the various benefits of cloud computing, and highlights the most pressing issues and security concerns in cloud storage. As a consequence, that leads to a review of distinctive cryptography algorithms that meet the security requirements (CIA: confidentiality, integrity, and availability) that are used to secure communications in cloud computing situations. It also displays many algorithms depending on the previous studies, such as Blowfish, RSA, DES, AES, MD5, Feistel, SP, HIGHT, LED, Cybpher, PRESENT, RC6, Diamond2, mCrypton, SLIM, Klein, PUFFIN-2, SEA, CLEFIA, LBlock, TWINE, 2, ANU, ANU-II, NLBSIT, Piccolo, BORON, RECTANGLE, LICI, QTL, LOGIC, TRIVIUM, Fruit-v2, Fruit-80, A4, the Enocoro family, and Grain family, to make a comparison among them using many measurements. It found that modern and lightweight algorithms are more suitable for use in this field. Furthermore, the purpose of this paper is to make some suggestions for improving the safety and security of cloud computing technologies.

Deep learning-based side-channel analysis is an efficient and suitable technique for profiling side-channel attacks. In order to obtain the better performance, it is highly necessary to analyze an in-depth training stage in which the optimization of relevant hyperparameters should be a vital process. During the training phase, hyperparameters that are connected to the architecture of the neural network are often selected; however, hyperparameters that impact the training process are to be effectively analyzed. This was represented by an optimized hyperparammeter that consists of considerable impact on attacking behaviour, which is the primary focus of our research. Our research has shown that even while the popular optimizers Adam and RMSprop are capable of delivering satisfactory outcomes, they are also tend to being overfit. Hence, it is necessary to use condensed training periods, simple profiling models, and explicit regularization in order to avoid this problem. On the other hand, the performance of optimizers of the SGD type is only satisfactory when momentum is used which results in slower convergence and less overfit. In conclusion, the research results provide a better use of Adagrad in the cases of longer training datasets or big profiling models.

Researchers are exploring the use of convolutional neural networks (CNNs) in side-channel attacks to understand the weaknesses in cryptographic implementation. CNNs can learn hierarchical characteristics automatically from electromagnetic radiation or power usage during cryptographic processes. Researchers train CNNs on side-channel data to extract meaningful representations and deduce secret keys. Deep learning algorithms are helpful in evaluating the security of embedded systems, and CNNs are a feasible paradigm for profiling side-channel analysis attacks. In this paper, it has been introduced a VGG (Visual Geometry Group)-Net architecture, which is a typical deep convolutional neural network design with numerous layers. It uses the ASCAD dataset to conduct experiments. They found that VGG-Net architecture Side Channel Attacks (SCA) provides better results than the previously optimized CNN model by significantly reducing the number of side-channel traces required for successful attacks on desynchronized datasets. The researchers also discovered that synchronous traces serve as the pre-training source for VGG-Net architecture, functioning successfully in terms of jittering with minimal fine-adjusting after training.