Author: Dr.Ammar Falih
Adaptive Steganography Method Based on Localized ACO for Pixel-Level Image Enhancement
With the rapid proliferation of digital communication in critical domains such as healthcare, defense, and finance, ensuring secure and private data transmission has become paramount. Image steganography offers a promising solution by concealing sensitive information within digital images. Among conventional techniques, the Least Significant Bit (LSB) method is widely adopted due to its simplicity and high embedding capacity; however, its deterministic nature renders it vulnerable to visual and statistical steganalysis. This study introduces a novel enhancement to the classical LSB approach by integrating a localized Ant Colony Optimization (ACO) strategy within a block-wise embedding framework. The proposed method segments the host image into non-overlapping 5×5 blocks, where ACO is employed to select optimal embedding pixels. A hybrid fitness function, incorporating Mean Squared Error (MSE), local variance, and pixel energy, guides the selection process to ensure high imperceptibility and robustness. The framework was implemented in Python and validated using standard grayscale and RGB images. We found in experimental results that the proposed method achieves a Peak Signal-to-Noise Ratio (PSNR) of up to 50 dB at a 0.1 bpp payload with maintains a Structural Similarity Index (SSIM) of 0.99, displaying very little visual distortion. The proposed technique that we present in this paper achieves a detection rate of less than 4.81% when evaluated using the StegExpose tool also the developed method achieved a high ROC-AUC score of 0.91, indicating strong resistance to steganography. Furthermore, the suggested ACO-LSB technique can be utilized as a more secure and effective method for hiding secret information in different images which makes it suitable for real-world applications such as hiding metadata in medical images, securing biometric data, and digital watermarking. https://njemcs.edu.iq/index.php/njemcs/article/view/9
AI-Driven Drones for Real-Time Network Performance Monitoring
The growing complexity of telecommunications networks, fueled by advancements like the Internet of Things (IoT) and 5G, necessitates dynamic and real-time network performance monitoring. Traditional static systems often fail to address challenges related to scalability, adaptability, and response speed in high-demand environments. Integrating artificial intelligence (AI) with unmanned aerial vehicles (UAVs) presents a transformative approach to overcoming these limitations. https://doaj.org/article/ab6396416ef94127a0af93f4f060c1b5
Artificial Intelligence in Network Security with Autonomous Threat Response Systems
With the continued advance in cyber threats, traditional network security systems offer little returns to organizations. AI has turned out to be a useful technology in improving network security because it proactively identifies and responds to threats in a short time. https://doaj.org/article/1f22c1ab09d44b52a68964d502037b0c
The Role of 5G and Blockchain in Binance’s Future Development.
The advent of 5G and block chain technologies has signified rapid growth in the digital space. Their unique capabilities have already redefined communication and decentralized networks; however, their possible combined role is the subject of this research article. https://ieeexplore.ieee.org/document/10749868
Deep Learning Approaches for Predicting Climate Change Impacts: An Empirical Analysis
Climate change stands as one of the most critical global challenges with enormous implications. Since its science is well understood, efforts now focus on modeling and predicting its effects to mitigate or adapt to these. Deep learning, with its remarkable aptitude for data representation and analysis, is a promising candidate to enhance weather attack predictions on a global scale. https://ieeexplore.ieee.org/document/10749950
Crahid: A New Technique for Web Crawling In Multimedia Web Sites
ABSTRACT With the quick growth and the huge amount of data that propagate in the web, we spend a lot of our time in finding the exact required information from the huge information retrieved from the web crawlers of search engines. Therefore, a special software is required to collect and find the exact required information and save our time and effort in finding what is good from the huge amount of retrieved data from the web. In our research, we proposed a software called “CRAHID” which uses a new technique to crawl (image, sound, text and video) depending on an information hiding technique for describing media. We supposed a new media format which describes media using hidden information to maintain time and effort for finding the exact information retrieved from the crawler. KEYWORDS: Computer, Crawling, Information hiding, Multimedia, Search , Technique, Web. http://www.ijceronline.com/v4-i2.html
Major depressive disorder diagnosis based on PSD imaging of electroencephalogram EEG and AI
Abstract One of the most common causes of functional frailty is major depressive disorder (MDD). MDD is a chronic condition that requires long-term therapy and professional assistance. Additionally, MDD effective treatment requires early detection. Unfortunately, it has intricated clinical characteristics that make early diagnosis and treatment difficult for clinicians. Furthermore, there are currently no clinically effective diagnostic biomarkers that can confirm an MDD diagnosis. However, electroencephalogram (EEG) data from the brain have recently been used to make a quantitative diagnosis of MDD. In addition, As being among the most cutting-edge artificial intelligence (AI) technologies, deep learning (DL) has exhibited superior performance in a wide range of real-world applications, from computer vision to healthcare. However, an additional challenge could be the extraction of information from the ECG raw data. This paper presents a method for converting EEG data to power spectral density (PSD) images, and then they were classified as healthy or MDD using a deep neural network for feature extraction and a machine learning (ML) classifier. When employing the proposed approach, the images formed from the PSD show a considerably improved performance in classification results. Keywords Artificial intelligence; Electroencephalogram; Machine learning; Major depressive disorder; Power spectral density https://ijeecs.iaescore.com/index.php/IJEECS/article/view/29130
Advanced Smart Algorithm for Integrating RFID and IoT Security
Abstract This research is an exploration into developing a system for enabling Radio Frequency Identification (RFID) labels to be connected to the Internet while taking into account their unique impediments. Additionally, this mechanism enables the tag to be extraordinarily distinct and spoken to as a communication material capable of communicating with other participants, which can facilitate and rearrange the use of the “Internet of Things” concept in the not-too-distant future. To build a mechanism capable of connecting RFID labels to the Internet. The methods taken by various researchers are investigated and dissected, enabling a better understanding of the difficulties and shortcomings associated with RFID labels connected to the Internet. The analysis and examination have resulted in the creation of another system that allows use of TCP/IP. The structure established in this paper is predicated on the capability of RFID labels to be used as procedures (TCP forms) within a host. As a result, each procedure has a procedure ID or port number, which enables various members to identify and communicate with the tag through the process ID. This is accomplished through a built-in interpretation portion that converts the RFID tag’s authentic personality (ID) to a new ID that can be recognized as a TCP port number. The results of this paper show that the system worked effectively for the purpose for which it was designed. The results show that the actualized system enables RFID labels to be linked to the Internet and to be exceptionally distinct. Additionally, it enables labels to send and receive information and guidance outside of the RFID system, through the Internet, and from various members. The framework’s success would provide several experts with opportunities to actualize the concept of “Internet of Things.” Keywords 1 Smart, network, algorithm, RFID, TCP/IP, IoT. CEUR-WS.org/Vol-3149/paper4.pdf
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