Advancements in internet accessibility and the affordability of digital picture sensors have led to the proliferation of extensive image databases utilized across a multitude of applications. Addressing the semantic gap between low- level attributes and human visual perception has become pivotal in refining Content Based Image Retrieval (CBIR) methodologies, especially within this context. As this field is intensely researched, numerous efficient algorithms for CBIR systems have surfaced, precipitating significant progress in the artificial intelligence field. In this study, we propose employing a hard voting ensemble approach on features derived from three robust deep learning architectures: Inception, Exception, and Mobilenet. This is aimed at bridging the divide between low-level image features and human visual perception. The Euclidean method is adopted to determine the similarity metric between the query image and the features database. The outcome was a noticeable improvement in image retrieval accuracy. We applied our approach to a practical dataset named CBIR 50, which encompasses categories such as mobile phones, cars, cameras, and cats. The effectiveness of our method was thereby validated. Our approach outshone existing CBIR algorithms with superior accuracy (ACC), precision (PREC), recall (REC), and F1-score (F1-S), proving to be a noteworthy addition to the field of CBIR. Our proposed methodology could be potentially extended to various other sectors, including medical imaging and surveillance systems, where image retrieval accuracy is of paramount importance.
Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications such as noise cancellation. Noise cancellation is a common occurrence in today telecommunication systems. The LMS algorithm which is one of the most efficient criteria for determining the values of the adaptive noise cancellation coefficients are very important in communication systems, but the LMS adaptive noise cancellation suffers response degrades and slow convergence rate under low Signal-to- Noise ratio (SNR) condition. This paper presents an adaptive noise canceller algorithm based fuzzy and neural network. The major advantage of the proposed system is its ease of implementation and fast convergence. The proposed algorithm is applied to noise canceling problem of long distance communication channel. The simulation results showed that the proposed model is effectiveness.
The brain tumors are among the common deadly illness that requires early, reliable detection techniques, current identification, and imaging methods that depend on the decisions of neuro-specialists and radiologists who can make possible human error. This takes time to manually identify a brain tumor. This work aims to design an intelligent model capable of diagnosing and predicting the severity of magnetic resonance imaging (MRI) brain tumors to make an accurate decision. The main contribution is achieved by adopting a new multiclass classifier approach based on a collected real database with new proposed features that reflect the precise situation of the disease. In this work, two artificial neural networks (ANNs) methods namely, Feed Forward Back Propagation neural network (FFBPNN) and support vector machine (SVM), used to expectations the level of brain tumors. The results show that the prediction result by the (FFBPN) network will be better than the other method in time record to reach an automatic classification with classification accuracy was 97% for 3-class which is considered excellent accuracy. The software simulation and results of this work have been implemented via MATLAB (R2012b).
Address Resolution Protocol (ARP) is used to resolve a host’s MAC address, given its IP address. ARP is stateless, as there is no authentication when exchanging a MAC address between the hosts. Hacking tactics using ARP spoofing are constantly being abused differently; many previous studies have prevented such attacks. However, prevention requires modification of the underlying network protocol or additional expensive equipment, so applying these methods to the existing network can be challenging. In this paper, we examine the limitations of previous research in preventing ARP spoofing. In addition, we propose a defence mechanism that does not require network protocol changes or expensive equipment. Before sending or receiving a packet to or from any device on the network, our method checks the MAC and IP addresses to ensure they are correct. It protects users from ARP spoofing. The findings demonstrate that the proposed method is secure, efficient, and very efficient against various threat scenarios. It also makes authentication safe and easy and ensures data and users’ privacy, integrity, and anonymity through strong encryption techniques.
Cross-Site Scripting (XSS) is one of the most common and dangerous attacks. The user is the target of an XSS attack, but the attacker gains access to the user by exploiting an XSS vulnerability in a web application as Bridge. There are three types of XSS attacks: Reflected, Stored, and Dom-based. This paper focuses on the Stored-XSS attack, which is the most dangerous of the three. In Stored-XSS, the attacker injects a malicious script into the web application and saves it in the website repository. The proposed method in this paper has been suggested to detect and prevent the Stored-XSS. The prevent Stored-XSS Server (PSS) was proposed as a server to test and sanitize the input to web applications before saving it in the database. Any user input must be checked to see if it contains a malicious script, and if so, the input must be sanitized and saved in the database instead of the harmful input. The PSS is tested using a vulnerable open-source web application and succeeds in detection by determining the harmful script within the input and prevent the attack by sterilized the input with an average time of 0.3 seconds.