Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts

A Flexible Encryption Technique for the Internet of Things Environment

IoT promises a new era of connectivity that goes beyond laptops and smart connected devices to connected vehicles, smart homes, smart cities and connected healthcare. The huge volume of data that is collected from millions of IoT devices raises information security and privacy concerns for users. This paper presents a new scalable encryption technique, called Flexible encryption Technique (FlexenTech), to protect IoT data during storage and in transit. FlexenTech is suitable for resource constrained devices and networks. It offers a low encryption time, defends against common attacks such as replay attacks and defines a configurable mode, where any number of rounds or key sizes may be used. Experimental analysis of FlexenTech shows its robustness in terms of its multiple configurable confidentiality levels by allowing various configurations. This configurability provides several advantages for resource constrained devices, including reducing the encryption computation time by up to 9.7% when compared to its best rivals in the literature

A hybrid dependency-based approach for Urdu sentiment analysis

In the digital age, social media has emerged as a significant platform, generating a vast amount of raw data daily. This data reflects the opinions of individuals from diverse backgrounds, races, cultures, and age groups, spanning a wide range of topics. Businesses can leverage this data to extract valuable insights, improve their services, and effectively reach a broader audience based on users’ expressed opinions on social media platforms. To harness the potential of this extensive and unstructured data, a deep understanding of Natural Language Processing (NLP) is crucial. Existing approaches for sentiment analysis (SA) often rely on word co-occurrence frequencies, which prove inefficient in practical scenarios. Identifying this research gap, this paper presents a framework for concept-level sentiment analysis, aiming to enhance the accuracy of sentiment analysis (SA). A comprehensive Urdu language dataset was constructed by collecting data from YouTube, consisting of various talks and reviews on topics such as movies, politics, and commercial products. The dataset was further enriched by incorporating language rules and Deep Neural Networks (DNN) to optimize polarity detection. For sentiment analysis, the proposed framework employs predefined rules to trigger sentiment flow from words to concepts, leveraging the dependency relations among different words in a sentence based on Urdu language grammatical rules. In cases where predefined patterns are not triggered, the framework seamlessly switches to its sub-symbolic counterpart, passing the data to the DNN for sentence classification. Experimental results demonstrate that the proposed framework surpasses state-of-the-art approaches, including LSTM, CNN, SVM, LR, and MLP, achieving an improvement of 6–7% on Urdu dataset. In conclusion, this research paper introduces a novel framework for concept-level sentiment analysis of Urdu language data sourced from social media platforms. By combining language rules and DNN, the proposed framework demonstrates superior performance compared to existing methodologies, showcasing its effectiveness in accurately analyzing sentiment in Urdu text data

Improved flat mobile core network architecture for 5G mobile communication systems

The current mobile network core is built based on a centralized architecture, including the S-GW and P-GW entities to serve as mobility anchors. Nevertheless, this architecture causes non-optimal routing and latency for control messages. In contrast, the fifth generation (5G) network will redesign the network service architecture to improve changeover management and deliver clients a better Quality-of-Experience (QoE). To enhance the design of the existing network, a distributed 5G core architecture is introduced in this study. The control and data planes are distinct, and the core network also combines IP functionality anchored in a multi-session gateway design. We also suggest a control node that will fully implement the control plane and result in a flat network design. Its architecture, therefore, improves data delivery, mobility, and attachment speed. The performance of the proposed architecture is validated by improved NS3 simulation to run several simulations, including attachment and inter- and intra-handover. According to experimental data, the suggested network is superior in terms of initial attachment, network delay, and changeover management

Innovative Energy-Efficient Proxy Re-Encryption for Secure Data Exchange in Wireless Sensor Networks

In the realm of wireless sensor networks (WSNs), preserving data integrity, privacy, and security against cyberthreats is paramount. Proxy re-encryption (PRE) plays a pivotal role in ensuring secure intra-network communication. However, existing PRE solutions encounter persistent challenges, including processing delays due to the transfer of substantial data to the proxy for re-encryption and the computational intensity of asymmetric cryptography. This study introduces an innovative PRE scheme that is meticulously customized for WSNs to enhance the secure communication between nodes within the network and external data server. The proposed PRE scheme optimizes efficiency by integrating lightweight symmetric and asymmetric cryptographic techniques, thereby minimizing computational costs during PRE operations and conserving energy for resource-constrained nodes. In addition, the scheme incorporates sophisticated key management and digital certificates to ensure secure key generation and distribution, which in turn, facilitates seamless authentication and scalable data sharing among the entities in WSN. This scheme maintains sensor-node data encryption and delegates secure re-encryption tasks exclusively to cluster heads, thereby reinforcing data privacy and integrity. Comprehensive evaluations of security, performance, and energy consumption validated the robustness of the scheme. The results confirm that the proposed PRE scheme significantly enhances the security, efficiency, and overall network lifetime of WSNs

Fuzzy Clustering Algorithm Based on Improved Global Best-Guided Artificial Bee Colony with New Search Probability Model for Image Segmentation

Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been extensively investigated and successfully implemented in image segmentation. FCM is useful in various aspects, such as the segmentation of grayscale images. However, FCM has some limitations in terms of its selection of the initial cluster center. It can be easily trapped into local optima and is sensitive to noise, which is considered the most challenging issue in the FCM clustering algorithm. This paper proposes an approach to solve FCM problems in two phases. Firstly, to improve the balance between the exploration and exploitation of improved global best-guided artificial bee colony algorithm (IABC). This is achieved using a new search probability model called PIABC that improves the exploration process by choosing the best source of food which directly affects the exploitation process in IABC. Secondly, the fuzzy clustering algorithm based on PIABC, abbreviated as PIABC-FCM, uses the balancing of PIABC to avoid getting stuck into local optima while searching for the best solution having a set of cluster center locations of FCM. The proposed method was evaluated using grayscale images. The performance of the proposed approach shows promising outcomes when compared with other related works

Digital image watermarking using discrete cosine transformation based linear modulation

Abstract The proportion of multimedia traffic in data networks has grown substantially as a result of advancements in IT. As a result, it's become necessary to address the following challenges in protecting multimedia data: prevention of unauthorized disclosure of sensitive data, in addition to tracking down the leak's origin, making sure no alterations may be made without permission, and safeguarding intellectual property for digital assets. watermarking is a technique developed to combat this issue, which transfer secure data over the network. The main goal of invisible watermarking is a hidden exchange of data and a message from being discovered by a third party. The objective of this work is to develop a digital image watermarking using discrete cosine transformation based linear modulation. This paper proposed an invisible watermarking method for embedding information into the transformation domain for the grey scale images. This method used the embedding of a stego-text into the least significant bit (LSB) of the Discrete Cosine Transformation (DCT) coefficient by using a linear modulation algorithm. Also, a stego-text is embedded with different sizes ten times within images after embedding the stego-image immune to different kinds of attack, such as salt and pepper, rotation, cropping, and JPEG compression with different criteria. The proposed method is tested using four benchmark images. Also, to evaluate the embedding effect, PSNR, NC and BER are calculated. The outcomes show that the proposed approach is practical and robust, where the obtained results are promising and do not raise any suspicion. In addition, it has a large capacity, and its results are imperceptible, especially when 1bit/block is embedded