Low Profile MIMO Diversity Antenna with Multiple Feed

ABSTRACT A Compact low profile MIMO Diversity antenna system with multiple feeds with a size of 105mm*61.5mm is proposed. Multiple feeds are used to provide maximum power to antenna elements so that signal can propagate a long distance. The proposed antenna is achieving multiple frequencies, i.e. 2.5 GHz, 3.21 GHz, 4.22GHz, 4.68GHz, 6.5GHz, 6.74 GHz, 7 GHz and 8.35 GHz. Measured S-parameters show the isolation is -23.715 db. The maximum achievable bandwidth is 1.32 GHz (1320 MHz). This antenna can be applicable at Wimax, WLAN, LTE and Satellite Bands

ARCADE—Adversarially Robust Cost-Sensitive Anomaly Detection in Blockchain Using Explainable Artificial Intelligence

Blockchain technology is increasingly being adopted across critical domains, such as healthcare and finance, yet it remains susceptible to anomalies and malicious attacks. Hence, robust anomaly detection is essential in these decentralized systems to maintain integrity, trust, and reliability. However, anomaly detection is still challenging due to data imbalances, adversarial resilience, and the lack of explanation in existing approaches. This work presents ARCADE, a novel approach for adversarially resilient anomaly detection in blockchain networks that leverages an optimized cost-sensitive stacking ensemble learning combined with explainable artificial intelligence (XAI) techniques. Firstly, the proposed approach uses cost-sensitive learning to address the data imbalance problem by optimizing class weights that are integrated with stacking ensemble learning to enhance detection accuracy. Secondly, along with this, newly engineered features are employed to strengthen the resilience of the model against malicious perturbations. Lastly, XAI techniques are applied to provide comprehensive insights and explanations for model prediction. To evaluate ARCADE, the Ethereum network transactions dataset is utilized to ensure a realistic case study. The experimental results show the superiority of the ARCADE in several aspects, achieving a high accuracy of 99.65%; strong resilience against adversarial perturbations, achieving an accuracy of 99.38% for low-intensity attacks, 91.04% for moderate attacks, and over 78% for extreme attacks; and surpassing existing techniques while also providing explainability for domain users

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

In project management, effective cost estimation is one of the most crucial activities to efficiently manage resources by predicting the required cost to fulfill a given task. However, finding the best estimation results in software development is challenging. Thus, accurate estimation of software development efforts is always a concern for many companies. In this paper, we proposed a novel software development effort estimation model based both on constructive cost model II (COCOMO II) and the artificial neural network (ANN). An artificial neural network enhances the COCOMO model, and the value of the baseline effort constant A is calibrated to use it in the proposed model equation. Three state-of-the-art publicly available datasets are used for experiments. The backpropagation feedforward procedure used a training set by iteratively processing and training a neural network. The proposed model is tested on the test set. The estimated effort is compared with the actual effort value. Experimental results show that the effort estimated by the proposed model is very close to the real effort, thus enhanced the reliability and improving the software effort estimation accuracy

A Robust Color Image Watermarking Scheme using Chaos for Copyright Protection

An exponential growth in multimedia applications has led to fast adoption of digital watermarking phenomena to protect the copyright information and authentication of digital contents. A novel spatial domain symmetric color image robust watermarking scheme based on chaos is presented in this research. The watermark is generated using chaotic logistic map and optimized to improve inherent properties and to achieve robustness. The embedding is performed at 3 LSBs (Least Significant Bits) of all the threecolor components of the host image. The sensitivity of the chaotic watermark along with redundant embedding approach makes the entire watermarking scheme highly robust, secure and imperceptible. In this paper, various image quality analysis metrics such as homogeneity, contrast, entropy, PSNR (Peak Signal to Noise Ratio), UIQI (Universal Image Quality Index) and SSIM (Structural Similarity Index Measures) are measures to analyze proposed scheme. The proposed technique shows superior results against UIQI. Further, the watermark image with proposed scheme is tested against various image-processing attacks. The robustness of watermarked image against attacks such as cropping, filtering, adding random noises and JPEG compression, rotation, blurring, darken etc. is analyzed. The Proposed scheme shows strong results that are justified in this paper. The proposed scheme is symmetric; therefore, reversible process at extraction entails successful extraction of embedded watermark

A Novel Fingerprinting Technique for Data Storing and Sharing through Clouds

With the emerging growth of digital data in information systems, technology faces the challenge of knowledge prevention, ownership rights protection, security, and privacy measurement of valuable and sensitive data. On-demand availability of various data as services in a shared and automated environment has become a reality with the advent of cloud computing. The digital fingerprinting technique has been adopted as an effective solution to protect the copyright and privacy of digital properties from illegal distribution and identification of malicious traitors over the cloud. Furthermore, it is used to trace the unauthorized distribution and the user of multimedia content distributed through the cloud. In this paper, we propose a novel fingerprinting technique for the cloud environment to protect numeric attributes in relational databases for digital privacy management. The proposed solution with the novel fingerprinting scheme is robust and efficient. It can address challenges such as embedding secure data over the cloud, essential to secure relational databases. The proposed technique provides a decoding accuracy of 100%, 90%, and 40% for 10% to 30%, 40%, and 50% of deleted record

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems

Neighbourhood oriented TDMA scheme for the internet of things-enabled remote sensing

Throughout the world, Internet of Things (IoT) have been used in different application areas to assist human beings in numerous activities such as smart buildings and cities via remote sensing-enabled techniques. However, simultaneous transmission of packet(s) by multiple devices Ci, which are interested to start a communication session with a common receiver device, is one of the challenging issues associated with these networks. In the literature, various mechanisms have been presented to resolve the aforementioned issue without changing the technological infrastructures; however, neighbourhood information of sensor nodes is not considered yet. In IoT-enabled remote sensing, neighbourhood information of various devices plays a vital role in developing a reliable communication mechanism specifically for scenarios where multiple devices Ci are interested to start communication with a common destination module. In this paper, a neighbourhood-enabled TDMA scheme is presented for the IoT to ensure the concurrent communication of multiple devices Ci with a common destination device Sj preferably with a minimum possible packet collision ratio (if avoidance is not possible). The proposed scheme bounds each and every member device Ci to assign a dedicated time slot to its neighbouring devices in the operational IoT network. Furthermore, neighbouring devices Ci are forced to communicate within the assigned time slot. Simulation results have verified that the proposed scheme is ideal solution compared to the existing schemes for the IoT and other resource-limited networks particularly in scenarios where the deployment process is random.The work was supported by the University Malaysia Sabah, Malaysia.Scopu

Neighbourhood oriented TDMA scheme for the internet of things-enabled remote sensing

Throughout the world, Internet of Things (IoT) have been used in different application areas to assist human beings in numerous activities such as smart buildings and cities via remote sensing-enabled techniques. However, simultaneous transmission of packet(s) by multiple devices Ci, which are interested to start a communication session with a common receiver device, is one of the challenging issues associated with these networks. In the literature, various mechanisms have been presented to resolve the aforementioned issue without changing the technological infrastructures; however, neighbourhood information of sensor nodes is not considered yet. In IoT-enabled remote sensing, neighbourhood information of various devices plays a vital role in developing a reliable communication mechanism specifically for scenarios where multiple devices Ci are interested to start communication with a common destination module. In this paper, a neighbourhood-enabled TDMA scheme is presented for the IoT to ensure the concurrent communication of multiple devices Ci with a common destination device Sj preferably with a minimum possible packet collision ratio (if avoidance is not possible). The proposed scheme bounds each and every member device Ci to assign a dedicated time slot to its neighbouring devices in the operational IoT network. Furthermore, neighbouring devices Ci are forced to communicate within the assigned time slot. Simulation results have verified that the proposed scheme is ideal solution compared to the existing schemes for the IoT and other resource-limited networks particularly in scenarios where the deployment process is random