A Novel computer assisted genomic test method to detect breast cancer in reduced cost and time using ensemble technique

Breast cancer is the leading cause of death among women around the world. It is a primary malignancy for which genetic markers have revealed the ability for clinical decision making. It is a genetic disease that generates due to gene mutations, but the cost of a genetic test is relatively high for a number of patients in developing nations like India. The results of a genetic test can take a few weeks to determine cancer. This time duration influences the prognosis of genes since certain patients suffer from a high rate of malignant cell proliferation. Therefore, a computer-assisted genetic test method (CAGT) is proposed to detect breast cancer. This test method will predict the gene expressions and convert these expressions in the state of mutation (under-expression (-1), transition (0) overexpression (1)) and afterwards perform the classification to get the benign and malignant class in reduced time and cost. In the research work, machine learning techniques are applied to identify the most responsive genes of breast cancer on the premises of the clinical report of a patient and generated a CAGT. In the research work, the hard voting ensemble approach is applied to detect breast cancer on the basis of most responsive genes by CAGT which leads to improving 3.5% accuracy in cancer classification

ETMA: Efficient Transformer Based Multilevel Attention framework for Multimodal Fake News Detection

In this new digital era, social media has created a severe impact on the lives of people. In recent times, fake news content on social media has become one of the major challenging problems for society. The dissemination of fabricated and false news articles includes multimodal data in the form of text and images. The previous methods have mainly focused on unimodal analysis. Moreover, for multimodal analysis, researchers fail to keep the unique characteristics corresponding to each modality. This paper aims to overcome these limitations by proposing an Efficient Transformer based Multilevel Attention (ETMA) framework for multimodal fake news detection, which comprises the following components: visual attention-based encoder, textual attention-based encoder, and joint attention-based learning. Each component utilizes the different forms of attention mechanism and uniquely deals with multimodal data to detect fraudulent content. The efficacy of the proposed network is validated by conducting several experiments on four real-world fake news datasets: Twitter, Jruvika Fake News Dataset, Pontes Fake News Dataset, and Risdal Fake News Dataset using multiple evaluation metrics. The results show that the proposed method outperforms the baseline methods on all four datasets. Further, the computation time of the model is also lower than the state-of-the-art methods

SWIPT-Enabled D2D Communication Underlaying NOMA-Based Cellular Networks in Imperfect CSI

Device-to-Device (D2D) communication is an emerging paradigm which enhances the coverage, capacity, and spectral efficiency of the network using cooperative communication in an underlay of cellular networks. In spite of these advantages, it suffers from large bandwidth and energy loss due to usage of half-duplex relaying and limited energy storage devices. To solve these issues, we integrate the full-duplex (FD) relaying and time splitting simultaneous wireless information and power transfer (SWIPT) technique at the D2D transmitters (DDTs). Moreover, to support the ubiquitous connectivity and reduce the latency, non-orthogonal multiple access is used at the base station. The SWIPT-FD-based DDTs and SWIPT-based cell user equipment (CUE) harvest the energy from the base station, after that the DDTs and CUEs decode the desired signal from the multiplexed signal using successive interference cancellation technique, and forward the desired signal to the D2D user equipment (DUE) to improve their QoS. In this paper, ergodic capacity at the DDTs and DUEs & closed-form expressions of the outage probabilities are derived with imperfect CSI. The results demonstrate that the proposed scheme achieves better performance as compared to that of the existing SWIPT-FD-OMA based scheme

An Energy-Efficient Resource Allocation Scheme for SWIPT-NOMA Based Femtocells Users with Imperfect CSI

In this paper, we propose, an energy-efficient resource allocation scheme for simultaneous wireless information and power transfer (SWIPT)-Non-orthogonal multiple access (NOMA) based femtocell users with imperfect channel state information. SWIPT enhances the transmission power of Femto users and NOMA improves the spectral efficiency. Therefore, SWIPT and NOMA at Femto base station are integrated to improve the energy efficiency and spectral efficiency of femtocells. The formulated resource allocation problem is divided into subchannel allocation and power control. The preference list of Femto users and macro users is developed on the basis of their energy harvested from the Femto base station and macro base station, respectively. Then, to optimize the power of a Femto base station, a successive convex approximation low complexity technique at each Femto base station is used. Also, Dinkelbach method is used to convert the non-liner fractional problem into a subtractive problem and Lagrangian duality is used for power computation of Femto base station. Finally, the simulation results demonstrate that our proposed scheme provides 0.2% and 31.25% higher energy-efficiency in comparison to the existing EH-NOMA and EH-OFDMA schemes

A Novel computer assisted genomic test method to detect breast cancer in reduced cost and time using ensemble technique

Breast cancer is the leading cause of death among women around the world. It is a primary malignancy for which genetic markers have revealed the ability for clinical decision making. It is a genetic disease that generates due to gene mutations, but the cost of a genetic test is relatively high for a number of patients in developing nations like India. The results of a genetic test can take a few weeks to determine cancer. This time duration influences the prognosis of genes since certain patients suffer from a high rate of malignant cell proliferation. Therefore, a computer-assisted genetic test method (CAGT) is proposed to detect breast cancer. This test method will predict the gene expressions and convert these expressions in the state of mutation (under-expression (-1), transition (0) overexpression (1)) and afterwards perform the classification to get the benign and malignant class in reduced time and cost. In the research work, machine learning techniques are applied to identify the most responsive genes of breast cancer on the premises of the clinical report of a patient and generated a CAGT. In the research work, the hard voting ensemble approach is applied to detect breast cancer on the basis of most responsive genes by CAGT which leads to improving 3.5% accuracy in cancer classification

Federated reinforcement learning based task offloading approach for MEC-assisted WBAN-enabled IoMT

The exponential proliferation of wearable medical apparatus and healthcare information within the framework of the Internet of Medical Things (IoMT) introduces supplementary complexities pertaining to the elevated Quality of Service (QoS) of intelligent healthcare in the forthcoming 6G era. Healthcare services and applications need ultra-reliable data transfer and processing with ultra-low latency and energy usage. Wireless Body Area Network (WBAN) and Mobile Edge Computing (MEC) technologies enabled IoMT to handle large amounts of data sensing, transmission, and processing while maintaining good QoS. Traditional frame aggregation (FA) systems in WBAN, on the other hand, create an excessive number of control frames during data transmission, resulting in significant latency and energy consumption, as well as a lack of flexibility. A Federated Reinforcement Learning (FRL) based TO Approach is recommended in this research. In the beginning, different types of service-related information were separated into queues with equal QoS needs. The duration of the FA was then automatically determined by the aggregation vertex based on energy consumption, latency, and throughput using FRL. Finally, based on the existing status, the amount of tasks offloaded was determined. The simulation results demonstrate that, as compared to the baseline schemes, the suggested FRLTO efficiently reduces energy consumption and latency while enhancing throughput and total WBAN utilization. Numerical results show that the proposed scheme improves the throughput by 37.06% and reduced the energy consumption by around 69.84% and time delay by about 6.23%, as compared to the state-of-the-art existing baseline schemes

Holochain: An Agent-Centric Distributed Hash Table Security in Smart IoT Applications

The accomplishment of blockchain has increased the focus on the various applications for simplifying the confidentiality and transaction sanctuary using the decentralized architecture via consensus mechanisms between different internet of things (IoT) nodes in daily increasing societal areas. The growth of blockchain lasted to grow and used to do compare technologies. The major shortcomings of blockchain is the lack of scalability in modern application settings. Holochain technology vends itself as a “thinking” exterior to blocks, and it is a peer-to-peer disseminated ledger technology. It works contrarily compared to the blockchain, and it offers an exclusive value in the existing market. IoT devices are continuously used in distributed environments, in various smart applications. The peer-to-peer IoT networks, connected to smart agricultural systems are exposed to the security issues. Specifically, the personal data of agricultural land records need protection against unauthorized access and eradicate corruption in land transactions. The Blockchain offers a possible solution based on distributed ledger, but it has scalability issues due to high storage and processing requirements with growing network size. Also data is not locally stored in a Blockchain. This paper studies the conventions of holochain technology, its architecture and challenges, and critical mechanisms of holochain applications. We also analyze the numerous models utilized for the implementation of protected transactions. We discuss an agent centric framework with distributed hash table for secured applications