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Recently, the emergence of social media has opened the way for online harassment in the form of hate speech and offensive language. An automated approach is needed to detect hate and offensive content from social media, which is indispensable. This task is challenging in the case of social media posts or comments in low-resourced CodeMix languages. This paper investigates the efficacy of various multilingual transformer-based embedding models with machine learning classifiers for detecting hate speech and offensive language (HOS) content in social media posts in CodeMix Dravidian languages that belong to the low-resource language group. Experiments were conducted on six sets of openly available datasets in Kannada-English, Malayalam-English and Tamil-English languages. The objective is to identify a single pre-trained embedding model that commonly works well for HOS tasks in the above mentioned languages. For this, a comprehensive study of various multilingual transformer embedding models, such as BERT, DistilBERT, LaBSE, MuRIL, XLM, IndicBERT, and FNET for HOS detection was conducted. Our experiments revealed that MuRIL pre-trained embedding performed consistently well for all six datasets using Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. In a set of experiments conducted on six datasets, the highest accuracy results for each dataset are as follows: DravidianLangTech 2021 achieved 96% accuracy for Malayalam, 72% accuracy for Tamil, and 66% accuracy for Kannada. For HASOC 2021 Tamil, the accuracy reached 76%, and for HASOC 2021 Malayalam, it reached 68%. Additionally, HASOC 2020 demonstrated an accuracy of 92% for Malayalam. Moreover, we performed an in-depth error analysis and a comparative study, presenting a tabulated summary of our work compared to other top-performing studies. In addition, we employed a cost-sensitive learning approach to address the class imbalance problem in the dataset, in which minority classes get higher classification weights than the majority classes. The weights were initialized and fine-tuned to obtain the best balance between all the classes. The results showed that incorporating the cost-sensitive learning strategy avoided class bias in the trained model. In addition to the aforementioned points, a significant contribution of our research presented in this paper is introducing a novel annotated test set for Malayalam-English CodeMix. This new dataset serves as an extension to our existing data, known as the Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages (HASOC) 2021 Malayalam-English dataset.

The size of the TCAM memory grows as more entries are added to the flow table of Open Flow switch. The procedure of looking up an IP address involves finding the longest prefix. In order to keep up with the link speed, the IP lookup operation in the forwarding table should also need to be speed up. TCAM's scalability and storage are constrained by its high power consumption and circuit density. The only time- or space-efficient algorithms for improvement are the subject of several research studies. In order to boost performance even further, this study focuses on time and space efficient algorithms. To strike a balance between speedy data access and efficient storage, this study proposes a combination of compression and a quick look-up mechanism to satisfy the space and speed requirements of the Open Flow switch. As the data is compressed, performance improves because less memory is required to store the look-up table and fewer bits are required to search. The look up complexity of proposed approach is $O(log\,(log\;n/2))$ and average space reduction is 61%.

The cardiovascular system is responsible for carrying the blood along with nutrition and oxygen throughout the body. This system consists of heart, blood, and blood vessels. The experts, or doctors called as cardiologists, analyze the sounds of heart’s (lub-dub) beat and flow of blood to diagnose Cardio Vascular Disease (CVD) using a traditional stethoscope and phonological cardiogram technique. Through the stethoscope, the cardiologist will listen to vibration produced by heart beat and heart beat sound and murmur sound are popularly known as phonocardiogram (PCG) signals, which are being recorded for medical diagnosis purposes using a stethoscope. The development of a technique for the automatic recognition of HVD’s assists the experts in recognizing the CVD effectively in the initial stage itself from PCG signals. There are many tools available to help doctors in a clinical setting for the accurate diagnose the CVD in a less time. The main aim of this proposed work is to provide an Artificial Intelligence (AI) based PCG signal analysis for the automatic and early detection of various cardiac conditions using supervised and unsupervised Recurrent Neural Network (RNN) based Bidirectional Long Short-Term Memory (Bi-LSTM) Machine Learning (ML) algorithm. Along with this algorithm, Generative Adversarial Networks (GAN’s) is considered because they can create fresh, high-quality, pseudo-real data that resembles their training set which has been demonstrated by using their two unique networks: Discriminator Network (DN) and the Generator Network (GN). The proposed method is tested using heart sound signals from the well-known, freely accessible PhysioNet and Kaggle datasets. The Experimental results are validated based accuracy, precision, F1-score, sensitivity, and specificity.

This paper presents a groundbreaking advancement in terahertz (THz) technology through the introduction of a novel ultrathin absorber. Composed of vanadium dioxide (VO2) and graphene, this absorber offers a non-metallic solution for wideband absorption, covering an impressive frequency range from 1.198 THz to 8.752 THz (7.554 THz). Our absorber exhibits exceptional tunable and switchable performance, a significant achievement considering the challenge of achieving frequency tunability across broadband absorption ranges, which was the primary focus of our research endeavor. Furthermore, our proposed absorber demonstrates remarkable characteristics, achieving up to 80% absorption even at high incidence angles of up to 80°. Additionally, the absorber enhances Shielding Effectiveness in electromagnetic interference (EMI) shields and stealth systems, further highlighting its versatility and potential impact in various applications.

Energy harvesting and Internet of Things (IoT) technology is being integrated with home appliances. IoT devices could harvest energy from alternative energy sources to convert the available energy from home appliances into electrical energy. Smart home energy management systems face isolated scheduling horizons and unreliable inputs. In this research, an IoT Heterogeneous Energy Harvesting (IHEH) technique has been proposed for residential home energy management to operate the smart home appliances which consists of four layers, namely, the Energy Harvesting Layer (EHL), the Control and Sensing Layer (CSL), the Application Layer (AL), and the Information Processing Layer (IPL). The proposed technique keeps track of the distribution of several types of energy, including thermal, piezoelectric, and light energy. The overall efficiency of the proposed method is 90% per day. The proposed IHEH technique yields more power and increases the lifetime of batteries.

Blockchain technology has fascinated and significantly sparked research activities and industries due to its transparency in transactions and data sharing. The striking proliferation of blockchain technology is used to confront the challenges associated with trust, transparency, security, centralization, supply chain traceability, and regulatory compliance. The promising technology has the potential to enable several boons across diverse fields like healthcare, supply chain management, manufacturing, cross-border payments, finance, and energy trading. Telemedicine primarily aims to facilitate the transmission of healthcare data through electronic channels, enabling users to access medical services. It supports healthcare services around the globe, aids in early diagnosis and treatment, and assists with remote care by provisioning effective healthcare that is safe, secure, and reliable. However, there are challenges associated with Personal Health Records (PHR) due to lack of ownership of the data, accuracy, reliability, and data transaction security. The architecture presented in this work enables us to benefit from telemedicine and securely share PHR. The proposed model offers a permissioned network-based Hyperledger Fabric blockchain framework for secure PHR sharing amongst healthcare providers. The Byzantine Fault Tolerance consensus mechanism protects patient privacy, and the IPFS protocol is used to store the data off-chain. Additionally, the smart contract is utilized for providing patients with granular access control over PHR data. Hyperledger Caliper is used as a benchmarking tool to test this technology and determine the average latency rate for viewing and updating the PHR by healthcare providers along with analysis of CPU and memory utilization. As a result, our goals in telemedicine to improve secure sharing and give the patient access control are achieved.

In the field of vehicular communication, the Internet of Vehicles (IoV) serves as a new era that guarantees increased connectivity, efficiency, and safety. The modern area and new technology have their challenges and constraints, though. This paper thoroughly examines these constraints significantly; we show how blockchain technology is being used to overcome them. This paper primarily explores the complexities of Blockchain-enabled Internet of Vehicles (BIoV) architectures, the applications they serve, and the robust security features they provide through a systematic literature review (SLR). In addition, we look at the several ways that blockchain and IoV might be integrated and investigate the subtle factors that should be considered when choosing consensus algorithms to maximize performance on different blockchains. This paper also addresses the methods and tools used to identify and avoid fraudulent activities in BIoV networks at a maximum level of security. It also reveals the wide range of BIoV applications and analyzes the different security levels they provide. In closing, we give an idea of the possibilities that will continue to develop the blockchain and IoV environment, reducing the roadblocks and advancing this combination toward a more secure, effective, and connected future for vehicle communication systems.

In this research, we introduce a metamaterial biosensor designed for the detection of various viruses. The proposed sensor configuration comprises four square split ring resonators (SRR) coupled with a central graphene ring. The strong coupling between SRR and graphene ring results in near-perfect absorption at a frequency of 1.354 THz. To enhance versatility, the sensor’s performance can be tuned and controlled by adjusting the chemical potential ( $\mu _{\mathrm {c}}$ ) of the graphene. Additionally, we validate the sensor’s functionality through an equivalent circuit model. Moreover, the proposed sensor demonstrates outstanding performance metrics, including a high sensitivity (S) of 1.7 THz/RIU, a Figure of Merit (FOM) of 165.09 RIU−1, high-quality factor (Q) of 112.5. Despite its reduced structural thickness of $3~\mu \text{m}$ , it remains suitable for integration into nanotechnology devices. Furthermore, the absorber exhibits exceptional absorption properties even at steep incidence angles, expanding its applicability in diverse scenarios. Its potential to detect a wide range of viruses, including malaria, dengue, herpes simplex virus, influenza, and HIV, and distinguish various cancerous cells, holds promise for advancing biosensing applications.