The transmission nature of Primary User (PU) is often unpredictable, which leaves certain frequency bands or geographic regions temporarily unoccupied—these underutilized portions, known as “spectrum holes,” represent the opportunity for dynamic spectrum access. Accurate Spectrum sensing is crucial that enables the cognitive radio (CR) system to optimize spectrum utilization efficiently. On the other hand, conventional sensing approaches face performance degradation, in low signal-to-noise ratio (SNR) conditions, due to high false alarm and low detection accuracy. To overcome these challenges, this study introduces a multi-feature-based hybrid deep learning model named DeepMLLHSNet, designed to enhance reliability, detection accuracy and robustness even under adverse situations. It integrates four distinct features of signal like, I/Q components, cyclostationarity, power spectrum and energy statistics—into a feature matrix facilitating efficient feature learning along with robust classification. For performance estimation, RadioML2016.10b dataset was used in simulation with special focus on low SNR condition (e.g., -20 dB to 0 dB). Proposed framework shows improvement in sensing than both traditional models (CNN, Inception, ResNet, LeNet, LSTM, CLDNN) and recent hybrid approaches (CNN-LSTM, DetectNet, ResNet-LSTM, DeepSenseNet) hence confirming its efficiency in spectrum sensing in challenging condition. DeepMLLHSNet has achieved 98.89% prediction accuracy(Pa) with 97.65% precision and 97.76% recall.

WSNs have truly revolutionised the realisation of real-time data acquisition and processing in almost every area, from the healthcare sector to industrial automation and environmental monitoring. Decentralised data collection is essential for these applications, which necessitate instant feedback and action. WSNs have enormous potential, but processing poses a challenge. Sensor nodes in such a network typically offer very limited computing capabilities and resources for executing significant calculations. This work is connected to the concept of introducing edge computing into WSNs to overcome such problems. In general, this integration is targeted at improving WSN performance, which will be achieved by reducing Latency, utilising bandwidth efficiently, and enhancing data processing efficiency. The primary reason for edge computing is to bring processing closer to the data source. The data source in this case would be the sensor node; this would lead to the immediate analysis of the data, and subsequently, decisions would be made quickly.

Banking and financial institutions need to have the correct and safe automated categorization of sensitive records, including invoices, tax forms, and bank statements. Standard rule-based systems and simplistic machine learning models are commonly unable to handle unstructured document layout, template variability, and Optical Character Recognition-induced noise, and therefore scale poorly and achieve low classification rates. To solve these problems, introduce a privacy-aware convolutional neural network architecture to classify financial documents based on text extracted by Optical Character Recognition. In the proposed pipeline, document images are converted to machine-readable text via OCR, followed by preprocessing steps, including token cleaning, normalization, and word embedding, to minimize recognition errors and noise. The convolutional neural network architecture is designed to identify contextual and semantic patterns in text sequences, enabling high-accuracy classification across a wide range of document types. To ensure compliance with financial data protection requirements, the framework incorporates mechanisms for data security, encrypted and controlled model storage, and a controlled inference process during training and deployment. The experimental findings indicate that the Secure convolutional neural network - Financial Document Classification is more accurate, efficient in processing, and more resistant to OCR noise than baseline classifiers. The proposed framework will facilitate scalable, secure, and automated financial document management across cloud and on-premises environments.

Communication and signal processing improve the efficiency of antennas; this is the core reason why smart system integration is dependent on antenna engineering. In addition, the development of smart networks that offer real-time data transmission is indeed very much supported by the combination of the antenna with the expanding IoT technology. The integration of smart devices has been restricted through interference, inadequate bandwidth and energy losses. This revolves around the issues of energy supply, equipment design and practical deployment in the proposed method ‘Integrated Smart Antennas with IoT (ISA-IoT), which proposes the use of IoT based antennas aimed at fitting such tasks. This solution offers low power consumption, lowers noise and enhances reliable communication by employing dynamic beam switching and tuning interference frequency. Integrated industrial IoT, smart houses and home healthcare monitoring are among the good application of the proposed method as it guarantees efficient use of energy resources and allows for reliable communication and intensive data treatment. Evaluation results demonstrate an evidence of gain in reliability, throughput and performance of the system.

Sustainable and renewable energy is the wave of the future, and scientists across the world are working harder than ever to perfect biomass conversion technology. Biomass has great promise as a carbon-neutral resource that can help meet the worlda s growing energy needs while reducing emissions of harmful gases. Nevertheless, energy losses are common in current conversion processes because of less-than-ideal operating circumstances and a lack of optimization in thermochemical and biochemical interactions. Improving the total efficiency of biomass conversion for high-yield renewable energy production is the main emphasis of this project, which aims to establish an integrated computational framework. Through the use of adaptive management of feedstock composition, reaction temperature, and pressure, the suggested conceptual algorithm integrates pyrolysis, gasification, and anaerobic digestion into a single optimization model, therefore minimizing conversion inefficiencies. Using a hybrid modeling method that combines process kinetics with data-driven optimization approaches, this study predicts the highest feasible power conversion efficiency (PCE) for different biomass feedstocks. This is what makes it unique. Results show that energy recovery is 6-8% better and carbon losses are reduced by up to 10% compared to traditional modeling tools like Aspen Plus and BioWin. The findings show that the suggested model might be a long-term, efficient, and scalable answer for current biomass power plants.

Smart utility grids make use of continuous consumption data streams to make the best delivery of energy and identify abnormal use patterns. Smart grids involve the detection of abnormality to prevent energy theft, equipment malfunction, as well as enhance efficiency on operation. The old methods are being spoiled by the weaknesses in terms of temporal dependency and real-time learning capabilities, so they cannot be applied to changing circumstances. It is a work that introduces Real-Time Detection and Prediction model of Abnormal Utility Usage in residential or industrial spaces driven by Temporal Fusion Transformer (TFT). The new approach takes advantage of the capability of TFT to form long term dependencies, multiple input variables, and inclusion of attention mechanisms that allow explainability in forecasting. It allows real-time and correct detection of anomaly in the use of electric power, water, or gas. The model was tested with real utility usage data and it performed better than the traditional machine learning models both in terms of speed and accuracy in detection. Through experimental evidence, TFT-based system has demonstrated to detect malicious usage patterns at a higher degree of reliability and effectiveness in smart utility grids.

The primary user (PU) transmission is sporadic in nature, which explains why the PU is inactive during some time slots, geographic directions or frequency bands. The frequency bands where the PU is not active are called "spectrum holes". Secondary users (SUs) periodically perform sensing to detect the spectrum holes and monitor primary spectrum. For the best possible spectrum utilization, PU signal detection is very crucial. For measuring the spectrum sensing performance, two main metrics are applied, like, probability of false alarm (PFA) and probability of detection (PD). Due to PFA and PD, the conventional sensing techniques have to face issues. These two constraints used to hinder spectrum utilization. Traditional sensing strategies are mostly based on feature extraction of received signal. Advancement of artificial intelligence (AI) has reduced the inaccuracy in detection of spectrum hole. Deep learning (DL) based approaches have shown a remarkable improvement in this aspect. Hence, the present research work was undertaken to address the problem of spectrum sensing in low SNR and improves accuracy. This research penetrates into the use of deep neural network (DNN) for sensing the vacant spectrum accurately. In this article, RadioML2016.10b dataset was used for the experiments. The results are also studied. The proposed approach shows betterment in sensing than other existing spectrum detection models. DeepSenseNet model was validated through simulation results and showed that it has achieved 98.84% prediction accuracy (Pa) with 97.53% precision and 97.62% recall.

MillimeterWaves (mmWave) RadarCommunicating(RC) technologies are anticipated to mitigate spectrum collision and radiointervention in fifth-generation (5G)Vehicles-To-Everything (V2X) networks.The dimensions of radar objectives in the V2X technology should not be overlooked, considering the need for short-distance detection and beams facilitated by mmWave beamforming. In this context, a unique Singular-Targeted-Multiple-Beams (STMB) beam-formation technique is developed to provide greater accuracy regarding estimated distances and speeds by distributing multiple radio beams to a specific goal. The comparable velocity orientation can be precisely calculated utilizing the STMB scheme through balanced linear estimating techniques.The mixture of analog-digital beamforming using the STMB system is developed and improved by improving the propagation rate while adhering to radar signals-to-interference-to-noise (SINR) limitations. A beam cancellation method is suggested for automatically adjusting the number of radar beams directed at a specific target, ensuring compliance with stringent radar SINR limitations across varying distribution power stages.The results confirm the efficacy and dependability of the proposed method, demonstrating that the suggested method surpasses the standard in terms of spectrum effectiveness and minimal radar SINR.

The enormous developments of gaming devices as well as mobile apps have increased the demand of bandwidth. Development of wireless applications has been affected because of the insufficient spectrum resources in the 3G and 4G network. This spectrum scarcity is the main limitation of 3G and 4G networks, which leads to the revolution of the 5G technology. Spectrum scarcity is a great challenge in wireless communication. There are two significant research areas that need to be explored such as, searching spectrum resource and maximum spectrum utilization. To address spectrum insufficiency, consistent research is needed to find out spectrum band and share the spectrum resource. In this work, complete Spectrum management framework is discussed as well as spectrum sharing techniques are classified. We have conducted a thorough survey on various spectrum sharing schemes. The literature survey has been classified based on some significant sharing techniques depending on spectrum access method, network architecture, spectrum allocation behavior etc. We have also summarized contribution as well as limitation of recent spectrum sharing approaches. To enhance 5G technology, related spectrum sharing surveys are studied and future research directions are also discussed here.