In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. The machine learning methods greatly offer an efficient prediction ability in diagnosis system alternative with the clinicians. Most of the systems operate on the extracted features from the patients and most of the predicted cases are accurate. However, in recent time, the prevalence of COVID-19 has emerged the global healthcare industry to find a new drug that suppresses the pandemic outbreak. In this paper, we design a Deep Neural Network (DNN) model that accurately finds the protein-ligand interactions with the drug used. The DNN senses the response of protein-ligand interactions for a specific drug and identifies which drug makes the interaction that combats effectively the virus. With limited genome sequence of Indian patients submitted to the GISAID database, we find that the DNN system is effective in identifying the protein-ligand interactions for a specific drug.

In the medical field, some specialized applications are currently being used to treat various ailments. These activities are being carried out with extra care, especially for cancer patients. Physicians are seeking the help of technology to help diagnose cancer, its dosage, its current status, cancer classification, and appropriate treatment. The machine learning method developed by an artificial intelligence is proposed here in order to effectively assist the doctors in that regard. Its design methods obtain highly complex cancerous inputs and clearly describe its type and dosage. It is also recommending the effects of cancer and appropriate medical procedures to the doctors. This method ensures that a lot of doctors’ time is saved. In a saturation point, the proposed model achieved 93.31% of image recognition, 6.69% of image rejection, 94.22% accuracy, 92.42% of precision, 93.94% of recall rate, 92.6% of F1-score, and 2178 ms of computational speed. This shows that the proposed model performs well while compared with the existing methods.

Intelligent transportation systems (ITS) are a newer trend in technology that improve the safety and performance in transportation systems. The exchange of information is considered as one of the key elements for ITS since it involves communication between vehicles and other components of ITS on road. On other hand, the data collections via Internet of Things (IoT) sensors play a major role for data collection and transmission between the vehicles and road segments in ITS. The data collection provides the current traffic and weather conditions that is considered necessary for driving in traffic. However, energy savings is one of the predominant objectives of electric vehicle (EV) while it is connected with ITS. In this research, we propose an ontology‐based architecture for EVs using the data collected from IoT sensor network, which is intended to improve the overall driving experience. The system uses IoT sensor data to execute a range of activities in order to ensure the driver safety and comfort while on the road. The simulation is conducted on an Eclipse SUMO simulator, and the performance is reported. The results of simulation shows that the proposed intelligent model on making decisions along with weather and traffic conditions is reported efficient than existing ITS models.

The number of users of current mobile and cellular networks is constantly increasing. The allocation of spectrum to its users is constantly facing various hurdles. This causes users to leave that network and connect to another network. Thus, those telecom companies are constantly struggling to provide high-speed services to the users. As a result, the demand for 5G networks is currently increasing. Considering these, an algorithm has been proposed here to suit the needs of the users. Its main feature is that it easily identifies the primary and secondary users of the 5G network and creates a spectrum allocation system for them accordingly. Generally, all other methods are designed with the primary user in mind. Furthermore, the spectrum hole calculation that is currently being proposed is done accurately so that the spectrum switching processes required for the secondary user can take place here very quickly so that the secondary user can use the spectrum without any hindrance. The proposed model achieved 93.29% of spectrum blocking, 6.71% of spectrum band dropping, 94.03% bandwidth utilization, 1073 ms end-to-end delay, and 17273 bps of throughput. The proposed model effectively handles the spectrum and intelligent approach to resolve the spectrum hole problems. The existing models are practically faced with these problems. The proposed model spectrum utilization and efficiency were increased compared with the existing models.

Asynchronous NOCs are most prominent in present SOC designs, due to their low dynamic power consumption, modularity, heterogeneous nature, and robustness to the process variations. Though asynchronous designs are proved efficient over synchronous counterparts, they have some severe drawbacks when area and speed are considered, due to complex handshake control circuits which increase the static power loss. Quasidelay insensitive (QDI) class of asynchronous NOCs based on 2-phase encoding is proved beneficial for speed and throughput enhancement but with complex design. The work has introduced lightweight minimal buffer router based on LEDR encoding to design a low power, high speed with compact NOC architecture. Then, minimal buffer router with FSM-based arbiter and priority assigner block is designed to enhance the speed, power, and area. This proposed work achieves zero dynamic power consumption with a total power consumption of less than 0.082W with a router latency of 0.8ns.

Biomedical IoT, predictive modeling, disease diagnosis, and temporal data analysis have become essential components in modern healthcare systems. Accurate and efficient predictive disease modeling is crucial in biomedical IoT for early detection and intervention, yet existing models like LSTM and XGBoost face limitations in handling the complexity and temporal nature of health data streams. The research article addresses this gap by introducing the Temporal Adaptive Neural Evolutionary Algorithm (TANEA), a novel approach designed to enhance the predictive capabilities in biomedical IoT systems. TANEA leverages temporal data patterns, adapts to dynamic changes in sensor readings, and optimizes feature selection through an evolutionary mechanism, resulting in a more precise and reliable predictive model. Experimental evaluations demonstrate TANEA’s superior performance over traditional methods, achieving improved accuracy, reduced computational overhead, and faster convergence rates. The algorithm’s adaptability to various biomedical data patterns enables more effective real-time monitoring and decision-making in IoT-based healthcare environments. These results highlight TANEA’s potential to revolutionize predictive disease modeling, offering a robust solution for intelligent health monitoring and proactive healthcare interventions in the IoT ecosystem.