This research investigates the detection of pleasant fragrances using a comprehensive approach leveraging advanced machine learning techniques. Initially, a dataset comprising pleasant (Indian Jasmine) and unpleasant (Human Urine) samples, collected via GC-MS dataset is utilized. To address the challenge of missing data, a Missing Completely at Random (MCaR) based preprocessing method is applied to the dataset. Following preprocessing, a Heterogeneous Graph Convolution Network with Cell Attention (HGCNCA) is employed to accurately classify the jasmine fragrance. To further enhance the performance of the HGCNCA, a skill optimization algorithm is incorporated. This combination of preprocessing and advanced graph-based deep learning techniques aims to improve the reliability and precision of fragrance detection, providing a robust framework for distinguishing between pleasant and unpleasant odors based on analytical data. The proposed method attains higher accuracy as 99.9

This paper introduces a novel design and development of a compact circularly polarized antenna operating at two distinct frequency bands, 2.5 GHz and 6 GHz. The innovation lies in using a modified circular patch and a uniquely designed feeding element, which optimize circular polarization performance while maintaining a low-profile form factor. Fabricated on an FR4 substrate with an overall thickness of 1.6 mm and dimensions of 62 mm × 62 mm × 1.6 mm, corresponding to approximately 0.5167λ × 0.5167λ × 0.0133λ at 2.5 GHz, the antenna achieves a compact configuration well-suited for space-constrained applications. Extensive simulations and measurements show close agreement, with axial ratios below 3 dB across both operational bands, ensuring effective circular polarization. The antenna achieves peak gains of 1.8 dBi at 2.5 GHz and 3.8 dBi at 6 GHz, highlighting its ability to combine compactness, dual-band functionality, and strong radiation characteristics. This proposed design provides a groundbreaking solution for Industrial, Scientific, and Medical (ISM) and Vehicle-to-Everything (V2X) communication systems, addressing the increasing demand for efficient, dual-band, and space-saving wireless communication solutions.

Summary One of the key strategies in wireless sensor network (WSN) for sending data packets to the base station (BC) is known as routing. However, malicious node outbreaks will occur, which exaggerate the functions of WSNs during the routing procedure. A secure routing protocol is necessary to protect the effectiveness of WSN and routing fortification. In real‐time scenarios, the existing routing protocol is volatile dynamically; it makes difficult to identify the performance of unprotected routing nodes. In this manuscript, a unique routing strategy is proposed for secured dynamic optimal routing (SDOR) in WSN that combines block chain technology optimized with a generative adversarial capsule network. Every routing sensor node contains a secret key, which is used to construct a crypto hash signature (CHS) token for flow access. The proposed block chain‐based WSN is used to provide SDOR information using a self‐attention‐based generative adversarial capsule network that is improved using the flamingo search algorithm. Following that, the security on the proposed block chain WSN is assessed from six angles. Block chain token transactions are evaluated using measures including average latency, average energy usage, and throughput. The proposed SDOR‐SA‐GAC‐FSA‐CHS‐BWSN delivers 53.87%, 42.57%, and 32.87% lower average packet delay, 28.97%, 37.73%, and 34.75% lesser identification time analyzed to the existing methods, such as trusted distributed routing scheme for WSNs using block chain (SDOR‐DCNN‐SSOA‐BWSN), secure authentication with DSM‐KL ascertained presentation optimization of hybrid block chain‐enabled model for multi‐WSN (SDOR‐DSM‐KL‐BWSN), and optimizing the valid transaction utilizing reinforcement learning‐based block chain ecosystem in WSN (SDOR‐RIL‐BWSN).

Unmanned Aerial Vehicle (UAV) swarms play an essential role in emergency communication situation, especially when conventional infrastructure is under attack. Making effective routing protocols for Flying Ad Hoc Networks (FANETs) is a challenging task. Current routing protocols-which were frequently created for MANETs and VANETs-assume that UAV nodes move randomly, which is inappropriate in mission-oriented applications. The purpose of this research is to alleviate network bottlenecks in mission-oriented UAV swarms by proposing a novel 3D cone-shaped location-aided routing protocol with increased Quality of Service (QoS), called LARP-EQ. Modified Location Aided Routing protocol (MLARP) balances power consumption and end-to-end (E2E) delay to maximize network performance. The outcomes of the simulation demonstrate significant improvements in 20% Packet Delivery Ratio (PDR), 15% average end-to-end delay and better energy consumption over the current standards.

The Onion Routing (Tor) network has created to allow manipulators to peruse the internet anonymously. It is renowned for its strong anonymity and privacy features, which protect against various agents attempting to monitor client activities or track their browsing habits. Despite the anonymity Tor provides, Onion URLs (Uniform Resource Locator) are generated based on cryptographic keys, making them challenging to discover or censor the active hidden services. The primary objective is to enhance privacy and security on the internet by providing anonymity to users. To create an efficient online environment, allowing users to access information and communicate without the fear of surveillance or censorship. Machine Learning (ML) algorithms used to adaptively manage data traffic, ensuring that it aligns with network demands. The improvised crawling algorithm improving the by frequent IP address modification in addition to that onion routing ensuring that users access hidden services securely while maintaining the principles of anonymity and privacy inherent in the Tor network. The latency of the network is compared with different traffic requests, from that the proposed system ToR-ML outperforms 45% more when comparing with the existing ToR network system.

First era of mobile device introduction have taken place more than a decade ago. The latest smart phones and the applications that which we download from web are more latest in version and work nature. Furthermore for publishing, music, television, film, photography, navigation, banking, transportation etc. have become much more smarter with enhanced features. While trying to cope up with these technologies the next wave of technology is here and wearing it. Wearable technologies include from the base to the top such as trackers, smart-watches, ear wear, smart clothing, smart jewellery etc. This is said to be one of the latest and fastest growing technologies in the industry. Wearable devices are now a days coming up with greater innovative solutions. They play a major role in the medical field with many latest applications like prevention of diseases, maintenance of health, weight control, physical activity monitoring etc. It also comes with embedded SOC (system on chip). This review focuses on the existing wearable devices which are used in medical industry and future research scope for various applications.

Spectrum sensing allows cognitive radio systems to detect relevant signals even in the presence of interference for reliable communication. Most of the existing spectrum sensing techniques use a particular signal-to-noise ratio model with assumptions and provide certain detection performance. Dynamic spectrum management techniques enabled the efficient allocation of channels to increasing number of users. In cognitive radio system, the dynamic spectrum management is efficient for sensing the channel occupancy and mobilizing the secondary user towards the unused primary user channel. For spectrum sensing, wavelet-based spectrum sensing method is analyzed and effectively made spectrum decision. The performance analysis is made for various SNR values with enhanced false alarm and throughput in spectrum management cognitive radio system in 2.2 GHZ band communication.

This article shows a novel variation in high increase turned notch type receiving antenna working at Wireless-A, N, and AC band standards. The structure is organized with test encouraging in a specific request to get round polarization. By utilizing the consecutive turn method, the proposed receiving antenna develops impedance transmission capacity of over 50% (return loss <- 30 dB) and 3dB axial ratio capacity of 25% in the working band with a gain of 13.55 dBi at an operating frequency of 5GHz. The substrate made of RT Duroid material at εr =2 outcomes is of great concurrence with the achieved results. To achieve circular polarization, circular patches are constructed with probe feeding in a certain order. The suggested antenna has an impedance bandwidth of more than 40% (return loss less than -10 dB) and a 3dB axial ratio bandwidth of 15% in the operational band, with a peak gain of about 13 dB, thanks to the sequential rotation approach. The array antenna is constructed on an RT-duroid substrate, and the measured results match the modelling findings well.

The precise recognition of cells is required for the diagnosis of numerous blood illnesses, such as Leukemia and Myeloma, using WBCs or Leukocytes. In order to assist oncologists an automated system is necessary to minimize time and improve accuracy. The segmentation and classification of immature white blood cells are the most crucial phases in such systems. In this aper we have discussed the methodology for detection of white blood cancer caused because of immature leukocytes by using preprocessing, segmentation by Otsu algorithm, Salp swarm algorithm and random forest algorithm. In this paper we have explored the performance of three techniques such as random forest algorithm (RSA) Salp swarm algorithm (SSA) and Statistically enhanced Salp swarm algorithm (SESSA). We have achieved better segmentation and detection accuracy compared to other peer soft computing algorithms. The results shall be beneficiary for hematologists and oncologist as diagnostic reference before proceeding t the treatment strategy

The COVID-19 pandemic has affected countries economically due to lack of documented guidance; it has also resulted in increased spread and mortality rate. This motivated the authors to design a disaster preparedness system to enable countries to plan strategically and strengthen their efforts in future events. Web-scraping technique is used for crowdsourcing both past and current information such as government policies, associated cost, the spreading and mortality rate, and the medicines used; the results are stored in a database. The framework aims to enable the government bodies and other stakeholders to detect, prevent, and respond to future novel pandemics. Thus, the proposed model aids as a reference tool and can be used for predictive analysis. This information collectively helps countries to acknowledge their strength, weakness, opportunity, and threat (SWOT) ranking and allows them in their national capacities to improve their strategies, infrastructure, and to draft plans in line with the most updated guidance.

The advent of the automated technological revolution has enabled the Internet of Things to rejuvenate, revolutionize, and redeem the services of sensors. The recent development of microsensor devices is distributed in a real-world terrestrial environment to sense various environmental changes. The energy consumption of the remotely deployed microsystems depends on its utilization efficiency. Improper utilization of sensor nodes’ heterogeneity could lead to uneven energy consumption and load imbalance across the network, which will degrade the performance of the network. The proposed heterogeneous energy and traffic aware (HETA) considers the key parameters such as delay, throughput, traffic load, energy consumption, and life span. The residual energy and a minimum distance between the base station and cluster members are taken into consideration for the cluster head selection. The probability of hitting data traffic has been utilized to analyse energy and traffic towards the base station. The role of the sensor node has been realized and priority-based data forwarding are also proposed. As a result, the heterogeneous energy and traffic aware perform well in balancing traffic towards the base station, which is analysed in terms of maximum throughput and increase in a lifetime of heterogeneous energy networks more than 5000 rounds, and the algorithm outperforms 34.5% of nodes are alive with transmissible energy. The proposed research also endorses unequal clustering and minimum energy consumption. We have modeled our proposed research using various p-type junctionless nanowire FET without doping injunctions. The materials used in this analysis were silicon (Si), germanium (Ge), indium phosphide (InP), gallium arsenide (GaAs), and Al(x)Ga(1−x)As. The dimensions of the p-type cylindrical nanowire channel were 25 nm long and 10 nm in diameter.

The latest innovative technology products in the market are paving the way for a new growth in the medical field over medical wearable devices. Globally, the medical market is said to be segmented on the basis of global medical wearable report by its type, application level, regional level, and country level. In this medical advisory, these devices are classified as diagnostic, therapeutic, and respiratory. The regions covered include Europe, Asia-Pacific, and the rest of the world. These wearable devices are technically embedded with electronic devices which the users are able to adhere to their body parts. The main function of these wearable devices is said to be collecting users' personal health data (e.g., such devices include measurement on fitness of body, heartbeat measurement, ECG measurement, blood pressure monitoring, etc.).

Portable wellbeing (applications) has quickly multiplied, yet their capacity to improve results for patients stays indistinct. An approved apparatus that tends to applications conceivably significant measurements has not been accessible to patients and clinicians. This venture was to create and start probing a usable, legitimate and open source evaluation apparatus to impartially assess the dangers and benefits of wellness applications. Regardless of this expansion, barely any wellbeing applications have been appeared to accomplish what is seemingly their most significant objective: to improve tolerant results. Numerous applications have all the earmarks of being centered on moderately sound patients, with numerous less being centered on significant expense, high-need patients, or patients with ongoing infections. All things considered, most applications are utilized for brief timeframes and afterward dropped. This is hazardous particularly for patients with constant illnesses who may profit by a more drawn out term insight. We realize that far reaching longitudinal consideration bears the cost of patient’s better results, yet a longitudinal relationship with an application isn't the standard. For example, when present, there is a significant reduction in hemoglobin A1c in type 1 diabetes. Although short-term use may be beneficial, for example, for patients passing and colonoscopy that have colonic prep in the direction of the application, we have not put the disease in a weak state given its large community size and open space for development.

The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.

Heterogeneous energy and traffic-aware (HETA), an efficient traffic scheduling algorithm is proposed for forecasting the data traffic and the time-sensitive data process. In this structure, the time of non-intermittent critical traffic delays and the time slots are allocated for the intermittent data traffic. Also, the central sleep scheduling algorithm is used to offer an efficient time scheduling mechanism for the priority and time intermittent data towards the central Base Station. In this paper, the queuing delay against the traffic near the Base Station and the processing delay has been considered. The analysis of end-to-end delay by performing well in simulation study through improved efficiency in the performance of the HETA algorithm 90% more in lifetime of the Network with consistent of 3.7 times increase in throughput.

This paper aims to build a smart lighting system with applications such as remote for controlling power supply and optimizing heat management in the metal body of the semiconductor diode and with a printed circuit board for agriculture. The semiconductor diode strips with multiple colors are lined up and configured as a LED lamp with proper casing and heat sink. It has a driver circuit with required power regulation that is able to control the intensity of light for photosynthesis and plant growth requirements. The system uses hydroponics to plant the water, thus decreasing the usage of fertilizers. The entire system is controlled remotely using necessary communication interface application.

Cancer is the world second most common syndrome which causes death. In statistics nearly one of every four death occurs day-to-day, but it is remediable if detected earlier. The numerous imaging technologies exist for diagnosis with different constrains but those techniques provide either macroscopic or microscopic imaging. Histopathology of biopsy samples uses microscopic imaging methods to provide corporeal and functional information. In macroscopic level X-ray and MRI are used to provide images of living tissues and can achieve only much poorer resolution and specificity. Moreover it is harmful in terms of radiation and other causes To syndicate the macroscopic and microscopic imaging progresses, for delineating the precise margins of cancers is one of the prime mysterious complication in medical imaging.The THztomography research does that syndication and achieves much high resolution with Non-ionizing radiation. The proposed nanomaterial based Microstrip antenna is pertinent to do THztomography with low cost and high resolution which can diagnosis and detection various cancers such as skin cancer, breast cancer, cervical cancer and colon cancer. In this paper, a terahertz imaging antenna was designed and analyzed using Ansys - HFSS v.14 simulation tool.

Cognitive radio is an inventive system to wireless technologies in which radios are designed with an astonishing level of intelligence and agility. This handles the available spectrum in an expedient manor to avoid spectrum scarcity. The cognitive radio antenna consists of integrated wideband and narrow band antenna in same substrate which is taxing task. A wideband antenna is pondered with a minimum bandwidth of 7.5GHz for sensing white space in the spectrum. For narrowband antenna, frequency Reconfigurable antenna is employed which is for the purpose of transmitting the data through the white space from the outcomes of UWB antenna. In this paper the novel design of cognitive radio antenna is designed with low-profile and miniaturized size using ansys HFSS V.14 and the simulation result are investigated tremendously.

The integration of mobile communications together with computer internet network is the main focus of Information technology industry. The user anticipations are swelling with regard to a huge range of services and applications with unalike point of quality of service (QoS), which is related to bandwidth, throughput, energy and delay. LAS-CDMA (Large Area Synchronized Code Division Multiple Access) covers global area, enables high-speed data transmission and increases voice capacity. MC-CDMA (MultiCarrier Code Division Multiple Access) is designed for running on wide area, called macro cell. The LMDS (Local Multipoint Distribution System) designed for micro cell, enables wireless broadband technology used to carry voice, data and video services in 25GHz. 4G network integrates all access networks resulting in overlap coverage area. This may cause network resources wasted in the overlap coverage area. Therefore, it is necessary to propose a solution for this situation through utilizing multi network radio frequencies. This paper focuses on Network Resource Management and Energy efficiency through Multi Network Data Path; considering mobility between the nodes. The Network Resource Management Algorithm is designed using bandwidth management which includes bandwidth monitoring and selection. The performance is evaluated by assigning different paths to the mobile nodes. Simulation results shows that Network Resource Management Algorithm improves the energy efficiency by 23%. This algorithm considers movement pattern of the mobile nodes which varies the buffer size by 0.1%. Keywords—Multi-network, 4G, Mobile Multimedia