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Mobile ad hoc networks (MANETs) facilitate data communication across multiple nodes and hop stations, characterized by their dynamic topology. This inherent flexibility, however, makes MANETs vulnerable to various security threats, notably blackhole and wormhole attacks, where malicious nodes can intercept and manipulate data. This study investigates the security vulnerabilities of MANETs, particularly against blackhole, Sybil, and wormhole attacks, and introduces the Advanced Blockchain Dynamic Source Routing (ABCD) algorithm to address these challenges. Motivated by the need for robust and decentralized security solutions in MANETs, the proposed algorithm integrates blockchain technology and homomorphic encryption to secure data communication without intermediate decryption. The ABCD algorithm leverages Dijkstra’s algorithm for optimal routing and employs a tamper-proof, decentralized data storage approach. Comparative analysis under attack scenarios reveals that the ABCD algorithm outperforms the standard DSR protocol across multiple quality of service metrics, demonstrating a significant improvement in MANET security over equivalent studies. The packet delivery rate is also improved from 81 to 92% using the modified ABCD algorithm.

Some features of left and right brained people can be determined using the left and right brain dominance theories. It can assist in the development of training syllabus for brain-balancing education topics. When performing any action, human focus or concentration is essential. This paper will examine the concentration levels of patients with left and right brain dominance using electroencephalogram (EEG) data. Brain activity may be tracked and recorded using EEG waves. The human brain’s thinking and attention cause brain waves to alter in distinct frequency bands. A frequency-based EEG signal can be cleaned up and characteristics can be extracted using the baseline correction method. As a result, the EEG Topographical Power Spectral Density Value is created. The main objective of this paper is to compare the concentration levels of people with different brain dominance. Inversely, EEG signal can be used to predict whether a person has a left or a right brain dominance.

Automated plant disease detection has become an essential application of deep learning, supporting early diagnosis and effective crops and ornamental plant management. Recent advancements in the You Only Look Once (YOLO) family of object detection models have improved both accuracy and efficiency, making them suitable for real-time deployment. This paper presents a comparative analysis of YOLOv8, YOLOv9, and YOLOv11 for classifying diseased and healthy leaves of Ixora and Bougainvillea, two widely grown ornamental species. A curated dataset of annotated leaf images covering multiple disease conditions was used to train and evaluate the models under consistent experimental settings. To capture both accuracy and real-time feasibility, performance was evaluated using standard detection metrics like mean Average Precision (mAP), precision, recall, and F1-score in addition to inference speed (FPS). The assessment also highlights environmental robustness and subtle disease localization parameters, which are important for monitoring ornamental plants in unrestricted outdoor environments. Results indicate that YOLOv11 achieves the highest detection accuracy, especially in capturing subtle disease patterns, while YOLOv8 and YOLOv9 demonstrate competitive performance with faster inference, making them preferable for resource-limited applications. The findings highlight practical trade-offs between accuracy and efficiency across YOLO versions, offering valuable insights for real-world deployment. By extending research beyond staple crops to ornamental plants, this work underscores the broader applicability of AI-driven disease detection and establishes a benchmark for evaluating next-generation YOLO architectures in horticulture.

The prevailing educational methods predominantly rely on traditional classroom instruction or online delivery, often limiting the teachers’ ability to engage effectively with all the students simultaneously. A more intrinsic method of evaluating student attentiveness during lectures can enable the educators to tailor the course materials and their teaching styles in order to better meet the students' needs. The aim of this paper is to enhance teaching quality in real time, thereby fostering a higher student engagement in the classroom activities. By monitoring the students' electroencephalography (EEG) signals and employing machine learning algorithms, this study proposes a comprehensive solution for addressing this challenge. Machine learning has emerged as a powerful tool for simplifying the analysis of complex variables, enabling the effective assessment of the students' concentration levels based on specific parameters. However, the real-time impact of machine learning models necessitates a careful consideration as their deployment is concerned. This study proposes a machine learning-based approach for predicting the level of students' comprehension with regard to a certain topic. A browser interface was introduced that accesses the values of the system's parameters to determine a student's level of concentration on a chosen topic. The deployment of the proposed system made it necessary to address the real-time challenges faced by the students, consider the system's cost, and establish trust in its efficacy. This paper presents the efforts made for approaching this pertinent issue through the implementation of innovative technologies and provides a framework for addressing key considerations for future research directions.

In recent years, radio frequency (RF) energy harvesting (EH) has gained prominence in wireless networks. Both data transmission and powering energy-constrained network nodes are possible using RF. Fading is one of the many issues that prevent wireless networks from transmitting signals effectively. Data is sent from the source to the destination with fewer hops when cooperative communication is used. This thesis considers a twin-hop relay-aided network with wireless energy harvesting. The source's RF energy is captured by the relays and used for data transmission. Using the results from the simulations and the Distributed Switch and Stay Combining (DSSC) combining technique at the receiver, the performance of the proposed relay selection that is Two Round Relay Selection (2RRS) network in terms of outage probability, throughput, and spectral efficiency are analysed.

This study describes a new method for gripping and sensing a physical object (or material) with a prosthetic arm that uses haptic technologies, kinaesthetic communication, and machine learning. Haptic technology is a method of determining if an object is firm or soft as if it were gripped by a human and determining how much gripping force the object can withstand without crushing it. The bending moment and gripping force are measured using a flex sensor in human fingers and a pressure sensor applied by the tip of the human fingers. Three different types of objects (soft sponge, hard sponge, and plastic) are studied and tested in this work by pressing them with varying gripping pressures (soft, firm, and firmer). In addition, a model (Haptic Intelligence Recorder arm) is proposed that can anticipate the object type and gripping force based on the recorded intelligence data. The major goal is to educate our prosthetic hand to be able to grip various items with varying finger pressures, much like we can do naturally. Finally, a glove is created that is tailored to the intelligence arm’s ability to anticipate grabbing items.

To improve mental health and wellness and create specific solutions, it is essential to comprehend how individuals feel and brain functions. In this study, we present a novel approach for emotion recognition and analysing electroencephalography (EEG) data for cognitive evaluation. EEG data were collected from 30 participants using non-invasive electrodes positioned at AF3, AF4, T7, T8, and Pz, corresponding to the frontal, temporal, and parietal lobes.We have obtained real-time EEG data from participantes during various tasks, including as rest, listening to music, answering questions, and completing mathematical puzzles. Our goal was to investigate the brain correlates of different emotional and cognitive states. The recorded signals were pre-processed using a 4–8 Hz bandpass filter targeting theta waves, followed by Fast Fourier Transform (FFT) and sequence pattern mapping. Statistical significance of variations between brain states was confirmed using ANOVA (p < 0.05). A supervised machine learning classifier (Random Forest) achieved 89.2% prediction accuracy, with precision = 0.87, recall = 0.90, and F1-score = 0.885, demonstrating robust differentiation between emotional and cognitive states. We have developed prediction models for emotion recognition and cognitive assessment using linear regression classification based on EEG features extracted from multiple brain areas. Using statistical analysis and graphical representation techniques, the EEG data was visualised and analysed, revealing a variety of patterns associated with different tasks and stimuli. Our study demonstrates that emotional states and cognitive activity may be accurately identified from EEG signals. More specifically, we observed significant differences in EEG patterns between tasks, suggesting that real-time tracking of human emotions and mental processes can be achieved with EEG-based techniques. Applications in human-computer interaction, mental health monitoring, and tailored interventions to improve well-being are possible with the suggested methodology. 

Self-inoculation takes major part for the transmission of infections. Basically self-inoculation is nothing but a type of contact transmission. A person’s contaminated hands make contact with other parts of the body by Self-inoculation. Many infections mainly respiratory infections (e.g., influenza, coronavirus) can transmit via self-inoculation. In this pandemic situation it is very necessary to resist the transmission of infection (corona virus) via self-inoculation. In this paper a module of personal protective intelligent hat has been proposed for the protection of human being. The protective intelligent hat fabricates with two ends (i) Face Side End System (FSES) and (ii) Hand Side End System (HSES). FSES is made up with hall sensor whereas HSES is set up with magnet set ring or band. The novel corona virus enters the human body by eyes, nose and mouth when all are touched by infected hand. Avoid touching of contaminated hand is one of the key to survive from the attack of corona virus as well as other bacteria. The smart hat not only applicable to protect COVID-19, it is also applicable to protect various diseases which caused by contaminated hand like Influenza, Common cold, Chicken Pox etc.

Currency notes and coins play an important role in daily needs for the human being across the world. These currency notes take a major role for spreading the Corona virus infection and circulation of the currency notes enhance some infectious disease like COVID-19. A new prototype is introducing here to prevent the problem that is spreading of corona virus by hand to hand money transfer across the world. This paper is about preventing the spreading of corona-virus through currency by developing automatic, portable alcohol-based money disinfector. The prototype disinfects both currency notes and coins by ethyl alcohol.

In this paper, a complete non-linear symmetric oscillator model using the Hamiltonian approach has been developed and used to describe the cardiovascular conduction process’s dynamics, as the signal generated from the cardiovascular muscle is non-deterministic and random. Electrocardiogram (ECG) signal is a significant factor in the cardiovascular system as most of the medical diagnoses can be well understood by observing the ECG signal’s amplitude. A non-linear cardiovascular muscle model has been proposed in this study, where a modified vanderPol symmetric oscillator-based equation is used. Gone are the days whena non-linear system had been designed using the describing function technique. It is better to design a non-linear model using the Hamiltonian dynamical equation for its high accuracy and flexibility. Varying a non-linear spring constant using this type of approach is more comfortable than the traditional describing function technique. Not only that but different initial conditions can also be taken for experimental purposes. It never affects the overall modeling. The Hamiltonian approach provides the energy of an asymmetric oscillatory system of that cardiovascular conduction system. A non-linear symmetric oscillator was initially depicted by the non-linear mass-spring (two degrees of freedom) model. The motion of an uncertain non-linear cardiovascular system has been solved considering second-order approximation, which also demonstrates the possibility of introducing spatial dimensions. Finally, the model’s natural frequency expression has also been simulated and is composed of the previously published result.