Cognitive-state assessment in real-time is a key part of adaptive learning, neuroergonomics, and safety-critical systems. Electroencephalography (EEG) is helpful in monitoring neural oscillations, as it allows fast monitoring of changes in attention and working memory status of neural fluctuations that can respond to changes in attention and working memory. In this paper, an interpretable EEG-based framework is proposed to use sensitive fronto-temporal and temporo-parietal measures of dynamic working-memory volumes, profile attentional functions of target regions, and a transparent fuzzy-logic mechanism with detailed case-study analysis to represent the perception of cognitive status. EEG signals from bipolar pairs F7-T3 and F8-T4 capture verbal and visuospatial working-memory activities, and T4-T6 monitors attentional engagement across multimedia and text-type activities. The dynamic weighting model generates a composite working-memory index which is integrated with the attention measure to generate a continuous cognitive state index (CSI) indicative of passive, moderate and focused engagement. Case studies depict temporal EEG behavior, working memory shifts, changes in attention and the respective CSI trajectories. This indicates clear neurophysiological patterns, with pronounced right fronto-temporal fluctuation for high cognitive load, stable temporo-parietal activity for memory maintenance and consistently lower level steady activity on the left frontal regions.

In high-pressure environments such as education, healthcare, and high-stakes workplaces, cognitive load, multitasking, and time constraints create significant challenges. Traditional methods often overlook psychological factors like emotional resilience and reasoning capacity, while failing to capture nonlinear interactions. This study proposes a two-stage hybrid model combining fuzzy logic and machine learning to analyze and predict cognitive load and working memory performance. In the first stage, fuzzy logic is used to categorize cognitive load based on electroencephalography-derived metrics of emotional resilience and thinking capacity through membership functions and rule-based inference. In the second stage, the predicted cognitive load is integrated with the performance of working memory to assess its impact on learning outcomes and mental health. Analysis shows that when both emotional resilience and thinking ability are high, the cognitive load is also high, aligning with theoretical expectations. Machine learning models, including Support Vector Machine, Naive Bayes, Random Forest, and Decision Trees, validate the framework, demonstrating superior classification of cognitive load and enhanced prediction of working memory performance compared to traditional linear approaches. By bridging the gap between cognitive load dynamics and mental health, this framework provides actionable insights to optimize performance and well-being in demanding settings. The findings underscore the importance of integrating fuzzy logic and machine learning to address complex cognitive challenges in high-demand sectors.

Mental Stress has evolved as a major health issue that has negative impacts on humans mentally and physically. It causes significant physiological and psychological changes in the human body. Therefore, continuous observation of mental stress is highly essential for a human which increases the productivity of humans with sound health. A machine learning-based predictive model using Decision Tree (J48) algorithm in cross-validation mode has been proposed to detect the mental stress of any human with respect to different positions of the body and the behaviour of the person. Cost-benefit analysis approach has been proposed and implemented with the machine learning-based predictive model to enhance the accuracy of the model. Due to this, the accuracy of the model has been increased from 80 to 99.67

The security and privacy of data are major concerns in the mobile crowdsensing (MCS) environment due to the huge amount of heterogeneous data received from various users and devices automatically or manually regarding their surrounding environment. User participation in the MCS approach is highly essential to have a vast dataset for analysis that will provide the required information or beneficial solution for society. However, it is difficult to achieve due to huge energy consumption, the need for internet connectivity for data transmission, and the security and privacy of data. Therefore, it is essential to have a network coverage model in which data transmission can be done with minimal energy consumption and the need for internet connectivity can be removed from the user’s side. The user’s sensitive data needs to be protected from internal and external attackers to improve the efficiency of the solution provided by the MCS environment with genuine data. This work is based on data collection from users based on their experience for a certain location using the hybrid network coverage model based on clustering, in which each location may have just one or multiple heterogeneous cluster heads. Discrete event-based CrowdSenSim Simulator has been used to design a simulation environment in urban spaces in which 2000 users will move to any location randomly among considered 40 locations and provide feedback data for the location. In this paper, a novel security mechanism based on multiple heterogeneous cluster heads per location has been presented, and it provides better security against attackers than the security model with one cluster head per location. The proposed multiple-cluster heads per location (MCHL)-based mechanism has been compared with the vulnerable one-cluster head per location (OCHL)-based mechanism on the basis of the average number of rounds attackers attacked, average number of locations attackers attacked, average coverage and average efficiency of attackers, and average efficiency of system security.

The content-based order of urban sound classes is an important aspect of several emerging techniques and applications as a result, the research issue has gotten a lot of attention recently. The goal of this work is to develop an effective Machine Learning(ML) based approach for urban sound categorization in cities for intelligent object detection and recognition through sound for environmental study. Audio samples from urban space has been classified through various ML algorithms based on Mel spectrogram technique. Where, mel spectrogram techniques is a frequency based approach for extraction of features from audio samples for classification. The Urban Sound dataset, which contains a total of 8732 number od sound samples belongs to ten different classes, has been used in this work. This may be used for smart object detection through sound coming from objects in different applications, such as Traffic Management, Agriculture, Smart city, etc.

Stress is vital in assessing the physical and mental state of the human body with significant psychological and physiological changes. A proper and timely diagnosis of stress may make one healthier, happier, and more productive. In the workplace, undergoing many changes leads to stress, trauma, and anxiety. At the same time, hormonal changes in the human body due to stress can be reflected in terms of psychological and physiological changes. This paper has identified three different activities (normal, tension, and exercise) with varied positions (laying, sitting, and standing). Airflow, Temperature, and Galvanic Skin Response (GSR) are different sensors that sense data. This work has emphasized GSR sensors and conceptually connected them with other sensors. GSR values differ regarding the contact surface area with the body. Different machine learning algorithms such as; Naive Bayes, Support Vector Machine, Decision Tree (J48), and Random Forest have been used to analyze sensed datasets. Random Forest Algorithm has been observed to perform better in the proposed work.

A smart wheelchair provides mobility assistance to persons with motor disabilities by processing sensory inputs from the person. This involves accurately collecting inputs from the user during various movement activities and using them to determine their intended motion. These smart wheelchairs work by collecting brain signals in the form of electroencephalography (EEG) signals and by processing them into a quantized format to provide movement assistance to people. Such systems can be referred to as brain–computer interface (BCI) systems that work with EEG signals. Acquiring data from human beings in the form of brain signals through EEG, along with processing of those signals and ensuring the correctness of actions instigated by those brain signals involve a huge amount of data. In this work, we carried out an experiment by taking 100 human subjects and recording their brain signals using a NeuroMax device. Typical wheelchairs are constrained by design as the motion of those is limited either by manual operation or controlled by haptic sensors and actuators. The main objective in this work was to design a wheelchair with better usability and control using machine learning-based knowledge, which is typically a data-driven approach. However, the proposed approach was designed to take inputs from human gestures and brain sensory activities to provide better usability to the wheelchair. The attention meditation cost–benefit analysis (AMCBA) proposed in this paper aims to reduce the risk of inappropriate results and improve performance by considering various cost–benefit parameters. The said classifier aims to improve the quality of emotion recognition by filtering features from EEG signals using methods of feature selection. The operation of the proposed method is described in two steps: in the first step, we assign weights to different channels for the extraction of spatial and temporal information from human behavior. The second step presents the cost–benefit model to improve the accuracy to help in decision-making. Moreover, we tried to assess the performance of the wheelchair for various assumptions and technical specifications. Finally, this study achieves improved performance in the most difficult circumstances to provide a better experience to persons with immobility.

A smart wheelchair can be perceived as a wheelchair that can provide autonomous mobility and features to persons who are incapable of using their own body for self-mobility. There are broadly two categories of smart wheelchairs. In the first category, there are provisions for the usage of wheelchairs using in-built functions built into the wheelchair. In the second category, the smart wheelchair can use the signal obtained from human brain using Brain–Computer Interface (BCI), for the movement of human being who are incapable of using their own body for the various functions of a wheelchair. It has been substantial research on providing accuracy for the movement of smart wheelchairs. In this regard, there can be significant differences between the accuracy received from the user-supplied dataset and from the operational aspect of the wheelchair when put to the real operational environment. We have hence tried to improve the accuracy of our model by incorporating a cross-validation approach to our model. Cross-validation is a statistical method for evaluation of machine learning methods by segregating two different sections where the first section is used to develop and learn a model using training dataset. Further, it has been validated using testing dataset. Finally, test the stability of our machine learning model with real-time dataset.

Emotions play a significant part in a person's social connections, decision-making, and perception of the world. Elicited emotions cause a change in a person's physiological and psychological states. As Electroencephalography (EEG) facilitates a close study of brain activity, it is becoming a standard method among the research community for reliable recognition of human emotions. This work demonstrates various advancements in emotion recognition utilizing EEG signals and points out major changing trends by making a comparison of previously available research in this field. In addition to the survey a detailed explanation of the procedure for refining EEG for emotion recognition has been explained in this work. This aims to help researchers, especially beginners, have a thorough understanding of the developmental research in this field.

Cloud computing has been of greater convenience in recent years owing to its flexibility and on-demand service availability. Cloud computing services allow multiple users to access the available resources concurrently. So, trust is a key parameter from every client’s point of view. While accessing the resources in the cloud security becomes the primary concern for every participant and cryptography provides a secure room for every client. The cryptographic solution can be achieved in two different ways such as the conventional mechanism and the homomorphic encryption (HE) mechanism. The main objective of these two solutions is to maintain the confidentiality, integrity, and availability (CIA) of the information and resources stored in the cloud storage. The conventional mechanism provides a secure means to encrypt and decrypt the information at the sender and receiver sides respectively. But for any kind of operation, the information needs to be decrypted which increases the computational overhead which shows the path to the HE. In HE, the user can directly perform any kind of operation on the encrypted data which can help to reduce the computational time and cost to a much more significant value. In this current research work, the main focus is on the HE. Several HE mechanisms such as the RSA, Elgamal, Paillier, and DGHV have been implemented and the performance has been analyzed in terms of computational time. The empirical analysis demonstrates that the RSA homomorphic algorithm shows an average encryption and decryption time of 138.5 and 206.25 s which is lower as compared to others. Considering average CPU utilization, RSA shows around 27% with an average of 30 MB memory utilization which is found lower as compared to other algorithms.

Mobile Crowdsensing (MCS) is a major source of a vast dataset containing heterogeneous types of data collected from various sources and stored in the local or remote server.Proper analysis of MCS data helps in better decision-making.However, MCS data suffers from data integrity issues, such as validity, accuracy, and reliability, that affect decision-making.Therefore, ensuring data integrity in the MCS environment is essential as it is a major source of a huge dataset.The proposed work considers user review data collection and analysis using a mobile application developed for the purpose.To ensure the data integrity, identification of fake and invalid reviews in the dataset need to be determined.This work proposes two approaches to solve data integrity issues.The first approach is to detect and eliminate fake/ invalid reviews from the dataset.The second is to identify the sources of fake/ invalid reviews and block them to protect the dataset from future fake reviews.Machine learning (ML) models are proposed to solve these issues and to ensure data integrity by filtering out fake reviews from real-time data sets.The proposed model uses data fuzzification over a purely mathematical model that categorizes users or customers as honest, suspicious, or malicious and their reviews/ feedback as genuine or fake using ratings provided by the user in the MCS Environment.Using the developed mobile application, user can give feedback about the desired location through various devices, which is stored in a cloud platform.The dataset can be analyzed through a fuzzy logic-based mathematical model followed by an ML algorithm and cost-benefit analysis to detect genuine reviews for maintaining data integrity.Further accuracy of the proposed models is compared with popular ML algorithms such as Naive Bayes (NB), Bayes Net(BN), Support Vector Machine(SVM), Decision Tree(J48), and Random Forest(RF).Initially, it achieves 99.79% of accuracy using the Random Forest algorithm that has been enhanced to 100% using cost-benefit analysis in cross-validation mode.

Next generation 5G network provides solution for rigorous demand for data with increasing network speeds. Now-a-days cell free 5G network can resolve many issues such as interference that appear in cellular configuration. Massive MIMO with cell free configuration is one of the solutions for massive-MIMO with conventional cellular network. This is also called cell free (CF) MIMO in massive. A CF Massive-MIMO system consists of number of access points (APs) which are distributed uniformly. It serves few numbers of user equipment at same frequency or time resources. It is based on characteristics of channel which is measured directly. Eachuser and APs possess only single antenna. The channel state information is acquired by APs through time division duplex(TDD)operation. Users transmits uplink pilot signals. The Multiplexing/de-multiplexing performed by APs through matched filtering during uplink and conjugate beamforming during downlink. The simplified expressions for uplink and downlink throughputs of single user would lead to maximum/minimum power control algorithms. The objective is to focus a comparative analysis on optimized cell free network which has maximum the coverage area, and a minimum transmission power. The interference problem can be resolved in operation of cell free network and it can resolve by using cell free network that appear normally in cellular network. The major challenge is to achieve the benefits of cell-free configuration that it can scalable to large network with increase in number of users. A frame work could arrive for scalable massive MIMO in cell free system by using the concept of dynamic cooperation cluster (DCC). Several algorithms are used for jointly initial access to appoints master AP, assignment of pilot signals to invite other APs, and cluster formation. It has been proved that it can be scalable. Also, the standard channel estimation, different precoding methods, and combining methods have been adopted for making the system scalable and robust.

Study of the stress level in the human body is vital now a days. It is very important to assess the mental state of the human being with significant physiological changes. Proper and on time diagnose of the stress and anxiety may make one’s lifestyle happier, healthier, and more productive. Persons, when stay and work far from their places; undergone many types of life changes and become the victim of stress, trauma, and anxiety. Hormonal changes in the human body due to stress can be reflected in terms of physiological and psychological changes. It becomes more significant to address such situations at remote places by analysing physiological data and send the same data through heterogeneous wireless communication for further analysis. In this paper, it has been identified three different activities with varied positions and sending of galvanic sensing response sensed data to the intended sink node through the heterogeneous wireless communication medium. Galvanic sensing response sensed data are different in respect to the contact surface area with the body, body position, environment, and activities. Proper investigation of sensed data can give real time solution.

In the modern world, industries rely on the feedback/reviews of the users for estimating their future plan for better customer care services and customer relationship management. The evaluations and follow up achievement can be computed as +ve/−ve types of feedback or review. This work attempts to present a model using fuzzy logic over mathematical model that will outperform the categorisation of customers or users feedback using ratings given by users or customers to ensure data integrity in mobile crowdsensing environment. In this work, customers or users can provide feedback or review for the location using web-based applications or Android Application, that will be stored in a cloud environment. This data-set will be analysed using fuzzy logic to isolate genuine reviews to maintain data integrity which may be used for different types of real-time applications such as tourism, medical, educations, among a few other applications and also categorise the customers or users as honest, malicious and suspicious.

Cloud computing allows customers to use a variety of computing resources on-demand and with no maintenance overhead. One of the major issue concerning cloud computing is security. From the end user's perception, migrating to cloud exposes them to additional security risks that are entirely considered to be produced by other occupants who may have some access to shared resources. The co-location or co-residence attack, otherwise called as co-resident assault, is the focus of this research. This is a type of attack in which malevolent individuals construct side channels and steal confidential information from VMs that share the same server. Here we have studied on the co-resident attacks and the mechanisms to detect and prevent the attack. To address this issue, we have focused on the PSSF VM allocation policy as PSSF policy has high security with low energy consumption.