A critical design blind spot persists in contemporary electric vehicle (EV) safety engineering: ISO 26262 functional-safety frameworks generally treat battery State-of-Health (SoH) as a passive maintenance metric rather than a live, mode-governing safety variable. This paper proposes a five-layer theoretical co-design framework that integrates SoH estimation, power-margin monitoring, functional-safety decision logic, actuation, and lifecycle management. A qualitative Fault Tree Analysis and preliminary Hazard Analysis and Risk Assessment are used to examine the representative BMS–ADAS power-interface hazard. The electrical analysis introduces a bounded DC/DC converter model and distinguishes two explicit converter-control scenarios: regulated operation with no active hard limit and an adverse 5.15 A hard-input-current-limit boundary condition. For the stated 851 W transient load, both scenarios retain positive converter margin; failed shedding of a 551 W comfort load is therefore the dominant source of power-margin compression, while the isolated SoH contribution is 0 W under regulated operation and approximately 40 W under the adverse hard-limit condition. A separate illustrative LiDAR subsystem-allocation model yields a beta-derived additional stopping-distance estimate of approximately 0.84–1.40 m, while an independently defined 100 ms recovery-delay scenario yields 1.67 m. These estimates are analytical scenario results rather than device-validated predictions. Platform-specific converter, power-allocation, sensor, timing, and hardware-in-the-loop validation is identified as the essential next steps.

Accurate multi-day streamflow forecasts support flood preparedness, but single holdout scores can conceal lead-time dependence, temporal instability, baseline skill, and uncertainty. This study compares Long Short-Term Memory, Random Forest, Multiple Linear Regression, Multi-Layer Perceptron, Support Vector Regression, and AdaBoost using an audited Bangladesh Water Development Board discharge record for the Bahadurabad Transit station on the Brahmaputra River. The source contained 10,314 entries from 1995 to 2023. After 275 excess same-date records were consolidated by date-wise averaging, 553 calendar days were missing; no temporal interpolation was applied. We constructed 9,272 complete samples using 30 antecedent discharges to predict Days 1–30 directly. Leakage-resistant evaluation used three expanding rolling-origin folds, target-end-date training cut-offs, a 2019–2020 calibration period, and an independent 2021–2023 test period. Moving-block bootstrap confidence intervals, split-conformal prediction intervals, flow-regime errors, and lag sensitivity were evaluated. Random Forest had the lowest mean rolling-origin RMSE, whereas Multi-Layer Perceptron had the lowest pooled test RMSE (6,763.47 m³/s; R²/NSE = 0.721); their uncertainty intervals overlapped. Ranking changed with horizon: Multiple Linear Regression was best at Day 1 and AdaBoost at Day 30. All models underestimated the highest-flow decile. The results support an uncertainty-aware discharge-only benchmark, not universal model superiority or operational flood-warning readiness.

In this study, we have compared manual machine learning with automated machine learning (AutoML) to see which performs better in predictive analysis. Using data from past football matches, we tested a range of algorithms to forecast game outcomes. By exploring the data, we discovered patterns and team correlations, then cleaned and prepped the data to ensure the models had the best possible inputs. Our findings show that AutoML, especially when using logistic regression can outperform manual methods in prediction accuracy. The big advantage of AutoML is that it automates the tricky parts, like data cleaning, feature selection, and tuning model parameters, saving time and effort compared to manual approaches, which require more expertise to achieve similar results. This research highlights how AutoML can make predictive analysis easier and more accurate, providing useful insights for many fields. Future work could explore using different data types and applying these techniques to other areas to show how adaptable and powerful machine learning can be.

Agricultural production contributes significantly to the country’s economy. Bangladesh is an agriculturally dominant country and 70% of people depend on agriculture for their livelihood. But nowadays, urban people are far away from agriculture due to lack of space and time. Soil moisture content in land and rooftop gardens is influenced by natural factors like rainfall and groundwater, but rooftop gardens often rely on manual watering due to limited access to natural water sources, and their water filtration processes vary based on soil types, textures, and pores. “Botanika” such an IoT enabled remote sensing system which presents a comprehensive solution for monitoring soil parameters in rooftop gardens. Internet of Things (IoT) is an advanced technology for monitoring and controlling device anywhere in the world. Through in-situ calibration by an in-house soil profiling probe, it ensures accurate and reliable data collection, optimal plant growth. The accompanying android app enables users to effortlessly monitor soil moisture levels remotely. By integrating sensors and advanced technology, it offers real-time insights into crucial factors such as soil moisture, soil temperature, temperature, humidity, and rainfall. This innovative solution enhances crop health and productivity in urban agriculture. With its user- friendly interface, Botanika empowers gardeners to efficiently manage their rooftop gardens with precision. Embracing sustainability, this IoT-enabled soil profiling probe promotes green spaces utilization and food production on rooftops, contributing to a more resilient and eco-friendly urban environment.

The study offers a thorough examination of the accumulation and distribution of wealth among billionaires through the application of big data analytics methodologies. This research centres on an extensive dataset known as "Billionaires.csv," which encompasses a range of information about billionaires from diverse nations, including their demographic characteristics, company particulars, sources of wealth, and more details. The study aims to get a deeper understanding of the determinants that change the net worth of billionaires and detect trends in the worldwide financial system that can guide entrepreneurial ventures and investment possibilities. The dataset is subjected to analysis and visualisation through the utilisation of Python tools and libraries, including but not limited to Pandas, NumPy, Matplotlib, and Seaborn. The results of this study offer valuable insights into the distribution of wealth among billionaires, the factors that contribute to industry success, gender disparities, age demographics, and other factors that influence the accumulation of billionaire wealth.

Sound classification is a multifaceted task that necessitates the gathering and processing of vast quantities of data, as well as the construction of machine learning models that can accurately distinguish between various sounds. In our project, we implemented a novel methodology for classifying both musical instruments and environmental sounds, utilizing convolutional and recurrent neural networks. We used the Mel Frequency Cepstral Coefficient (MFCC) method to extract features from audio, which emulates the human auditory system and produces highly distinct features. Knowing how important data processing is, we implemented distinctive approaches, including a range of data augmentation and cleaning techniques, to achieve an optimized solution. The outcomes were noteworthy, as both the convolutional and recurrent neural network models achieved a commendable level of accuracy. As machine learning and deep learning continue to revolutionize image classification, it is high time to explore the development of adaptable models for audio classification. Despite the challenges associated with a small dataset, we successfully crafted our models using convolutional and recurrent neural networks. Overall, our strategy for sound classification bears significant implications for diverse domains, encompassing speech recognition, music production, and healthcare. We hold the belief that with further research and progress, our work can pave the way for breakthroughs in audio data classification and analysis.

AbstractThis paper presents an investigation on a battery-less voltage of Piezoelectric (PZT) V-shape cantilever beam Energy Harvester (EH) using human body vibration. The frequency ranges are walking (0–5 Hz), running (6–10 Hz) and motions (11–15 Hz) for human movement. Pacemaker devices typically require a lower resonant frequency with higher voltage which is powered by batteries. The battery has a limited duration during its working process and the battery is difficult to replace in the human body. To address the aforementioned issue, a V-shape cantilever beam EH has been developed as a solution to overcome these limitations. The cantilever beam was designed in COMSOL Multiphysics software 5.5 version using the Finite Element Analysis (FEA) method for experimental investigations followed by three categories of frequency ranges of the human body. The simulation results showed that the generated battery-less higher voltage was 269 mV (AC) at the resonant frequency of 14.37 Hz in the motion range of 11–15 Hz. Later, an Ultra Low Power (ULP) electronic circuits will be designed and simulated in the LTSPICE software to convert and boost-up from 269 mV (AC) to DC voltage attained. The estimated output power of the energy harvester system can be powered up (4.7 µW) for modern pacemaker applications.

The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply.

The technology grows quickly in the area of the VLSI physical design; it is crucial to integrate the greater number of transistors and parts into a very small range. Before the placement is completed, the physical and technical positioning of the blocks in the chip area is planned, which is nothing but floor planning. In order to lessen the placement region in the physical layout, floor planning must be carried out effectively. This paper proposes a blended harmony search and particle swarm optimization (BHSPS) algorithm which is the deliberate blend of the harmony search (HS) algorithm, and the particle swarm optimization (PSO) algorithm is proposed to acquire the central goal of the VLSI placement strategy. The objective here is to lessen the field of plan. The MATLAB code for the blended harmony search and particle swarm optimization (BHSPS) algorithm is compiled, and investigations were carried out for better examination through the standard MCNC, i.e., North Carolina Microelectronics Center benchmark circuits.

The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as "asymptomatic" COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the "recall" metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.

In this paper, an autonomous brain tumor segmentation and detection model is developed utilizing a convolutional neural network technique that included a local binary pattern and a multilayered support vector machine. The detection and classification of brain tumors are a key feature in order to aid physicians; an intelligent system must be designed with less manual work and more automated operations in mind. The collected images are then processed using image filtering techniques, followed by image intensity normalization, before proceeding to the patch extraction stage, which results in patch extracted images. During feature extraction, the RGB image is converted to a binary image by grayscale conversion via the colormap process, and this process is then completed by the local binary pattern (LBP). To extract feature information, a convolutional network can be utilized, while to detect objects, a multilayered support vector machine (ML-SVM) can be employed. CNN is a popular deep learning algorithm that is utilized in a wide variety of engineering applications. Finally, the classification approach used in this work aids in determining the presence or absence of a brain tumor. To conduct the comparison, the entire work is tested against existing procedures and the proposed approach using critical metrics such as dice similarity coefficient (DSC), Jaccard similarity index (JSI), sensitivity (SE), accuracy (ACC), specificity (SP), and precision (PR).

The burst dropping ratio is witnessed in the contemporary literature as a considerable constraint of optical burst switching (OBS) networks that attained many researchers’ efforts in the recent past. Among the multiple practices endeavoring to reduce the burst drop ratio, the optimal burst scheduling is one dimension in this regard. The transmission channel scheduling and appropriate wavelength allocation are critical objectives to achieve optimal burst scheduling in regard to minimal burst drop ratio. Many of the scheduling models depicted in the contemporary literature aimed to achieve the optimum scheduling by electing the channels, which depend on optimum utilization of idle time. Some of the studies tried to select channels by any metrics of quality, and significantly minimal amount of studies focused on wavelength allocation for lowering BDR. Moreover, in regard to this, this study tried to achieve optimum wavelength allocation beneath manifold objective QoS metrics, which is identified as “multiobjective dynamic wavelength scheduling (DyWaS).” The experimental study carried through the simulations evinced that the proposed model DyWaS escalated the optimality of burst scheduling through wavelength allocation compared with other existing methods represented in the contemporary literature.

The paper investigates a naturally motivated meaning of wise shrewd self-ruling specialists of humans. Knowledge is identified with whether the conduct of a framework adds to its self-upkeep. Conduct turns out to be clearer (or adapts to more biological issue factors) when it is able to make and utilize portrayals. The thought of portrayal ought not to be confined to formal articulations with neuro hypothetical semantics. The element at different degrees of canny frameworks assumes a fundamental part in shaping portrayals. The paper investigates an organically roused meaning of shrewd self-ruling specialists of humans. Insight is identified with whether the conduct of a framework adds to its self-upkeep. Conduct turns out to be keener when it is proficient to make and utilize portrayals. The idea of portrayal also focused on formal articulations with sentimental hypothetical semantics.

Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops.

The burst dropping ratio is witnessed in the contemporary literature as a considerable constraint of optical burst switching (OBS) networks that attained many researchers’ efforts in the recent past. Among the multiple practices endeavoring to reduce the burst drop ratio, the optimal burst scheduling is one dimension in this regard. The transmission channel scheduling and appropriate wavelength allocation are critical objectives to achieve optimal burst scheduling in regard to minimal burst drop ratio. Many of the scheduling models depicted in the contemporary literature aimed to achieve the optimum scheduling by electing the channels, which depend on optimum utilization of idle time. Some of the studies tried to select channels by any metrics of quality, and significantly minimal amount of studies focused on wavelength allocation for lowering BDR. Moreover, in regard to this, this study tried to achieve optimum wavelength allocation beneath manifold objective QoS metrics, which is identified as “multiobjective dynamic wavelength scheduling (DyWaS).” The experimental study carried through the simulations evinced that the proposed model DyWaS escalated the optimality of burst scheduling through wavelength allocation compared with other existing methods represented in the contemporary literature.

Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as ‘Cascaded Object Detector’ (COD) and the other is a composition of traditional customised methods, individually known as ‘Colour Transformation’: ‘Colour Segmentation’ and ‘Circular Hough Transformation’. The (Viola‐Jones) COD generates ‘histogram of oriented gradient’ (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro‐controller based miniature electronic devices in terms of its run time complexity of O(n3) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.

Because of the increased popularity and fast expansion of the Internet as well as Internet of things, networks are growing rapidly in every corner of the society. As a result, huge amount of data is travelling across the computer networks that lead to the vulnerability of data integrity, confidentiality and reliability. So, network security is a burning issue to keep the integrity of systems and data. The traditional security guards such as firewalls with access control lists are not anymore enough to secure systems. To address the drawbacks of traditional Intrusion Detection Systems (IDSs), artificial intelligence and machine learning based models open up new opportunity to classify abnormal traffic as anomaly with a self-learning capability. Many supervised learning models have been adopted to detect anomaly from networks traffic. In quest to select a good learning model in terms of precision, recall, area under receiver operating curve, accuracy, F-score and model built time, this paper illustrates the performance comparison between Naive Bayes, Multilayer Perceptron, J48, Naive Bayes Tree, and Random Forest classification models. These models are trained and tested on three subsets of features derived from the original benchmark network intrusion detection dataset, NSL-KDD. The three subsets are derived by applying different attributes evaluator's algorithms. The simulation is carried out by using the WEKA data mining tool.

For estimation of signal coverage and localization, path loss is the major component for link budget of any communication system. Instead of traditional Doppler shift or Doppler spread techniques, ...

Wireless Sensor Networks (WSNs) are playing remarkable contribution in real time decision making by actuating the surroundings of environment. As a consequence, the contemporary agriculture is now using WSNs technology for better crop production, such as irrigation scheduling based on moisture level data sensed by the sensors. Since WSNs are deployed in constraints environment, the life time of sensors is very crucial for normal operation of the networks. In this regard routing protocol is a prime factor for the prolonged life time of sensors. This research focuses the performances analysis of some clustering based routing protocols to select the best routing protocol. Four algorithms are considered, namely Low Energy Adaptive Clustering Hierarchy (LEACH), Threshold Sensitive Energy Efficient sensor Network (TEEN), Stable Election Protocol (SEP) and Energy Aware Multi Hop Multi Path (EAMMH). The simulation is carried out in Matlab framework by using the mathematical models of those algorithms in heterogeneous environment. The performance metrics which are considered are stability period, network lifetime, number of dead nodes per round, number of cluster heads (CH) per round, throughput and average residual energy of node. The experimental results illustrate that TEEN provides greater stable region and lifetime than others while SEP ensures more throughput.