The Journal of Engineering Excellence is an international peer-reviewed, open-access journal published by NOVARA PUBLISHERS. The journal provides a scholarly platform for researchers, academicians, professionals, and industry experts to showcase innovative findings and technological developments across all major engineering domains. Aim The Journal of Engineering Excellence aims to provide a global platform for researchers, academicians, industry professionals, and practitioners to publish high-quality, original, and impactful research in diverse engineering domains. The journal seeks to promote innovation, technological advancement, and the exchange of scientific knowledge by showcasing cutting-edge developments and emerging trends in engineering. JEE is committed to supporting interdisciplinary research and fostering collaborations that contribute to sustainable technological growth. Scope The Journal of Engineering Excellence publishes original research articles, review papers, and case studies covering a broad spectrum of engineering and technology fields. The journal welcomes high-quality contributions from, but not limited to, the following areas: Electrical Engineering Mechanical Engineering Civil Engineering Computer Science and Engineering Electronics and Communication Engineering Artificial Intelligence and Machine Learning Internet of Things (IoT) Renewable and Sustainable Energy Other Emerging Engineering Fields JEE encourages interdisciplinary submissions that integrate multiple engineering domains and address real-world challenges through innovative approaches, advanced methodologies, and practical applications. The journal follows a double-blind peer-review process to ensure the highest standards of quality and academic integrity. All published articles are made available under the Creative Commons Attribution (CC BY 4.0) License, enabling unrestricted access, sharing, and distribution with proper attribution. JEE adheres to the ethical standards recommended by the Committee on Publication Ethics (COPE) and ensures long-term preservation of published content through LOCKSS and CLOCKSS archiving systems. Title: Journal of Engineering Excellence Frequency of Publication: Quarterly (January, April, July, October) ISSN: 3108-2793 Publisher Name: NOVARA PUBLISHERS Publisher Address: #605, Jasmine Block, RDB Coconut Grove, Dynamics Colony, Mayuri Nagar, Hyderabad, Telangana – 500049, India Editor-in-Chief: Dr. Kishor Kumar Gajula Copyright: Journal of Engineering Excellence Starting Year: 2025 Subject: Engineering Language: English Publication Format: Online Phone Number: +91 9030161092 Email ID: editor@jeejournal.com |editorjeejournal@gmail.com | info@novarapublishers.com Website: jeejournal.com Publisher Website: novarapublishers.com
All articles published in the Journal of Engineering Excellence (JEE) are licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, authors retain full copyright of their work while granting permission for anyone to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or author — provided that the original work is properly cited. This open-access license ensures maximum dissemination and impact of the published research by allowing free and immediate access to scholarly work. For more details, please refer to the official license page: ???? https://creativecommons.org/licenses/by/4.0/
PERFORMANCE ENHANCEMENT OF SIX-PHASE INDUCTION MOTOR DRIVES USING ADVANCED CONTROL METHODS
In order to make six-phase induction motor drives more dependable, efficient, and load-responsive than three-phase systems, this work employs sophisticated control techniques. The inherent benefits of six-phase induction motors include reduced torque ripple, harmonic distortion, fault tolerance, and power density. For these sensors to function, contemporary control systems are required. This study investigates the potential benefits of fuzzy logic, vector control, model predictive control, and direct torque control (DTC) for enhancing motor performance under different conditions. The recommended adjustments minimize losses, boost power stability, and enhance performance even in the event of a break or imbalance. Six-phase induction motor drives are effective in high-performance industries. Significant gains in torque responsiveness, speed control, and system durability are shown by simulation and experiment findings.
Six-phase induction motor, advanced control methods, vector control, direct torque control (DTC), model predictive control
Copyright (c) 2026 Journal of Engineering Excellence https://creativecommons.org/licenses/by-nc-nd/4.0
ADVANCED CONTROL STRATEGIES FOR EFFICIENCY ENHANCEMENT OF ACTIVE POWER FILTERS
In order to improve the performance of active power filters (APFs), new approaches to their management are examined in this study. To improve the dynamic performance and accuracy of Active Power Filters, this study looks into sophisticated control techniques such neural networks, fuzzy logic, and predictive control. These techniques control load imbalance, reactive power, and harmonic distortion in a range of operating conditions. The suggested designs improve relay efficiency and decrease energy loss, which increases the system's dependability. Real-time monitoring and adaptive algorithms guarantee that the process is unaffected by power disruptions. This study shows how better management can lead to electrical distribution networks with lower total harmonic distortion (THD), better power quality, and lower energy consumption.
Active Power Filters (APF), Advanced Control Strategies, Power Quality Improvement, Total Harmonic Distortion (THD), Reactive Power Compensation
LIGHTWEIGHT PRIVACY-PRESERVING AUDITING MECHANISM FOR SHARED CLOUD STORAGE
This research introduces a simple privacy-preserving auditing approach for shared cloud storage that ensures data integrity without imposing a significant computational burden or jeopardizing user privacy. By employing robust cryptographic techniques such as homomorphic authenticators and random masking, the method enables an independent auditor to verify the data's accuracy without observing the data itself. It is well-suited for use in real-world collaborative cloud environments due to its support for dynamic data operations, including alteration, insertion, and deletion. The method is capable of managing large datasets with multiple users due to its capacity to reduce computation and communication costs. In order to prevent unauthorized audits and data leaks, secure user management and access control are also included. Based on the results of the experiments, the proposed mechanism is a viable alternative for secure shared cloud storage due to its ability to integrate privacy, efficiency, and security.
Cloud Storage Security, Privacy-Preserving Auditing, Data Integrity, Third-Party Auditor (TPA), Homomorphic Authenticators, Random Masking
AGILITY IN THE CLOUD: A HYBRID MODEL FOR SCALABLE AND CONTINUOUS DEPLOYMENT
Software development underwent radical transformation in the mid-1990s, when the concept of rapid development gained traction. Depending on the needs of the client, brief iterative cycles of output are prioritized. Quick feedback and agile development were still not supported by any current technologies. Because it eliminated the need for widely dispersed demands, which can impede progress, cloud computing was the connecting piece. The primary objective of this research is to examine the relationship between cloud computing and agile software development. Software development is both practical and innovative when we look into this connection. Building a mobile app for warehouse management on Google Cloud Platform utilizing the fast prototyping process of the Dynamic Systems Development process (DSDM) exemplifies the overall points made in this article. Warehouse management doesn't typically employ agile software development methodologies.
Copyright (c) 2025 Journal of Engineering Excellence https://creativecommons.org/licenses/by-nc-nd/4.0
ADVANCED COMPOSITE WING DESIGN AND FINITE ELEMENT STRUCTURAL ANALYSIS
Advanced composite wing design and finite element structural analysis are implemented in this investigation to enhance the efficiency, durability, and utility of existing aircraft structures. Because of their higher strength-to-weight ratios, fatigue resistance, and corrosion resistance, composite materials—like carbon fiber reinforced polymers—often replace conventional metal structures. This study uses complicated wing models and Finite Element Analysis (FEA) to assess stress distribution, wing deformation, and the maximum weight that the wings can support under various wind and structural loading situations. In order to minimize a structure's weight, optimization techniques are used to improve its architecture and material usage. Dependability and security are guaranteed. The findings imply that modern composite wing designs significantly enhance an aircraft's overall performance and structural efficiency. This demonstrates their potential significance in the aviation engineering area.
LEVEL INVERTER FOR REGULATED POWER SUPPLYFROM DC GENERATOR
An inverter with five customizable parameters is typically employed to convert direct current (DC) into alternating current (AC). There are specific conditions in which the 5-level inverter/converter can only receive ±2 Vdc from the load. In order to create the voltage, which is an alternating current output, the CPU often modifies the voltage. This cause-and-effect relationship is made feasible by the method's capacity to reduce THD levels. Some things just can't be done when you combine a high frequency rate with high power and voltage. These limitations are sometimes caused by switching losses or the restrictions of the device's rate of operation. To accommodate variations in power, a five-level inverter generates an alternating current signal. A stepped waveform, which goes more gently, can be achieved by adding voltage levels that rise slowly. To do this, the inverter configuration must permit voltage increase while reducing dv/dt. An increase in the number of voltage levels results in a more stable output pattern. A more sophisticated inverter controller is required as the design hierarchy progresses due to the increased number of switches and components.
DESIGN AND ANALYSIS OF A SNUBBERLESS DC–DC CONVERTER INTEGRATED INVERTER TOPOLOGY
The specified inverter does the DC-to-DC conversion in the absence of a snubber. When it comes to DC-DC converters, a snubber circuit is an industry standard for reducing voltage transients. When a standard DC-DC converter is turned off, voltage spikes are generated across all semiconductor components. In your stead. As the number of components and losses in the converter increase, its efficiency decreases. The problem can be fixed by using a converter that can handle soft-switching and secondary modulation. Therefore, a second snubber is superfluous. The zero-voltage switching (ZVS) and zero-current switching (ZCS) are employed in this configuration. In order to initiate and terminate the converter's operation, respectively, ZVS and ZCS use zero current circumstances. Our recommendation is that you use a voltage converter that has a high step-up voltage conversion ratio. A full bridge inverter and a DC-DC converter can collaborate by connecting their respective outputs. The inverter gets its juice from an AC power source. Snubberless DC/DC inverters are utilized in electric vehicles, residential solar PV systems, and energy storage systems. The MATLAB Simulink Model was used for both the development and verification of the system.
IOT-DRIVEN FALL DETECTION: A SMART MONITORING SYSTEM FOR ELDERLY CARE
The number of senior accidents is more than previously believed, according to statistics. Fatalities or severe injuries could result from such incidents. Curiously, falls account for 70% of mortality in the 75-plus age group. As a result of falls, more than 90% of hip fractures in the elderly occur in this age group. A study conducted in 2000 by the American Family Physician indicated that among the elderly, 33% experience falls in their homes, while 60% do so in nursing homes. Dr. George F. Fuller, of the United States Army Medical Center in Washington, D.C., received this communications. We need to launch an awareness campaign and employ statistical analysis to secure assistance. A multimodal fall detection and assistance tool is the intended outcome of this study. It tracks the user's orientation, speed, and weight. You get a CPU, Wi-Fi, load sensor, and measurement system. To identify a fall, record, transmit, and track any changes to the initial velocity, trajectory, or mass data. Upon activation of the alarm, the microcontroller communicates with the appropriate parties by sending and receiving multiple signals. A little shift in the probability of an event can be mistaken for a "decline," leading to unnecessary alarms and stress. In case of emergencies, the gadget contains a "nap button," also known as a safety button. Pressing this button will temporarily turn off the sensor system. It can be fastened to a wheelchair if that becomes necessary. A smart device is also yours. Users can receive aid quickly and avert serious harm with this incredible technology.
DESIGN & ANALYSIS OF 4:2 COMPRESSORS FOR ACCURATECONFIGURABLEMULTIPLIERS
The four 4:2 compressors discussed in this study are capable of operating in near and precise modes. These multi-function compressors have an estimating mode that allows them to decrease accuracy while enhancing speed and energy efficiency. Each of these compressors has its own unique power and time requirements for switching between real and estimated modes. All of these engines perform to varying degrees of accuracy when operating in approximation mode. Modifying the compressor's power, speed, and precision is made possible by a computer using preset multipliers. The planning procedure requires the usage of parallel multipliers. The results show that the actual 4:2 compressor consumes significantly more power than the optimal estimate. The 8x8 Dadda Multiplier was followed by the 16x16 and 32x32 Dadda Multipliers. Four distinct compressor types with four distinct approximation factors are available when the quality ratio is 4:2. Examining the dimensions and reaction times of the various components is crucial.
DESIGN AND IMPLEMENTATION OF AN IOT-BASED REAL-TIME ALERT SYSTEM FOR WOMEN’S SAFETY
The contributions of women to every society and culture are vital. Domestic violence, sexual harassment, unwanted touching, and rape are all types of sexual violence against women. With the IoT, people can solve a lot of problems and live better lives all at once. The community has developed IoT-based strategies to protect women. Much effort has gone into developing machine learning, smart technology, sensor, and Internet of Things (IoT) devices with the goal of making women's safety a priority. The majority of these women's safety devices contain integrated pressure and pulse-rate sensors, according to the findings. Women at risk can benefit from machine learning techniques such as hidden Markov models, decision trees, and logistic regression in order to prevent future issues. A large body of research suggests that the optimal course of action is to develop alarm delivery automation systems that are both more accurate and require less human intervention. The Internet of Things (IoT) safety products for women are systematically organized in this study according to the wearable, sensors, features, and technologies that make them work. There are challenges and concerns with the Internet of Things (IoT) that must be addressed, despite the fact that it can aid in women's safety. An approach to construction that prioritizes key components for women's Internet of Things safety devices is also proposed in this study.
A MACHINE LEARNING APPROACH TO CYBER BREACH PREDICTION USING SUPPORT VECTOR MACHINES
The evolution and development of cyber dangers can only be understood with the use of data collected from cyber occurrences. Since this field of study is still in its infancy, there is much more to discover. Cyberespionage activities, primarily malware infections, that occurred between 2005 and 2017 are the focus of this statistical study. We found that distributions based on autocorrelation do not adequately describe the frequency and severity of cyber attacks. Rather, random procedures work better. Then, we display the bespoke stochastic process models we developed to account for different breach magnitude and inter-breach time span. On top of that, we prove that these models are highly predictive of both arrival timeframes and damage levels. We use quantitative and qualitative trend studies to find out how cyber events have evolved over the years. Concerning security, one can say many things. The frequency of leaks has increased, but the total number of leaks has remained unchanged.
A HYBRID CNN–RANDOM FOREST AND CNN–XGBOOST FRAMEWORK FOR REAL-TIME FOREST FIRE DETECTION
This technology aims to detect forest fires immediately. Finding flames is made possible through methods for processing video and photos. Methods for photo recognition constitute the bulk of the effort. Due to the dynamic nature of fire borders, a procedure involving the elimination of the background is necessary. By utilizing a color segmentation approach, the potential zones are subsequently located. Use of a Convolutional Neural Network (CNN) allows for the detection of fires in the selected regions. A real fire may be located with pinpoint accuracy even in the absence of monitoring devices, and the forest service is promptly alerted.
TRANSPARENT AND ACCURATE OBESITY RISK CLASSIFICATION USING ENSEMBLE LEARNING AND LIME EXPLANATIONS
The purpose of this method is to make sure that forecasts are clear and easy to use for planning healthcare, while also accurately assessing the risk of obesity. It makes use of the advantages of several classifiers, such as Random Forest, Gradient Boosting, and Logistic Regression, by employing a hybrid ensemble learning methodology. In order to address the complex nature of obesity, the methodology takes into account a wide range of clinical and lifestyle factors, such as age, BMI, eating habits, physical activity, and hereditary susceptibility. The implementation of thorough preprocessing, feature improvement, and imbalance management sustains the learning process and reduces biased results. The goal of LIME is to provide instance-level explanations that highlight the characteristics that have the biggest impact on each person's risk score. This clarifies predictions. In terms of accuracy, precision, recall, F1-score, and confusion matrices, models generally outperform individual models. Strong generalization across a range of behavioral and demographic characteristics is shown by the ensemble. It ensures that doctors can rely on the results by striking a balance between forecasting accuracy and understandability. The method combines transparency and dependability to allow for efficient, real-time obesity risk assessment. To sum up, it is a reliable instrument for making decisions in medical environments that prioritize prevention.
Explainable Artificial Intelligence (XAI)
IDENTIFYING MONEY LAUNDERING ACTIVITIES IN CRYPTOCURRENCY USING MACHINE LEARNING
The quick rise of cryptocurrencies has made it easier to carry out complex money laundering schemes via decentralized networks and to create cutting-edge financial services. Conventional rule-based monitoring systems often fail to keep up with the latest techniques of money laundering on blockchain networks. Machine learning is a scalable approach for analyzing extensive transaction data and detecting anomalous, suspicious patterns. The suggested methodology utilizes both supervised and unsupervised models to discern behavioral signs of unlawful activities. To enable identification, it is imperative to gather vital information, encompassing network architecture, transaction frequency, wallet interactions, and temporal patterns. To improve the model's accuracy and robustness, we employ dimensionality reduction and feature engineering techniques. The system is trained on annotated datasets that include both lawful and unlawful transactions. Experimental assessment is more accurate and preserves information more efficiently compared to conventional detection approaches. This approach facilitates the swift identification of high-risk wallets and transaction patterns.
SENTIMENT AND CONTENT-BASED CYBERBULLYING DETECTION USING MACHINE LEARNING
Social media's rapid growth has exacerbated cyberbullying, necessitating the implementation of automated detection techniques. Tokenization, noise reduction, and normalization are among the techniques employed to process content on the internet. In order to identify abusive language patterns, it is possible to locate emotional indicators and sentiment polarity. TF-IDF vectors, semantic embeddings, and N-grams are among the manifestations of contextual meaning. Predictive classifiers that are instructed include KNN, Random Forest, and Deep Neural Networks. A strategy that employs ensemble learning enhances accuracy and reduces the incidence of false positives. The paradigm effectively differentiates between content that is non-bullying, harassment, and bullying. In an effort to evaluate performance, we evaluate the confusion matrix, F1-score, precision, recall, and accuracy. The results indicate that the detection process is more effective when multiple models are employed than when only one is employed. In general, the algorithm is effective in generalizing to a variety of forms of cyberbullying. Abuseful sentences that alter predictions are identified by interpretability techniques. In general, the strategy simplifies the identification of cyberbullying in a variety of online scenarios.
DESIGN AND IMPLEMENTATION OF A WEAPON DETECTION SYSTEM FOR SMART CITIES USING DEEP LEARNING
Deep learning-based convolutional neural network topologies help build and implement smart city weapons detection systems. The device detects knives and guns in real time using cutting-edge computer vision. Time-sensitive urban security applications can grow inference with low latency using the edge and cloud. A robust data preparation and augmentation method makes the system more tolerant to lighting, occlusion, and crowds. Many labeled datasets are used to train the model for different urban scenarios and camera angles. Transfer learning reduces training time without compromising detection. An intelligent alerting system alerts authorities to high-confidence detections. Accuracy, recall, F1-score, and inference speed are common system performance measurements. Experiments show detection works dependably in difficult real-world situations. This architecture allows smart city systems to install many cameras. Data is processed for privacy and preserved minimally owing to ethics and legislation.
PERFORMANCE ANALYSIS OF LARGE INTELLIGENT SURFACE-ASSISTED NON-ORTHOGONAL MULTIPLE ACCESS FOR 6G WIRELESS NETWORKS
In order to establish sixth-generation (6G) wireless networks, we must implement innovative technology that can accommodate increased data rates, multiple connections, and improved spectral efficiency. Reconfigurable intelligent surfaces, also known as large intelligent surfaces (LIS), enable clever wireless environment control. Through power-domain multiplexing, Non-Orthogonal Several Access (NOMA) effectively enables numerous users to use the same spectrum. With a focus on critical elements like spectrum efficiency, energy efficiency, outage probability, and system throughput, this study investigates the performance of LIS-assisted NOMA systems for 6G networks. When multiple people speak simultaneously, the combination of LIS and NOMA increases coverage, reduces interference, and improves signal quality. The findings show that LIS-assisted NOMA works significantly better than traditional systems, making it a viable option for the upcoming generation of wireless communication.
Reconfigurable Intelligent Surface (RIS)
IOT AND BIG DATA-BASED FOOD SAFETY TRACEABILITY SYSTEM FOR PUBLIC HEALTH MONITORING
IoT and Big Data analytics are being used in this project to make a food safety tracking system that protects public health. Because the food supply chain is so complicated, it is hard to make sure that quality, safety, and openness are maintained during production, processing, transport, and distribution. IoT-enabled sensors and smart devices are used in the proposed system to keep an eye on the production of food, the temperature of storage, transportation, and environmental factors in real time. Big data technologies make it easy to store, analyze, and make choices based on huge amounts of different types of data. With a digital traceability system, farmers, manufacturers, distributors, regulators, and customers can all keep track of the lifecycle of a food product and quickly find safety risks. The method lowers the risk of contamination and makes the supply chain more open. It also makes it easier to manage food safety and do recalls. Both IoT and big data work together to keep people healthy and boost trust in today's food supply systems.
MACHINE LEARNING-BASED WEATHER FORECASTING USING TIME SERIES ANALYSIS
The purpose of this research is to investigate the use of machine learning techniques to improve the accuracy of weather forecasts by making use of previous historical meteorological data. Many industries rely on accurate weather predictions, including farming, transportation, emergency preparedness, and ecological design. The research looks for trends in weather by analyzing important meteorological variables including air pressure, temperature, humidity, precipitation, and wind speed and direction. Prediction models are built using a variety of machine learning algorithms, such as neural networks, decision trees, random forests, and support vector machines. With the help of these algorithms, large datasets may be able to uncover complex correlations and produce accurate predictions. Improving predicted accuracy is the primary focus of the research, which places an emphasis on feature selection, data preparation, and model training. Accuracy and error measures are among the suitable metrics used to assess the models' performance. According to the results of the tests, machine learning algorithms can improve the precision of weather predictions by spotting trends more quickly. The accuracy of weather predictions is improved by combining current and historical meteorological data. Businesses that are dependent on the weather can benefit from the suggested method since it helps people make better judgments.
IOT-ENABLED DEEP LEARNING FRAMEWORK FOR CROP DISEASE DETECTION USING IMAGE PROCESSING TECHNIQUES
In order to provide real-time plant health monitoring in smart agriculture, this study proposes a deep learning-based crop disease diagnostic system that makes use of IoT and image processing. The suggested system uses Internet-connected sensors to gather data on temperature, humidity, and soil moisture. Additionally, it uses camera modules to capture high-resolution images of crop leaves. CNNs, which are sophisticated deep learning models, are used to scan and analyze these images. They automatically recognize and categorize agricultural diseases with accuracy. With this method, farmers may make decisions more quickly, decrease manual inspection, and identify diseases earlier. Experiments show that this technology is more precise, effective, and scalable than existing techniques. It is therefore perfect for large-scale farming. This study encourages prudent agricultural management, minimizes crop loss, and enhances resource utilization.
MANAGING THE ENVIRONMENTAL EFFECTS OF MINING: IMPACTS AND SOLUTIONS
The economic development of China has been significantly influenced by the discovery of natural resources; however, mining will always have a substantial negative impact on the environment.The impacts of open pit mining on ecosystems, geological settings, and land resources are examined in this research using a methodical methodology. Environmental and ecological system impacts of open pit mining are the major foci of the research. This is predicated on research into the elements that amplify the environmental disasters brought about by mining operations.The research shows that mining technique, mining extent, and mining type are the three most influential factors on ecology and the environment.The research concludes with a variety of potential logical defenses, control mechanisms, and countermeasures based on the research findings. The goal of all of these is to control and protect the environment.