Category Archives: Uncategorized

Abstract-Dynamic, infrastructure-free communication be- tween mobile devices are made possible via mobile ad hoc networks, or MANETs. Their dependability is hampered by issues including malicious activity, node cooperation, and security risks. A collusion-free MANET communication architecture for safe and effective direct mobile-to-mobile networking is pro- posed in this research. The system uses trust-based processes and sophisticated cryptographic algorithms to identify and stop node collusion. Network performance indicators like throughput, latency, and packet delivery ratio may be thoroughly analyzed through simulation using MATLAB. By improving MANETs’ overall security and dependability, the suggested method enables smooth communication in dynamic, resource-constrained con- texts. Results show that it performs better than current methods, which makes it appropriate for use in remote connectivity, military operations, and disaster recovery.

Abstract-This paper compares two popular methods for time-frequency analysis: the Fast Fourier Transform (FFT) and the Continuous Wavelet Transform (CWT) for a synthetic chirp signal wave. We apply these techniques to a chirp signal, which is a non-stationary signal with a frequency that changes over time. The results demonstrate the strengths and limitations of each method, highlighting the FFT’s ability to provide global frequency information and the CWT’s superior time-frequency localization. FFT renders the representation of signal only in the frequency domain. The CWT, using the morse wavelet, on other hand provides a more compact visualisation of the signal in both time and frequency domain visualising or representing the frequency domain of the signal simultaneously. The performance of these techniques is compared visually and computationally.

Abstract-The rapid growth of urban mobility solutions has increased the demand for efficient and secure bike rental systems. This project proposes an automated bike rental system using RFID, GPS, and IoT technologies to enhance the rental experience. The system ensures accurate rentals, prevents unauthorized access, and optimizes bike availability through real-time tracking. The RFID-based scanning system enables automatic bike identification and authentication, reducing human error. Each bike has an RFID tag, while rental stations feature RFID readers for automated check-in and check-out, streamlining the rental process. GPS tracking provides real-time location monitoring, improving fleet management and user safety. Users can locate bikes, check availability, and plan trips efficiently. IoT integration connects bike locks, tracking modules, and the mobile app for seamless operation. Smart locks with RFID sensors automatically unlock bikes upon successful user verification. IoT ensures real-time updates on bike status, battery levels, and maintenance needs. The mobile application allows users to rent bikes, track rentals, and make payments securely. Secure communication protocols protect user data and prevent cyber threats. The system is scalable, supporting multiple locations and rental stations. Cloud management ensures efficient data handling and fleet coordination. By integrating automated management, real-time monitoring, and enhanced security, this system offers a reliable and safe bike rental solution for urban commuters.

Abstract-IoT-based Heterogeneous WSN (HWSN) technologies are useful instruments for accomplishing sustainability objectives in Sustainable Smart Cities (SSCs) due to their adaptability and wide range of applications. Even though WSN heterogeneity is still being investigated by researchers, it is becoming increasingly crucial to develop affordable models that address many aspects of SSC while maintaining their stability and dependability. To identify disjoint CSs that are energy-aware, we suggest a novel technique, the Required Energy Aware technique (REA). In every iteration, the REA algorithm carefully attempts to create the set that optimizes longevity while abiding by CS criteria. The output simulation increases network longevity, reduces resource consumption, and permits effective distribution of data sensing and collection activities throughout the network.

Abstract-This project aims to design and implement an assistive system for people with disabilities, combining voice- based wheelchair control, health monitoring, and emergency alert features. The system utilizes an Arduino Nano, Bluetooth communication, a mobile application, and various sensors to monitor the user’s health status in real-time. The wheelchair can be controlled via voice commands received from a mobile app, allowing users to move the wheelchair with simple verbal instructions. Health monitoring is achieved through sensors that track the user’s heart rate, body temperature, and detect falls using an accelerometer. In the event of an emergency, the system can send notifications via SMS using a GSM module, which includes the user’s GPS location and health data. Additionally, all sensor data is uploaded to the ThingSpeak IoT platform for remote monitoring and analysis. This integrated system provides not only mobility assistance but also enhances safety and well-being for disabled individuals by offering real- time health status updates and emergency alerts.

Abstract-The banking industry is undergoing a significant transformation with the integration of automation technologies such as Artificial Intelligence (AI), Robotic Process Automation (RPA), and advanced data analytics. Automation streamlines banking operations by reducing manual intervention, increasing efficiency, and minimizing errors. AI-powered chatbots enhance customer service with instant support, while automated fraud detection systems strengthen security and compliance. Additionally, automation improves regulatory adherence by facilitating real-time monitoring and reporting, ensuring transparency and risk mitigation. The implementation of automation also leads to cost savings, operational scalability, and seamless digital banking experiences. As the industry moves towards fully automated banking ecosystems and blockchain integration, automation is set to redefine the financial landscape, making banking more accessible, secure, and customer-centric.

Abstract-One of the most important and often used building materials in masonry construction worldwide is brick. The environmental load brought on by trash deposition can be reduced by making bricks from waste materials. The purpose of this study is to assess the impact of adding trash made from rice husk ash. Samples were prepared with different percentages of cement, fly ash, lime, river sand, and rice husk ash. recycling a variety of waste materials, including as fly ash (40 – 60%), rice husk ash (15 – 20%), lime (10%), cement, and river sand (15 – 20%), for use in brick production. The dimensions of the brick specimen are 230 x 110 x 75 mm. Experiments are conducted to examine differences in properties including compressive strength, water absorption, hardness, and soundness. This review will lead to recommendations for additional research on the effects of that waste on bricks’ mechanical and physical properties. The uses of agricultural wastes as cheap and environmental-friendly construction materials are beneficial towards provision of affordable housing in developing country.

Abstract-The cryptocurrency market is highly volatile and unpredictable, making traditional financial models less effective for price forecasting. Unlike stock markets, which are influenced by earnings reports and economic indicators, cryptocurrency prices are driven by a combination of market sentiment, technological developments, regulatory changes, and supply-demand dynamics. Due to the complexity and non-linearity of these factors, machine learning (ML) has emerged as a powerful tool for predicting crypto prices with greater accuracy. The proposed work employs the steepest descent based scaled back propagation algorithm along with the data pre-processing using the discrete wavelet transform (DWT) for crypto price prediction. It has been shown that the proposed system attains lesser MAPE% error compared to previously existing techniques making it a more accurate forecasting model.

Abstract-This work proposes an intelligent locking and monitoring system to significantly enhance money theft deterrence in banking environments. Leveraging advanced sensor technologies and AI-driven analytics, the system provides real-time threat detection and automated response. Integrated biometric authentication and multi-factor authorization protocols fortify access control to sensitive areas and cash reserves. The system employs dynamic locking mechanisms, triggered by anomalous activity, to prevent unauthorized entry and asset removal. Continuous video surveillance, coupled with intelligent image processing, identifies and tracks suspicious behaviors within the bank premises. Remote monitoring capabilities enable swift intervention by security personnel in response to potential theft attempts. Data encryption and secure communication channels ensure the integrity and confidentiality of sensitive monitoring information. Predictive analytics models are utilized to forecast potential security breaches and proactively mitigate risks. The system aims to minimize human error and response time, thereby increasing the overall effectiveness of theft deterrence. This intelligent approach provides a robust, adaptable, and scalable security solution for modern banking institutions.

Abstract-Multimodal sentiment analysis (MSA) integrates data from multiple sources, such as text, audio, and visual cues, to enhance the accuracy and interpretability of sentiment classification models. Traditional sentiment analysis predominantly relies on textual data, which can be limited in capturing non-verbal nuances like tone of voice or facial expressions. This paper explores the synergy between text, speech, and visual data in sentiment analysis tasks, addressing key challenges such as data alignment, feature extraction, and fusion techniques. We compare various fusion strategies, including early, late, and hybrid fusion, using state-of-the-art deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Experimental results demonstrate that multimodal approaches significantly outperform unimodal systems, providing higher accuracy and robustness in sentiment detection. We discuss the potential applications of multimodal sentiment analysis in fields such as social media monitoring, customer sentiment analysis, and healthcare. Finally, the paper outlines future research directions, emphasizing the need for more efficient fusion techniques and the incorporation of emerging models to advance multimodal sentiment analysis further.