Sanjay Ghodawat University is a State Private University established under Government of Maharashtra Act No. XL of 2017, with the approval of the UGC. It is located in Kolhapur.
In recent days, the evolving growth of social-media applications and their reviews have given rise to Sentimental analysis (SA) to analyze the attitudes, feelings, and views of the users. However, the traditional sentimental analysis mechanism possessed limitations in understanding the context of the text, generalization, interpretability, inaccurate analysis, and dialectal variation. Therefore, to address these aforementioned issues, the Ateles Leading Gorilla Optimizer enabled Deep Ensemble Activation Model (ALGO-DeAM) is proposed in this research. Specifically, the Ateles Leading Gorilla Optimization (ALGO) tunes the hyperparameters and selects the optimal features of the ALGO-DeAM model, which in turn accelerates the training process and minimizes the computation complexity. In real-time SA applications, this hybrid optimization approach offers enhanced accuracy, resilience, and efficiency, making it especially useful for processing high-dimensional and dynamic sentiment data. The DeAM takes advantage of various learning patterns and improves performance by capturing multiple aspects of the input. The proposed ALGO-DeAM attains higher performance with the metrics of accuracy, sensitivity, and specificity, as 98.09
We have developed an eco-friendly and easy-to-use procedure for the conversion of primary allylic and benzylic alcohols to aldehydes using sodium bismuthate and microwave irradiation. The reaction is carried out with high efficiency, and little to no use of harsh and toxic chemicals of the oxidant can be achieved by performing the reaction in an aqueous solution of acetic acid. Allylic alcohols with varied structures gave good results; primary allylic alcohols gave the desired aldehyde products in good yields. Moreover, the procedure is safe and cost-effective, as it involves simple apparatus and the use of sodium bismuthate as a reactant, along with microwave irradiation, which provides efficient and fast reactions. Therefore, this method provides an eco-friendly, efficient, and economical way of synthesizing aldehydes as per the guidelines of green chemistry.
In the present study, Ni-doped ZnO nanosheets (NS) were successfully synthesized via biogenic route, utilizing Azadirachta indica (neem) leaf extract. This plant is commonly known for its medicinal uses, served as a sustainable reducing and stabilizing agent in the biogenic synthesis process. The synthesized Ni-doped ZnO NS were systematically characterized to evaluate their structural, optical, morphological, and thermal properties using a range of analytical techniques, including powder-XRD, HR-TEM, SEM, FTIR, UV-visible absorption spectroscopy, photoluminescence spectroscopy, and thermogravimetric analysis. Biogenically synthesized samples showed nanosheet-like morphology in HR-TEM and SEM analyses, leading to an increased surface area. The incorporation of Ni 2+ ions into ZnO NS, confirmed by analytical techniques, resulted in a synergistic effect that significantly enhanced the NS’ antimicrobial performance. Antibacterial activity was evaluated against Escherichia coli and Staphylococcus aureus to assess broad-spectrum efficiency. The Ni doping was found to improve reactive oxygen species (ROS) generation and increase surface positive charge, resulting in greater bacterial membrane interaction—particularly against E. coli . The antimicrobial performance of the biogenically synthesized Ni-doped ZnO NS closely matched that of its chemically synthesized Ni-doped ZnO NPs, demonstrating the potential of eco-friendly synthesis routes in developing effective antibacterial nanomaterials.
The rapid growth of electric mobility demands efficient and reliable ultra-fast charging infrastructures for Electric Vehicles (EVs), which remains challenging due to limitations in power availability and efficient energy management from renewable sources. To address this issue, this study proposes a Hybrid Renewable Energy System (HRES) integrating wind and photovoltaic (PV) sources for EV ultra-fast charging. In the proposed system, the AC output of a Doubly Fed Induction Generator (DFIG)-based Wind Energy Conversion System (WECS) is converted into DC using a PWM rectifier, while a Chaotic Particle Swarm Optimization (PSO) based MPPT algorithm is employed to maximize wind power extraction. The PV subsystem utilizes an Interleaved KY converter to achieve high voltage gain, regulated by a cascaded Artificial Neural Network (ANN) controller for improved dynamic response. The DC-link supplies power to the EV charging converter, while excess renewable energy is intelligently redirected to the grid to support peak demand. Grid-side power regulation is achieved using PI and cascaded ANN controllers. Simulation results in MATLAB demonstrate that the proposed Chaotic PSO MPPT achieves a high tracking efficiency of 98.79%, while the Interleaved KY converter attains an efficiency of 94.69%. Furthermore, the cascaded ANN controller exhibits improved transient performance with a settling time of 0.1 s, ensuring faster system stabilization. These results highlight the effectiveness of the proposed control and power conversion strategies in enabling a robust, efficient and intelligent renewable-energy-based EV ultra-fast charging infrastructure.
The emergence of IoT and its applications have enforced different security challenges to identify unauthorized users. Authenticator is one of the applications which is used to provide multi fold security for better robustness. Still there is a possibility that some unauthorized users will try to access the applications. In this article, we present a comprehensive exploration of user-centric analysis and suspicious user detection, specifically focused on the authentication process within the Authenticator application. With cybersecurity being of paramount importance, the study employs advanced machine learning techniques to analyze user interactions and activities, aiming to identify and flag potentially suspicious behavior within individual user accounts. The Authenticator multi-factor authentication system, encompassing email-password, One-Time Password (OTP), and push notification steps, forms the basis for analysis. The study’s motivation lies in safeguarding user accounts from unauthorized access and fraud, necessitating proactive measures against evolving cyber threats. The approach involves processing unstructured, unsupervised data from Elasticsearch and Kafka, extracting valuable insights through feature aggregation, temporal analysis, and geospatial aspects. Evaluation employs the Silhouette Score to measure k-means clustering quality, as well as in the Isolation Forest model, contributing to effective suspicious user detection. During the prediction phase, we retrieve a master dataframe from the SQL database, which contains patterns of both suspicious and normal user behaviors. Utilizing the k-nearest neighbors (KNN) algorithm, we identify the nearest matching pattern from this master dataframe and assign that label to our test data. The study’s outcomes enhance security in the Authenticator application by distinguishing normal and suspicious login patterns, strengthening the multi-factor authentication process for increased reliability.
