bluuebunny/crossref_metadata_2025 huggingface dataset:
Autonomous AI systems in the face of liability, regulations and costs
Authors: Saenz Agustina D. , Harned Zach , Banerjee Oishi , Abràmoff Michael D. , Rajpurkar Pranav
Published: October 2023

Autonomous AI systems in medicine promise improved outcomes but raise concerns about liability, regulation, and costs. With the advent of large-language models, which can understand and generate medical text, the urgency for addressing these concerns increases as they create opportunities for more sophisticated autonomous AI systems. This perspective explores the liability implications for physicians, hospitals, and creators of AI technology, as well as the evolving regulatory landscape and payment models. Physicians may be favored in malpractice cases if they follow rigorously validated AI recommendations. However, AI developers may face liability for failing to adhere to industry-standard best practices during development and implementation. The evolving regulatory landscape, led by the FDA, seeks to ensure transparency, evaluation, and real-world monitoring of AI systems, while payment models such as MPFS, NTAP, and commercial payers adapt to accommodate them. The widespread adoption of autonomous AI systems can potentially streamline workflows and allow doctors to concentrate on the human aspects of healthcare.
Dicalcin suppresses invasion and metastasis of mammalian ovarian cancer cells by regulating the ganglioside-Erk1/2 axis
Authors: Miwa Naofumi , Hanaue Mayu , Aoba Kayo , Saito Ryohei , Takamatsu Ken
Published: October 2023

Metastasis, a multistep process including cancer cell migration and invasion, is the major cause of mortality in patients with cancer. Here, we investigated the effect of dicalcin, a Ca2+-binding protein, on the invasion and metastasis of ovarian cancer (OC) cells. Extracellularly administered dicalcin bound to the membrane of OV2944 cells, mouse OC cells, and suppressed their migration in vitro; however, cell viability or proliferation were unaffected. Repeated intraperitoneal injection of a partial peptide of dicalcin (P6) prolonged the survival, and reduced the number of microcolonies in the livers of cancer-bearing mice. P6 bound to the ganglioside GM1b in a solid-phase assay; treatment with P6 inhibited the constitutive activation of Erk1/2 in OC cells, whereas excess administration of GM1b augmented Erk activity and cancer cell migration in vitro. Thus, dicalcin, a novel suppressor of invasion and metastasis of OC cells, acts via the GM1b-Erk1/2 axis to regulate their migration.
A machine learning approach to rapidly project climate responses under a multitude of net-zero emission pathways
Authors: Kitsios Vassili , O’Kane Terence John , Newth David
Published: October 2023

Navigating a path toward net-zero, requires the assessment of physical climate risks for a broad range of future economic scenarios, and their associated carbon concentration pathways. Climate models typically simulate a limited number of possible pathways, providing a small fraction of the data needed to quantify the physical risk. Here machine learning techniques are employed to rapidly and cheaply generate output mimicking these climate simulations. We refer to this approach as QuickClim, and use it here to reconstruct plausible climates for a multitude of concentration pathways. Higher mean temperatures are confirmed to coincide with higher end-of-century carbon concentrations. The climate variability uncertainty saturates earlier, in the mid-century, during the transition between current and future climates. For pathways converging to the same end-of-century concentration, the climate is sensitive to the choice of trajectory. In net-zero emission type pathways, this sensitivity is of comparable magnitude to the projected changes over the century.
MALDI-TOF Mass Fingerprinting for Rapid Identification of Bacteria: Data Analysis and Algorithm Development
Authors: Yang Y.
Published: October 2023

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been widely used for identification of microorganisms. In a typical MALDI-TOF MS analysis of microorganisms, spectra of unknown samples are compared to reference libraries of spectra of known microorganisms by spectral pattern matching. This chapter provides an overview of the data analysis workflow for MALDI-TOF MS-based identification of microorganisms, including spectrum preprocessing, spectral matching, and result interpretation. The existing computational methods for the three steps of data analysis and available software solutions are summarized. In addition, bioinformatic methods that do not require a reference spectral library are introduced as alternatives to typical spectral matching approaches. Finally, the current challenges and outlook of MALDI-TOF MS data analysis for microorganism identification are discussed.
Metal–support interactions in Pt-embedded porous Fe2P nanorods for efficient hydrogen production
Authors: Idris Ahmed Mahmoud , Zheng Song , Zhang Meng , Jiang Xinyan , Jiang Guocan , Wang Jin , Li Sheng , Li Zhengquan
Published: October 2023

A novel Pt–Fe2P catalyst featuring metal–support-interactions was delicately designed via a facile MOF-derived approach assisted by phosphating treatment toward the enhanced dye-sensitized photocatalytic HER with visible light.
Filling the gap with a bulky diaryl boron group: fluorinated and non-fluorinated copper pyrazolates fitted with a dimesityl boron moiety on the backbone
Authors: Vanga Mukundam , Diroll Benjamin T. , Muñoz-Castro Álvaro R. , Dias H. V. Rasika
Published: October 2023

Brightly luminescent, diarylboron decorated, cyclic copper trimers with and without cuprophilic interactions have been synthesized and characterized using multiple techniques including X-ray crystallography.
Exploring denitrification and anammox processes in the saturated zone of passively operated vegetated biofiltration systems
Authors: Zhang Kefeng , Prodanovic Veljko , Zhao Hengke , Zheng Min , Hu Shihu , Hong Pei-Ying , Yuan Zhiguo , Deletic Ana
Published: October 2023

Vegetated biofiltration systems (VBS) are common nature-based solutions (NBS) for urban water treatment, but their performance in treating nitrogen-rich wastewater is yet to be explored.
Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials
Authors: Clough Robert , Fisher Andy , Gibson Bridget , Russell Ben
Published: October 2023

This review covers advances in the analysis of advanced materials, metals, fuels and lubricants, nanostructures, ceramics, refractories, organic and inorganic chemicals, catalysts and nuclear materials by a range of techniques including X-ray, ICP, LIBS, mass spectrometry, synchrotron-based techniques, plus non-destructive and ablation surface techniques.
Phosphorus and nitrogen co-doped reduced graphene oxide as superior electrode nanomaterials for supercapacitors
Authors: Rhili Khaled , Chergui Siham , Abergo-Martinez Juan Carlos , El Douhaibi Ahmad Samih , Siaj Mohamed
Published: October 2023

Phosphorus/nitrogen co-doped reduced graphene oxide (PN-rGO) materials have been prepared by one-step pyrolysis of the functionalized GO (FGO) with hexachlorocyclotriphosphazene and p-phenylenediamine for supercapacitor devices fabrication.
A polyoxomolybdate-based hybrid nano capsule as an antineoplastic agent
Authors: Joshi Arti , Acharya Sobhna , Devi Neeta , Gupta Ruby , Sharma Deepika , Singh Monika
Published: October 2023

Anti-tumoral properties of bio(organic) functionalized polyoxomolybdate. The synthesized hybrid nanocapsules exhibited excellent biocompatibility and stability at physiological pH making them efficient candidates for biomedicinal application.
Magnetic field effects on the crystal structure, morphology, energy gap, and magnetic properties of manganese selenide nanoparticles synthesized by hydrothermal method
Authors: Nokabadi Ali Salmani , Yazdani Ahmad
Published: October 2023

In this study, we synthesized manganese selenide under magnetic fields ranging from 0 to 800 gauss and investigated its optical, electrical, and magnetic properties.
Low-temperature RWGS enhancement of Pt1−nAun/CeO2 catalysts and their electronic state
Authors: Onodera Taigo , Miyake Tatsuya , Sugimasa Masatoshi
Published: October 2023

Reverse water–gas shift (RWGS) operation at lower temperatures has multiple advantages such as use of low-cost materials and improvement of thermal efficiency.
Do metastable polymorphs always grow faster? Measuring and comparing growth kinetics of three polymorphs of tolfenamic acid
Authors: Sacchi Pietro , Neoptolemou Petros , Davey Roger J. , Reutzel-Edens Susan M. , Cruz-Cabeza Aurora J.
Published: October 2023

The crystal growth rates of three polymorphs of tolfenamic acid in isopropanol solutions are measured and compared to explore the relation between crystallisation kinetics and polymorph appearance.
Combining lipid-mimicking-enabled transition metal and enzyme-mediated catalysis at the cell surface of E. coli
Authors: Wegner Tristan , Dombovski Alexander , Gesing Katrin , Köhrer Alexander , Elinkmann Matthias , Karst Uwe , Glorius Frank , Jose Joachim
Published: October 2023

A novel approach of bacterial membrane functionalization using a lipid mimicking NHC–palladium complex was developed and combined with protein surface display to enable a metal-/enzyme-mediated cascade reaction on the cell membrane of E. coli.
Performance analysis of ZVS and ZCS boost converters for Solar Photo Voltaic Applications
Authors: Gireesh Kumar Devineni , Venkata Sireesha Nagineni , Malleswara Rao D.S. Naga , Sainadh Singh Kshatri , Chaudhary Prashant , Pavan kumar T.V.V.
Published: October 2023

This paper describes a useful modulated, asymmetric full-bridge converter for sustainable solar PV applications. The suggested converter achieves zero-voltage switching (ZVS) on the main side’s power switches and reduces circulating current loss by utilizing a full-bridge architecture and an asymmetric control method. The blocking capacitor and the leakage inductance of the transformer operate in resonance to further provide the Zero Current Switching (ZCS) off for the output diode without the need for any further circuitry. Output diode reverse recovery is no longer a concern as a result. Additional connections to the input voltage include the power switch voltage stresses. Because of these features, the suggested converter has the infrastructure to reduce power losses. The buildings of renewable energy sources can benefit especially from it.
Optimized Inverter Circuits for Driving Dual AC Loads for Home Solar Water Pumping
Authors: Gireesh Kumar Devineni , Venkata Sireesha Nagineni , Malleswara Rao D.S. Naga , Chinna Dastagiri Goud Pandla , Vaqur Musheer , Pavan kumar T.V.V.
Published: October 2023

This research presents a generic pulse width modulation (PWM) approach for nine switch inverters and five-leg inverters. The circuit design of both inverters and a modulation mechanism for separate drives have been described. The available DC bus voltage supplied the two induction motors using a PWM technique for a 5-leg inverter. Nine-switch inverters and nine-switch z-source inverters have lately been presented as dual-output inverter. A nine-switch z-source inverter and a unique pulse width modulation (PWM) were created. Also, a five-leg inverter is modelled using Extended Two Arm Modulation (ETAM) scheme. The suggested modulation techniques were applied to control two machine drive systems with a single inverter architecture with a reduced switch count for pumping the water to house hold appliances. The comparative analysis of the two dual-output inverters is integral to this work.
Single Phase Grid Connected PV-Wind-Diesel-Battery System Operated Three Phase Induction Motor for Aqua-Farms
Authors: M. Rekha , Srividya Devi P. , Pakkiraiah B. , Anitha Lakshmi A. , Bisht Amita
Published: October 2023

Sustainable Aqua Farms requires continuous power supply to drive three phase induction motors for maintaining good oxygen levels. Unfortunately many places are suffering from frequent power cuts as well as availability of only single phase supply. Hence, 1-Φ to 3-Φ conversion is essential to operate 3-Φ motors as well as required operation of diesel generators during no supply from grid. However, consumption of heavy diesel causes many problems in aqua culture which is not sustainable. Hence, in order to reduce diesel consumption, and increase eco-friendly environment, photovoltaic (PV) and wind generator system can be integrated to system. A proper energy management system is required among all these sources to achieve cost effective system with best performance. The battery is used for responding in transient time periods. The proposed energy management system can reduce consumption of diesel by operating it at maximum efficiency. In case of any excess power available from wind and PV, the surplus power can be transmitted to single phase grid to reduce electricity bill. In order to maintain stable dc-link voltage, Sliding Mode Controller (SMC) based DC to DC converters are incorporated. The proposed controller reduces the ripples in motor torque to improve the life time of components used in system as well as can reduce power consumption with the addition these renewable energy resources. For reduction of ripples in torque, a TS-Fuzzy based speed sensor less direct torque controller is implemented to run induction motor through three phase inverter.
Modeling and Development of Wireless Power Transmission System for Electric Vehicles
Authors: Awaar Vinay Kumar , Sandhya Rani M.N. , Bellal Sai Vignesh , Kurva Ganesh Kumar , Kirola Madhu , Baloji D.
Published: October 2023

The main objective of this paper is to develop a sustainable Wireless power transmission (WPT) model. WPT is the technique of transferring electric energy from a power source to a load without any contact between them. This process can be done through different methods using different topologies, which include Series Series (SS), Series Parallel (SP), Parallel Series (PS), and Parallel Parallel (PP). One of the most widely used methods in WPT is inductive power transfer. The main advantages of WPT – it allows charging for multiple devices, has high charging speeds, low cost, less maintenance, and higher efficiency than wired charging. Despite significant advancements in charging solutions, the system still has various issues, such as coil position, the number of receiving coils, the distance between transmitter and receiver coils, high initial cost, and impacts on its sustainability. This paper discusses the simulation model of the WPT model using SS topology in the JMAG software and analyzes the effect of the position of coils and distance between transmitter and receiver coils on the output. This simulation model can further be used to build a prototype for charging EV batteries.
Modelling and Development of Static Wireless Charging System for Electric Vehicles
Authors: Awaar Vinay Kumar , Sandhya Rani M.N , Narasimha Rao B.L. , Pavan Kumar U. , Tiwari Shailendra , Singarapu Sathish
Published: October 2023

In this work, it is proposed to develop wireless charging systems for sustainable electric vehicles. Wireless charging can transfer power from an outlet to devices without a connecting wire. Wireless electric vehicle charging is of two types; they are static wireless charging and dynamic wireless charging. The main advantages of wireless charging are that it takes less time, is more efficient, and less maintenance, and is more durable. However, the wireless charging system also has some problems, like the vehicle must be parked close to the charging pad, the installation cost, and a large battery required. Static wireless charging involves the charging of a sustainable electric vehicle while it is in a stationary position. While dynamic charging involves charging an electric vehicle in motion, charging pads are installed on the roads. In this work, the simulation model has been developed for the PS topology of wireless charging using JMAG software. This can further be developed in a prototype for EVs' static wireless charging system.
Automated Handwritten Text Recognition
Authors: Ram Kumar R.P. , Chandra Prasad A. , Vishnuvardhan K. , Bhuvanesh K. , Dhama Sanjeev
Published: October 2023

A computer’s capacity to recognize and convert handwritten inputs from sources like photographs and paper documents into digital format is known as Automated Handwritten Text Recognition (AHTR). Systems for reading handwriting are frequently employed in a variety of fields, including banking, finance, and the healthcare industry. In this paper, we took on the problem of categorizing any handwritten artwork, whether it be in block lettering or cursive. There are many different types of handwritten characters, including digits, symbols, and scripts in both English and other languages. This makes the evolution of handwriting more complex. It is difficult to train an Optical Character Recognition (OCR) system using these requirements. In order to convert handwritten material into digital form, this work aims to categorize each unique handwritten word. Because Convolutional Neural Networks (CNNs) are so good at this task, they are the best method for handwriting recognition system. The method will be used to identify writings in various formats.
Deep Generative Models for Automated Dehazing Remote Sensing Satellite Images
Authors: Poornima E. , Mohit Suryadevara , Cheresh Reddy Kunduru , Hemchandra Vallepu , Chandramauli Awadhesh , Kondal Rao Peram
Published: October 2023

Remote Sensing (RS) is the process of observing and measuring the physical features of an area from a distance by monitoring its reflected and emitted radiation, usually from a satellite or aircraft. The application of RS spans a wide range of fields, including precision agriculture, disaster management, military operations, environmental monitoring, and weather assessment, among others. Haze or pollution in the satellite images, makes satellite images unsightly and makes valuable information useless. Sometimes satellites must capture images in haze-filled atmospheres, rendering them unusable for study. This proposed work is implemented using the Modern Deep Learning techniques to dehaze the satellite images. We have proposed two GAN architectures, INC-Pix2Pix and RNX-Pix2Pix. A publicly available dataset was used for training our proposed approaches. To eliminate haze from images, we have suggested Deep Generative models by employing the best developments in the field of image processing. By using generative models, images can be dehazed without information loss, supporting the paper’s objective. It has the capacity to learn any kind of underlying data distribution using its learning mechanism. Therefore, it can dehaze satellite images that have been corrupted by haze using the approach automated dehazing remote sensing satellite images using deep learning models . Existing systems can be made more efficient by integrating this approach.
Automated Interview Evaluation
Authors: Sri Latha Ch. , Krishna Kalyan N. , Abhilash J. , Kaushik O.V.S.N. , Balmiki Vinod , Venukumar S.
Published: October 2023

The Interview Automation System is an Artificial Intelligence (AI) powered platform designed to help users prepare for real-life interviews. By asking questions and evaluating responses against original answers, our system provides users with a valuable opportunity to practice and improve their interview skills. The research aims to increase users' familiarity with the interview process, ultimately boosting their confidence and improving their chances of success. The workflow of the research involves several key steps, including data pre-processing and database management, audio-based question delivery, and audio-based answer collection and evaluation. Our platform is easily scalable and can be expanded to include additional functionalities such as users confidence detection, resume-based topic questioning, and body language analysis.
TEXT2AV – Automated Text to Audio and Video Conversion
Authors: Sanjeeva Polepaka , Balasri Nitin Reddy Vanipenta , Indraj Goud Jagirdar , Guru Prasad Aavula , Pathani Ashish
Published: October 2023

The paper aims to develop a machine learning-based system that can automatically convert text to audio and text to video as per the user’s request. Suppose Reading a large text is difficult for anyone, but this TTS model makes it easy by converting text into audio by producing the audio output by an avatar with lip sync to make it look more attractive and human-like interaction in many languages. The TTS model is built based on Waveform Recurrent Neural Networks (WaveRNN). It is a type of auto-regressive model that predicts future data based on the present. The system identifies the keywords in the input texts and uses diffusion models to generate high-quality video content. The system uses GAN (Generative Adversarial Network) to generate videos. Frame Interpolation is used to combine different frames into two adjacent frames to generate a slow- motion video. WebVid-20M, Image-Net, and Hugging-Face are the datasets used for Text video and LibriTTS corpus, and Lip Sync are the dataset used for text-to-audio. The System provides a user-friendly and automated platform to the user which takes text as input and produces either a high-quality audio or high-resolution video quickly and efficiently.
Smart and Sustainable Surveillance System
Authors: Karuna G. , Srinith Reddy A. , Vishal K. , Pavan E. , Singh Negi Gaurav
Published: October 2023

Safety and security are major concerns in the modern day. People and organizations can employ security mechanisms to safeguard their property for their homes or commercial enterprises. Present security systems involve the utilization of Assorted Sensors in cameras for video surveillance. This paper aims at providing one such idea to ensure the protection and security of one’s property. This technique performs Face Recognition as an authentication procedure when a new face is detected by a snapshot. We propose to present a sensible, smart and sustainable Closed - Circuit Television (CCTV) camera with intrusion detection using the LBPH-Local binary pattern histogram algorithm, SIM-Structural Similarity Index Measure, Haar Cascade Classifier, and TKinter. By utilizing intrusion detection, CCTV cameras record real-time videos and process the video at the time of recording to search out the unwanted people arriving within the surveillance area. Our GUI has different buttons supported with features. Adding DL support would create broad scope in this paper such as with DL we would be able to add up much more functionality. We can have future enhancements on this paper such as creating Portable CCTV, Deadly weapon detection, Accident Detection, Fire Detection.
Automated Diagnosis of Pneumonia using CNN and Transfer Learning Approaches
Authors: Madhavi A. , Sathya Abhijna Marisetti , Sumanjali Peddanna , Supraja Shane , Ghalwan Manish , Rathna Chary M.
Published: October 2023

Pneumonia is one of the most deadly diseases, especially for children below 5 years of age. To detect pneumonia radiologists, have to observe the chest x-ray and he/she has to update the doctor correctly which sometimes may not be accurate due to human error. The main objective of this paper is to identify if the person has Pneumonia or not with high accuracy. Automated diagnosis of pneumonia can be done with the help of CNN and Transfer Learning Approaches so that the person can get treatment as early as possible. The dataset used here is the chest X-ray (CXR) dataset based on a chest X-Ray scan database from paediatric patients from one to five years of age at the Guangzhou Women and Children’s Medical Centre. Deep Learning (CNN) and Transfer Learning Techniques along with Ensemble Learning have been implemented concluded that CNN achieved an accuracy of 89%, the Transfer Learning model achieved an accuracy of 93% and the ensemble model got an accuracy of 92%. Even though the highest accuracy is for the Transfer Learning model, considering all the other metrics like Recall, Support, and score, Ensemble has exhibited the best results.
Automatic Number Plate Detection for Motorcyclists Riding Without Helmet
Authors: Kiran Kumar Mamidi , Sanjana Chidrala , Shireen Farha , Harichandana Dursheti , Sharma Meera , Manasa M.
Published: October 2023

The increased usage of motorcycles in recent times has resulted in a rise of road accidents and injuries, with the absence of helmets being a major contributing factor. The current process of physically checking helmet usage at junctions or using CCTV footage to detect motorcyclists without helmets is time-consuming and requires human intervention. To address this issue, a computerized model is proposed to automatically detect motorcycle riders wearing helmets from images. The proposed model utilizes the You Only Look Once (YOLO) Darknet deep learning framework, which is customized to detect riders with and without helmets. The model also automates an alert to the rider found without a helmet. The dataset consists of a large collection of images with 80 different object categories, covering a wide range of real-world scenarios. The solution has the potential to enhance the capabilities of ANPR systems for traffic management, parking management, law enforcement etc.
Helpi – An Automated Healthcare Chatbot
Authors: Karuna G. , Gowthami Reddy Gomaram , Sushmitha J. , Gayathri Bitla , Dev Sharma Sameer , Khatua Debnarayan
Published: October 2023

Due to technological advancements, the healthcare industry has witnessed the emergence of innovative solutions, and one such solution is the healthcare Chatbot. The primary objective of this paper is to create a healthcare Chatbot capable of offering medical assistance to patients. The healthcare Chatbot serves as an AI-based conversational program designed to assist both patients and healthcare providers. The proposed Chatbot, named “HELPI,” functions as a round-the-clock healthcare provider. It utilizes Natural Language Processing (NLP) and Machine Learning (ML) algorithms such as decision trees to analyse user-provided symptoms and accurately detect specific illnesses or diseases. Subsequently, it offers appropriate healthcare recommendations and suggests relevant medications. This broadens HELPI’s capability to address various healthcare-related concerns. In essence, HELPI aims to alleviate the burden on healthcare providers by providing an alternative platform for basic medical advice and support. The success of the HELPI Chatbot lays the foundation for future enhancements. Additional features, such as appointment scheduling, guidance on lifestyle modifications, and medication reminders, could be incorporated to further enhance the Chatbot’s functionality.
Feasible LSTM Model for Detection of Sign and Body Language
Authors: Manasa Vanam , Alekya Damagari , Shreya Sonalin Dash , Shivathmika Kurva , Jeet Rawat Amar , Ahmed Mohiuddin Mohammed
Published: October 2023

The use of sign and body language facilitates communication between hearing individuals and those with hearing loss. They play an important role in facilitating effective human-human and human-computer interactions. It is a visual language composed of hand gestures and facial expressions to identify the meaning conveyed by the signer. Body language detection is the process of analysing the nonverbal cues and gestures used in communication to understand the emotional state and intentions of the speaker. Deep learning methods have recently demonstrated promising results in a range of computer vision tasks, including gesture detection. Using a media pipe holistically, extraction of essential points from the body, hands, and face is possible. TensorFlow and Keras are further utilized to construct Long Short-Term Memory LSTM feasible models that can predict on-screen behaviour presenting an innovative approach for sign and body language detection using LSTM.
An Automated System to Detect Plant Disease using Deep Learning
Authors: Karuna Gotlur , Eshwar Chalamalla , Sai Bharath Yamsani , Rohan Daliyet , Dutt Sharma Harish , Naveen Raja S.M.
Published: October 2023

Crop diseases, particularly in places with weak infrastructure, represent a severe danger to the security of the global food supply. To address this challenge, a platform for accurate identification of plant diseases is needed. The paper proposes deep learning techniques to identify plant diseases. The platform uses the “CNN” algorithm, widely known for its high accuracy in image classification, enabling it to accurately identify plant diseases from images. Furthermore, the platform offers automated suggestions for preventing and provide supplements for that disease and managing the spread of crop diseases using its user-friendly web interface.
Deep Learning based Automated Image Deblurring
Authors: Shruthi Ch.M. , Ramachandra Anirudh Vemullapalli , Bhargava Rao Palla , Shiva Shankar Birru , Pandey Akhilesh
Published: October 2023

Image deblurring is a challenging task that aims to restore a sharp and clear image from a blurred one. This problem is usually caused by camera motion or defocus blur. The objective of this paper is to develop a model that can effectively remove Gaussian blur from an image and improve its quality using deep learning techniques. Automated image deblurring is achieved using deep learning, this approach involves implementing a combination of convolutional neural networks (CNN) and simple auto encoders to train the model on a dataset of blurred and corresponding sharp images. The model is then used to deblur the test images and improve their quality. The paper uses a dataset of blurred and corresponding sharp images to train the model, and the performance of the model is evaluated based on metrics such as PSNR and SSIM. The results and discussions focus on the effectiveness of the model in removing Gaussian blur and improving the quality of the images. In conclusion, the paper demonstrates the effectiveness of using deep learning techniques for image deblurring and provides scope for future enhancements such as incorporating more complex models and exploring other types of blur removal techniques.
Automated Opencv-Based Presentation Controller
Authors: Ramesh G. , Madhavi Karanam , Jaya Prakash Reddy P. , Pedamallu Abhiram , Kumar Raj
Published: October 2023

Présentations have proved to be an effective way of communication in today’s world. Effective présentations are essential as they express one’s idea or analysis in professional meetings. A keyboard or a remote-controlled physical device is generally used to operate the presentations. The display can be performed using hand gestures by utilizing the features provided by Computer Vision (CV). The slides in the presentation can be navigated using specific hand gestures for each operation, like navigating forward or backward in slides. This model also allows the user to virtually draw on the screen in real-time by using their hands. So this model helps eliminate the use of a physical device and is cost-effective. This automation results in a software product that works as an efficient controller. Using this, the user can operate the slides efficiently without any physical contact. This automated model is cost-effective, easy to use, and doesn't require any physical device. This can also see tremendous market potential as presentations have become necessary in every field today. Many features and gestures can be added to further development of this model.
Secure Information System for Visitor Access Recognition using Machine Learning
Authors: Madhavi K. , Ramesh G. , Tetala Lakshmi Soundarya Reddy , Surekha P. , Dhiman Rohit
Published: October 2023

Typical visitor access apps are usually passive/manual. The security guards in highly populated communities have to verify the identity of the visitors every single time, be it the residents or new people. This process is also time-consuming. Availability of the resident is also essential to approve the entry. VISITOR ACCESS is a better substitute for the regular visitor access systems. The process starts with choosing the house number and the name initially. When there is a known person, the visitor is verified, recognized, and approved to enter. If the visitor is unknown, he or she is asked to enter a few more details, which are sent to the resident’s mobile. A message is sent to the visitor’s mobile regarding the approval status to deal with the case of multiple visitors. Open CV is used to verify the identity of the visitors using face recognition, and the app acts as an interface between the users and the system.
Traffic Light Detection for Information Systems and Telecommunications using CNN
Authors: V. Ramya Manasw , B. Sankara Babu , Karanam Madhavi , R. Soujanya , Bisht Deepa , H. Kalyan Rao
Published: October 2023

Information and telecommunications using traffic signals plays major role in computer vision. Distinguishing objects which are in more modest size is a difficult task. We centre around an exceptional case: Detection and the classification of traffic lights in road sees and gives a guidance to the regulator for semi-autonomous and completely self-governing vehicles. We are introducing a profound learning approach for precise traffic signal discovery in adjusting a Single Shot Detection (SSD) approach SSD performs object proposition creation and order utilizing one single CNN. The first SSD battles in recognizing smaller objects, that which is fundamental for Traffic Light Detection (TLD). By our transformations it is feasible to recognize protests a lot more modest than the ten pixels without expanding the input picture size.
Performance Comparison of CNN and DNN Algorithms for Automation of Diabetic Retinopathy Disease
Authors: Sankara Babu B. , Mandapati Bhavita , Mandapati Bhavana , Nallapu Harshith , Samanta Prasenjeet , Maithil K.
Published: October 2023

Automation of medical image analysis helps medical practitioners to ensure early detection of certain diseases. Diabetic Retinopathy (DR) is a widespread condition of diabetes mellitus and a main global cause of vision impairment. The manual diagnosis of diabetic retinopathy by ophthalmologists requires a significant amount of time, causing inconvenience and discomfort for patients. However, the use of automated technology makes it possible to quickly identify diabetic retinopathy, permitting the continuation of therapy without interruption and averting future ocular damage. This paper presents a comprehensive comparative analysis of six Convolutional Neural Networks and Deep Neural Networks based machine learning models, including simple CNN, VGG16, DenseNet121, ResNet50, InceptionV3, and EfficientNetB3, for the recognition of diabetic retinopathy using fundus photographs. The accuracy of various models is evaluated using the Cohen Kappa metric. The results of this study add a contribution to the research on the application of machine learning models for diagnosing diabetic retinopathy.
An efficient and sustainable novel approach for prediction of start-up company success rates through sustainable machine learning paradigms
Authors: Panduri Bharathi , Abhilash P.K. , K. Chidananda , Naga Tejaswi Bethapud Venkata , Naudiyal Anjali , Kodamunja Mahitha
Published: October 2023

The primary objective is to construct a sustainable machine-learning model that utilizes multiple variables to forecast the success of a startup enterprise. It incorporates a Flask application for creating a user-friendly interface, where users can input specific parameters related to a startup, such as financial metrics, industry sector, and location. These inputs are then passed through a sustainable machine learning prediction model, which has been trained on a comprehensive dataset of startup information. The model employs sustainable advanced algorithms to evaluate their startup ventures' potential success. Through the development and deployment of the Flask application and the integration of sustainable machine learning prediction model, this model contributes to the field of startup analysis and decision-making. It offers a sustainable and efficient solution for predicting startup success, empowering users to make data-backed decisions and optimize their resource allocation.
Sustainable Hand Gesture Recognition for Speech Conversion, Empowering the Speech-Impaired
Authors: Ledalla Sukanya , Reddy G. Vijendar , Nagendra Kumar Y. Jeevan , Shailika Joginipelly , Rajput Minakshi
Published: October 2023

A sustainable language disorder affects an individual’s ability to reach out to others through speaking and listening. So utilizing sustainable hand gestures is among the most widespread means of non-verbal and visual communication used by people with speech disabilities worldwide. However, even though sustainable sign language is used everywhere by speech-impaired and hearing-impaired people, most of the populace who don't have any knowledge about sign language face difficulties in sustainably communicating with them. This sustainable problem requires better solutions that can successfully support communication for people with speech disabilities. This sustainable approach will reduce the communication gap for the speech-impaired population. There are many sustainable solutions in the market such as using sensors to make a sustainable device that gives a helpful output. But these sustainable solutions are expensive and not everyone can afford them. We are employing Convolutional Neural Networks to create a sustainable model that is trained on different gestures. This sustainable model enables speech-impaired individuals to convey their information using signs which get converted to human-understandable language, and sustainable voice is given as output. The sustainable hand gestures made are captured as a series of sustainable images which are processed using Python code. This sustainable endeavor introduces a solution that not only automates the identification of sustainable hand gestures but also transforms them into sustainable speech. By interpreting these recognized sustainable gestures, the corresponding recorded audio will be played sustainably. The focus of this sustainable paper is to offer accessibility, convenience, and safety to individuals with speech impairments in a sustainable manner. These sustainable individuals often experience societal discrimination solely due to their disabilities. This sustainable paper is aimed at innovating a sustainable device to help those without the knowledge of sign language sustainably communicate with the people who face difficulty in speech.
Characterization of Microstructure and Properties of Additively Manufactured Materials under Room and Elevated Temperatures
Authors: Balaji B. , Rajashekar N. , Parmar Ashish , Arora Vandana , Abdul-Zahra Dalael Saad , Nijhawan Ginni
Published: October 2023

The utilisation of additive manufacturing (AM) has brought about a significant transformation in the manufacturing process of materials and components, since it allows for the creation of complex geometries and customised designs. The primary objective of this study is to conduct a thorough analysis of the microstructure and characteristics of materials produced by additive manufacturing techniques, including the effects of varying temperatures ranging from ambient temperature to increased levels. Microstructural analysis encompasses several methods, including optical microscopy, scanning electron microscopy (SEM), and X-ray diffraction (XRD), which are employed to investigate the grain structure, porosity, and phase composition. Standardised testing procedures are employed to assess mechanical qualities, such as tensile strength, hardness, and fracture toughness. temperature analysis methods, such as differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), are utilised in order to examine the temperature stability and phase transitions. This study investigates the impact of various printing factors, including layer thickness, printing speed, and build orientation, on the resultant microstructure and characteristics. This study aims to address the disparity between theoretical understanding and actual implementation, therefore facilitating the wider use of additively made materials in businesses that need exceptional performance in many environments.
Investigating the Microstructure and Mechanical Properties of Materials Joined using Advanced Welding Techniques
Authors: Pant Gaurav , Mohanta Prasanta Kumar , Praveen  , Parashar Ashish Kumar , Kareem Safa Abdul , Kansal Lavish
Published: October 2023

The science of materials joining has witnessed the emergence of advanced welding techniques, which have become crucial operations. These techniques provide improved manipulation and regulation of the microstructure and mechanical characteristics of welded joints. The primary objective of this work is to examine the complex correlation between the microstructure and mechanical characteristics of materials that have undergone joining processes utilising sophisticated welding methods. The present research provides insight into the development of many microstructural characteristics, including grain growth, phase transitions, and defect creation, and their influence on the mechanical properties of welded joints. The mechanical characteristics being examined span a broad spectrum of factors, such as tensile strength, toughness, hardness, and fatigue resistance. The evaluation of these qualities is conducted by subjecting specimens obtained from the welded joints to a variety of mechanical tests. Through the correlation of microstructural traits with the observed mechanical behaviour, a more profound comprehension of the structure-property link in advanced welding is attained. In addition, this study investigates the impact of welding parameters, including welding speed, heat input, and shielding gas composition, on the microstructure and mechanical characteristics. The objective of this inquiry is to optimise the welding parameters in order to get the specified material characteristics in the welded connections, while simultaneously minimising the development of faults and ensuring the maintenance of structural integrity.
Optimization of Design and Performance of Medical Implants using FEA
Authors: Singhal Piyush , Krishna B. Vijay , Kumar Ashwani , Chauhan Shilpi , Judi Hawraa K. , Jain Alok
Published: October 2023

In recent years, the optimization of medical implants to enhance their safety and functionality has emerged as a paramount concern in the biomedical field. This study elucidates a comprehensive approach to optimizing the design and performance of medical implants using Finite Element Analysis (FEA). The primary objective was to discern potential areas of stress concentration and deformation, consequently proposing modifications to existing designs. Various implant materials and geometries were explored, encompassing orthopaedic, dental, and cardiovascular applications. The research successfully employed a multi-phased FEA methodology that commenced with the development of an accurate model, followed by the application of realistic boundary conditions and subsequent simulation under physiological loads. Results consistently indicated that by leveraging FEA insights, it was possible to predict potential failure points and areas of undue stress, thereby guiding design modifications. Moreover, it was observed that the iterative design process, supplemented by FEA, led to implants that exhibited enhanced biocompatibility, reduced patient discomfort, and extended longevity. This paper underscores the potency of FEA as an indispensable tool for the evolution of medical implant designs, fostering a future where implant failures become a rarity rather than a risk.
Effects of Processing Parameters on the Microstructure and Mechanical Properties of Intermetallic Matrix Composites
Authors: Shiva Prakash S. , Kumar B. Santhosh , Vishkarma Manoj Kumar , Bhati Savita , Zabibah Rahman S. , Gupta Manish
Published: October 2023

In the realm of advanced materials, Intermetallic Matrix Composites (IMCs) have garnered significant attention due to their potential for high-temperature applications. This study systematically investigates the influence of various processing parameters on the microstructure and mechanical properties of IMCs. Utilizing a combination of powder metallurgy and subsequent heat treatments, samples were prepared under varied conditions. The microstructural evolution was meticulously examined using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), revealing distinct morphological changes as a function of processing parameters. Quantitative analysis demonstrated a direct correlation between processing conditions and the distribution, size, and morphology of the reinforcing phases. Mechanical testing, including tensile, compression, and hardness tests, was conducted to evaluate the resultant properties. The findings indicate that specific processing conditions can be optimized to achieve a desirable balance between ductility and strength. Notably, a unique set of parameters was identified that yielded an unprecedented combination of high strength and ductility, challenging the conventional trade-off paradigm in composite materials. This research underscores the critical role of processing in tailoring the microstructure and, consequently, the mechanical performance of IMCs, paving the way for their application in demanding environments.
Advanced Modelling and Simulation of Intermetallic Reinforced Composites for Structural and Functional Applications
Authors: Swamy Sujeeth , Prasanna B. Lakshmi , Rao A.L.N. , Kumar Rajeev , Ali Hanaa Addai , Gupta Lovi Raj
Published: October 2023

In recent years, intermetallic reinforced composites (IRCs) have garnered significant attention due to their exceptional mechanical properties, corrosion resistance, and high-temperature stability, making them ideal candidates for both structural and functional applications. This research paper presents an advanced modelling and simulation approach to understand the microstructural evolution, mechanical behaviour, and functional properties of IRCs. Utilizing a combination of finite element analysis (FEA), molecular dynamics (MD), and phase-field modelling, the study offers a comprehensive insight into the intricate interplay between the matrix, reinforcement, and the resultant composite behaviour. The developed models accurately predict the stress-strain response, thermal conductivity, and fatigue life of the IRCs under various loading and environmental conditions. Furthermore, the simulations provide a detailed understanding of the mechanisms governing crack initiation and propagation in these composites. The outcomes of this research not only pave the way for optimizing the design and processing parameters of IRCs but also underscore the potential of these materials in aerospace, automotive, and energy sectors. The findings presented herein serve as a foundational reference for researchers and engineers aiming to harness the full potential of intermetallic reinforced composites in advanced engineering applications.