Volume 12 | Issue 5 |

 Abstract

This system makes use of sensors and ESP 32 micrpocontroller to identify cracks and obstacles on railway track and send the location of cracks if any to the railway authority through the app.This can be used to avoid railway accidents .This becomes automated .

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 Keywords

ESP 32, sensors , railway app, crack and obstacle detection

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 Abstract

My present project concern itself how the term perception enplane by Advaita. According to the Advaita theory of Perception, it is the Chaitanya within us that makes perception possible,. The Chetana (intelligence) within us unites with the chenta in the object, and result is perception. The following well-know illustration from the Vedanta paribhasa gives an account of the nature of perception: "as water from a tank may flow through a channel into a plot of land and assume in shape, so the radiant mind (taijasa-Antahkarana) goes out through the eye or any Vedanta paribhasa cites instances, of perception experience where no sense contact is involved 5, such as pleasure pain, other internal perceptions where moder of mind are directly apprecheded, further, it clearly states that the fact of the sense organ is not the

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 Keywords

Chaitanga, Sannikarsa, Upadhis, Upadhi, Ghatakasa, Kala, Asasa, Kula, Nirupadhika, Paricayaka, Vritti, Janta, Jneya, Advaita, Pramata.

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 Abstract

The growing computation power has made the deep learning algorithms so powerful that creating a indistinguishable human synthesized video popularly called as deep fakes have became very simple. Scenarios where these realistic face swapped deep fakes are used to create political distress, fake terrorism events, revenge porn, blackmail peoples are easily envisioned. In this work, we describe a new deep learning-based method that can effectively distinguish AI-generated fake videos from real videos. Our method is capable of automatically detecting the replacement and reenactment deep fakes. We are trying to use Artificial Intelligence(AI) to fight Artificial Intelligence(AI). Our system uses a Res-Next Convolution neural network to extract the frame- level features and these features and further used to train the Long Short Term Memory(LSTM) based Recurrent Neural Network(RNN) to classify whether the video is subject to any kind of manipulation or not, i.e whether the video is deep fake or real video. To emulate the real time scenarios and make the model perform better on real time data, we evaluate our method on large amount of balanced and mixed data-set prepared by mixing the various available data-set like Face-Forensic++[1], Deepfake detection challenge[2], and Celeb-DF[3]. We also show how our system can achieve competitive result using very simple and robust approach.

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 Keywords

RESNEXT and LSTM Architecture

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 Abstract

"Infospectrum" is a web-based platform designed to streamline the job search process for users. Leveraging advanced technologies such as Convolutional Neural Networks (CNN) and collaborative filtering, the platform analyzes user resumes to suggest the most suit able job roles and the corresponding skills to acquire. By parsing resume information and employing machine learning algorithms, Infospectrum aims to make the job search journey smoother for users by providing personalized recommendations. Developed using the Django framework and incorporating APIs like YouTube, the platform offers learning sources for recommended skills. With a dedicated team trained in TensorFlow, Keras, and Git, Infospectrum aspires to empower users with the necessary tools to secure fulfilling employment opportunities.

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 Keywords

Convolutional Neural Network, Collaborative filtering, User Resume Analysis, Job role recommendation, Personalized recommendations, Skill enhancement.

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 Abstract

Diabetic retinopathy (DR) is a severe complication of diabetes that damages the retina and can lead to vision loss. Early detection and treatment of DR are crucial to prevent blindness. This study proposes using deep learning techniques to automatically detect and classify DR severity from fundus images. The methodology involves collecting fundus images, pre-processing them, training convolutional neural network models, and evaluating model performance using common classification metrics. Various deep learning architectures such as VGGNet, ResNet, and DenseNet will be explored. The models will be trained on large datasets such as Kaggle Diabetic Retinopathy Detection dataset. If successful, the deep learning model can be integrated into screening systems to detect DR early and enable timely treatment. Te process has the capability of reducing vision impairment that is noticed in DR globally.

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 Keywords

VGGNet ,ResNet, and DenseNet

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Environmental degradation is probably the biggest problem that the human rice as a whole is facing. To live in a safe and clean environment is a fundamental right that every person should have. This essay examines the historical evolution of efforts to incorporate environmental protection into the international human rights framework. It traces the major conferences, conventions, and initiatives undertaken since the 1970s to mitigate environmental issues from a human rights perspective. While significant progress has been made, including the recent UN recognition of the human right to a healthy environment, critical gaps remain. It argues that addressing these interconnected challenges requires a holistic, inclusive approach. Ultimately, the essay argues that integrating environmental protection with human rights is paramount in upholding dignity, equality, and other such fundamental rights.

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 Keywords

Environment, Environmental degradation, human rights, climate change, climate refugees, environmental rights

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 Abstract

Approximation computing emerged as a key option for balancing the need for accuracy with the need to use as little energy as possible. Some programmes, like multimedia and signal processing, can handle errors and still process information correctly. This means that the accuracy level is lower than what is required at the circuit level, but the service quality is still good enough at the application level. Automatic recognition of R-peaks is the first and most important step that an electrocardiogram (ECG) signal must go through before it can be processed and analyzed. As its name suggests, the Haar discrete wavelet transform (HDWT) is a simple pre-processing filter that works great for finding electrocardiogram (ECG) R-peaks in embedded systems with very little power, like wrist tech. A study is used to show an approximate HDWT hardware architecture for ECG processing that is very good at using energy efficiently. Our best choice, which includes pruning in the roughly HDWT hardware design, only needs seven extra things to be added. In addition, this article looks at how to use a truncation method to make things use less energy. This is done by looking at how the signal-to-noise ratio changes over time and how that affects the ECG peak-detection programme in the end. According to the findings of this study, our HDWT approximation hardware architecture proposal is capable of bearing larger truncation levels than the HDWT that was first developed. Finally, our results show that using our HDWT matrix approximation idea along with pruning and the highest level of truncation still achieves an average R-peak recognition performance accuracy of 99.68%. This results in a decrease of approximately nine times the amount of energy that was previously consumed.

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 Keywords

Haar DWT, Energy-Efficient, ECG.

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 Abstract

The Concerns about synthetic chemicals in textiles are growing, and this, together with an increase in skin allergies and infections, highlights the need for safer and more environmentally friendly textile finishing options. However the study aspects into the use of extracts from Psidium guajava plants as functional finishes on cotton fabrics to improve their dermatological qualities and add antifungal and antibacterial capabilities. Selected guava leaves, known for their skin-friendly qualities, were processed using the Soxhlet extraction method to yield strong extracts that were then used as finishes on cotton textile materials. The guava leaves extracts shows low toxicity levels, biodegradability, and environmentally gentle profile make them attractive green substitutes for synthetic chemical agents that are frequently utilized in textile industry. The study was to determine the fabric property and change due to the finishing process. The Antibacterial activity of the coated materials were analyzed with Klebisiella pneumonia, Streptococcus pneumonia, Staphylococcus aureus and Pseudomonas aeruginosa was within an acceptable level.

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 Keywords

Textile finishing, Cotton, Psidium guajava, extract, Soxhelet, Antibacterial, Physical properties.

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 Abstract

Power generation is one of the issues. Now -a-days number of power sources are present, non-renewable and renewable, but still we cannot overcome our power needs. Among these human population is one of the resources. In this project we are generating power by running or walking. Power can be generated by walking upon stairs. This system can be installed in homes, schools, colleges, where the people move around the clock. When the people walk on the steps or that of platform, power is generated by using weight of person. This mechanical energy applied on the crystal into electrical energy. When there is some vibrations, stress or straining force exert by foot on flat platform

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 Keywords

Renewable Energy power, Electrical Energy, Mechanical Energy, Foot Step

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 Abstract

The RFID-based smart attendance system is a creative method of automating and streamlining the tracking and monitoring of attendance in commercial and educational contexts. RFID (Radio Frequency Identification) technology is used by the system. RFID scanners are positioned at strategic entry points, and RFID tags are assigned to particular persons. When an individual passes the reader with a valid RFID tag, their attendance is automatically entered into a central database. This approach significantly reduces the amount of manual effort required to track attendance while minimizing errors and enhancing data security. Administrators may monitor attendance trends and generate comprehensive reports using its real-time data analytics and reporting features. The integration of RFID technology enables adaptability and adaptation to satisfy various organizational needs, hence permitting additional features such as access control and time management. Since an RFID-based attendance system promotes more efficiency, enhanced accountability, and fewer administrative tasks, it is a valuable tool for modern educational institutions and enterprises seeking to raise operational efficacy and production.

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 Keywords

Smart Attendance, Advanced Technology, RFID, Tag, Efficient, Administrative Operations

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Creative Commons Attribution 4.0 and The Open Definition