The rapid proliferation of misinformation across digital platforms has highlighted the critical need for advanced fake news detection mechanisms. Traditional methods primarily rely on textual analysis, often neglecting the structural patterns of news dissemination, which play a crucial role in determining credibility. To address this limitation, this study proposes a Dual-Stream Graph-Augmented Transformer Model, integrating BERT for deep textual representation and Graph Neural Networks (GNNs) to model the propagation structure of misinformation. The objective is to enhance fake news detection by leveraging both linguistic and network-based features. The proposed method employs Graph Attention Networks (GAT) and Graph Transformers to extract contextual relationships, while an attention-based fusion mechanism effectively integrates textual and graph embeddings for classification. The model is implemented using PyTorch and Hugging Face Transformers, with experiments conducted on the FakeNewsNet dataset, which includes news articles, user interactions, and source metadata. Evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC indicate superior performance, with an accuracy of 99%, outperforming baseline models such as Bi-LSTM and RoBERTa-GCN. The study concludes that incorporating graph-based propagation features significantly improves fake news detection, providing a robust, scalable, and context-aware solution. Future enhancements will focus on refining credibility assessment mechanisms and extending the model to support multilingual and multimodal misinformation detection across diverse digital platforms.

With the rapid expansion of digital communication and data sharing, ensuring robust security for sensitive information has become increasingly critical, particularly when data are transmitted over public networks. Traditional encryption techniques are increasingly vulnerable to evolving cyber threats, making single-layer security mechanisms less effective. This study proposes a multi-layered security approach that integrates cryptographic and steganographic techniques to enhance data protection. The framework leverages advanced methods such as encrypted data embedding in images, DNA sequence coding, QR codes, and least significant bit (LSB) steganography. To evaluate its effectiveness, experiments were conducted using text messages, text files, and images, with security assessments based on PSNR, MSE, SNR, and encryption–decryption times for text data. Image security was analyzed through visual inspection, correlation, entropy, standard deviation, key space analysis, randomness, and differential analysis. The proposed method demonstrated strong resilience against differential cryptanalysis, achieving high NPCR values (99.5784%, 99.4292%, and 99.5784%) and UACI values (33.5873%, 33.5149%, and 33.3745%), indicating robust diffusion and confusion properties. These results highlight the reliability and effectiveness of the proposed framework in safeguarding data integrity and confidentiality, providing a promising direction for future cryptographic research.

There are many applications available in the face detection algorithm but very limited applications are identified for further processing. When it comes to identifying faces in the crowd and that too in all-weather conditions then it's too difficult a task to be conducted. Considering this challenge, most of the surveillance systems are not automated. In the sense the CCTV deployed are used only for bufferingpurposes. Very rarely an event is brought to the notice and later CCTV footage is used as a tool for legal issues. There are a lot of concerns about deploying CCTV's in Public places as well. Selecting this system, our work have made sure that the CCTV's will be used to process the video being taken, and whenever an event triggers, notice(s) has to be provided for officials. Thus, the proposed system is for automatic detection and recognition of human faces for finding criminals/suspects/missing persons for surveillance. The intended method first detects a face in the video using a face detection algorithm and then it will search whether the face is present in the data centre. The method provides the ability to detect, extract features, and recognize a face from inputs taken by camera or video automatically. Recognizing faces under different natural conditions can be done by training the system on a limited number of facial images. Also, this system is tested for Covid-19 Post situations, wherein wearing masks in public places is compulsory thus face recognition in these aspects is also tested, and encouraging results have been achieved.

Many people around the world face the data storage and maintenance dilemma, where cloud storage has become a unique platform for this, providing instant access. Cloud storage is highly intrusion tolerant by replication and data sharing, highly resilient by making versioned copies, and offers data protection. Now when crime is committed on the cloud, the real concern comes up. Such kinds of offenses are solved by means of cloud forensics. Cloud forensics is a digital forensics program that monitors and examines the crime committed over the internet. This paper has reviewed the new age digital forensic method, which is in the form of Cloud Forensics.

The term ‘robot’ generally refers to a machine that looks and works in a way similar to a human. The modern industry is rapidly shifting from manual control of systems to automation, in order to increase productivity and to deliver quality products. Computer-based systems, though feasible for improving quality and productivity, are inflexible to work with, and the cost of such systems is significantly high. This led to the swift adoption of automated systems to perform industrial tasks. One such task of industrial significance is of picking and placing objects from one place to another. The implementation of automation in pick and place tasks helps to improve efficiency of system and also the performance. In this paper, we propose to demonstrate the designing and working of an automated robotic arm with the Machine Learning approach. The work uses Machine Learning approach for object identification / detection and traversal, which is adopted with Tensorflow package for better and accurate results.

Current assembling lines for the fridge exploit a robotized assessment device that depends on cameras. As a developing issue, fridge arrangement dependent on pictures from its front view is important for cooler modern mechanization. In any case, this remaining parts an extremely testing task in light of the fact that the cooler is frequently seen against thick mess under conditions. In this paper, we propose a computerized fridge picture arrangement strategy, in light of the new undertaking of the Convolution Neural Network (CNN). It decides the challenges in fridge picture order by streamlining the information driven instrument and advancing both arrangement and comparability imperative. As far as anyone is concerned, this is the first occasion when that profound learning design has been reasonable for the fridge's home apparatuses field. Due to the investigations ordinary out utilizing 31,247 pictures of 30 classifications of coolers, our CNN engineering make a very monumental exactness of 99.96 %.