In digital forensics, one of the complicated tasks is analyzing web browser data due to different types of devices, browsers, and the absence of modern analytical approaches. Browsers store a large amount of information about user activity because users most often access the internet through them. However, existing approaches to analyzing this browser data still have gaps. Existing approaches fail to provide a comprehensive and precise representation of user activity. This article examines the internal architecture of web browsers as stored in the memory and storage subsystems of various devices, including desktop and mobile platforms. A novel method is proposed that integrates machine learning algorithms, such as k-nearest neighbors and Naive Bayes, to automatically analyze browser data, identify suspicious login activities, and construct user behavior profiles. The results indicate that the proposed method and the developed platform can effectively construct individual user behavior profiles. Moreover, this approach not only productively observes top visited domains and main user’s favorite website categories, but also highlights suspicious websites and user’s login attempts. Compared to existing browser forensic tools which have less capabilities, the proposed technique provides increased accuracy (more than 90%) in automated user profiling and detection of suspicious user activity.

This paper presents a novel fully autonomous multilingual audio transcription system tailored to Kazakh, Russian, and English. The proposed solution integrates a language detection module based on SpeechBrain with a transcription engine using Vosk, and employs FFmpeg for robust audio preprocessing. The system automatically detects the language from the initial 10 seconds of an audio stream, selects the corresponding acoustic model, and produces an accurate text transcription. Experimental evaluations on both synthetic and real audio data indicate that our approach achieves competitive performance in terms of accuracy (with word error rates ranging from 5 to 10% under optimal conditions) and processing speed, while operating entirely on local resources without dependency on cloud services. These features make it particularly suitable for applications in digital forensics and other domains that require secure real-time transcription capabilities.

Kazakh automatic speech recognition (ASR) presents a persistent challenge for large-scale multilingual models. This paper presents a systematic evaluation of 27 ASR pipeline configurations (three ASR models × three VAD methods × three post-processing strategies) on the Kazakh Speech Dataset (KSD), examining the contribution of model fine-tuning, voice activity detection (VAD) preprocessing, and large language model (LLM) post-correction and benchmarking the resulting pipelines against two non-Whisper foundation models. Language-specific fine-tuning reduces Word Error Rate (WER) from 43.20% (generic Whisper-large-v3) to 11.88% (Kazakh fine-tuned Whisper-turbo), a 31.32-percentage-point absolute reduction (72.5% relative; p < 0.001, bootstrap test); the effect persists after controlling for model size (generic Whisper-large-v3-turbo, 18.92%, vs. the same architecture after fine-tuning, 11.88%; p < 0.001). VAD preprocessing consistently degrades performance. Zero-shot post-correction with general-purpose LLMs yields no benefit and adds substantial latency: Gemma-2-9B and Qwen2.5-7B raise WER by 5.5 and 7.2 percentage points at real-time factors of 0.52 and 0.30, and a larger 32B model still degrades accuracy (+10.8 points), indicating that scale is not the limiting factor. Among all systems evaluated, a larger multilingual foundation model, SeamlessM4T-v2 (9.72% WER), outperforms the fine-tuned Whisper, showing that for Kazakh model coverage matters more than pipeline engineering. Character-level error analysis identifies systematic confusion between Kazakh-specific and Russian Cyrillic characters as a dominant error source. These findings establish that, for Kazakh under the evaluated conditions, model choice dominates pipeline add-ons: fine-tuning is essential, VAD and zero-shot LLM correction consistently hurt, and a strong multilingual model sets the best result; we further discuss the extent to which these conclusions extend to typologically similar Kipchak-Turkic languages.

The paper examines in detail the modern methods of image compression, focusing on how advanced algorithms are used in practical digital imaging systems. The study examines many compression methods, including LZMA, LERC, ZSTD and their mixed forms and compares how well they perform in terms of compression ratio, time required, memory efficiency and how much information entropy they keep. Machine learning methods for compression are used in the analysis, focusing on how they work with images from medical imaging as well as satellite data. Experiments are performed on standardized datasets, with the main goal of following the theoretical limits set by Shannon’s Source Coding Theorem. The study shows that using modern hybrid algorithms, it is possible to compress data by at least 4:1 and keep it safe, with LZMA and LERC combinations performing best when the data is subject to entropic constraints. The results show that using parallel processing leads to a 60% decrease in processing time when compared to traditional single-threaded methods. The results strengthen the theories and techniques needed for the next generation of compression systems, mainly for handling high-resolution images quickly.

In digital forensics, one of the complicated task is analyzing web browser data due to different types of devices, browsers and no updated approaches. Browsers store a large amount of information about user activity because users most often access the internet through them. However, existing approaches to analyzing this browser data still have gaps. One of the main problem developed platforms based on the old methods can not show complete information about the user's activity and have issues with precision. The article discusses the internal architecture of the browser, which is stored in the memory drives inside devices, for instance, computers or mobile devices. The research paper offers solution with developed module based on new method which integrates machine learning algorithms, such as K-NN algorithm and Naive Bayes. The main purpose of the paper it is shows new method which can automatically analyzes browser's data, detects suspicious login activity, and generates user behavior profile. The results show that the proposed new method , on which the developed platform is based, demonstrates user's profile by interests, emotional state and financial state. Also it possible to see list of top visited domain and main user's favorite website categories. It has been found that our methods shows with high accuracy 99.9\% . Also the result of new method , on which the developed platform is based shows the suspicious web-sites and user's logins. Compared to Oxygen Forensics and Nirsoft which less capabilities., the proposed method provides increased accuracy , automated user profiling and detection of suspicious user's activity.

This study presents «trajectory_analyzer», a Python-based system designed for the forensic analysis and visualization of geolocation data extracted from mobile devices. With the increasing volume of spatial-temporal data collected from sources such as GPS, Wi-Fi, and image metadata, forensic professionals face growing challenges in structuring and interpreting mobility patterns. Existing solutions often lack flexibility, require supervised models, or depend on proprietary infrastructure. Our approach applies an unsupervised DBSCAN-based trajectory clustering method, temporal ordering, and a real-time web map interface to reveal behavioral insights without the need for manual labeling or cloud services. Compared to prior research, the system improves spatial accuracy, source transparency, and visual clarity. Experimental results show that the proposed clustering method identifies movement clusters and transitions with high precision and responsiveness, while maintaining full offline operability. However, this improvement comes at the expense of more local storage because of embedded map tiles. Overall, this work provides a practical, understandable, and independent foundation for investigators dealing with unstructured multi-source geolocation data.