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Aim: The aim of the present study was to assess the neurological manifestations in patients diagnosed with HIV and to assess CD 4 counts in HIV Patients. Methods: This study was conducted in the Department of Medicine and ART Center, Rajindra Hospital, Patiala. A total 200 patients in the time period 1st April to March 2021 were included in the study. Results: Majority of patients were in economically productive age group 21-45 years. 70.50% patients were in 26-45 years of age group with mean age range 36.28±10.20. Out of 200 patients 55 (28.5%) had neurological manifestations. Among the 55 patients with neurological manifestations, majority (60.40%) were in 26-45 year age group with Mean age of study subjects: 36.28+ 10.20, Mean age for males: 34.30 ± 7.29, Mean age for females –29.27 ± 6.47and Male to female ratio : 2.56:1. Fever was the commonest symptom seen among the patients with neurological manifestations in 41 patients (71.93%) followed by altered sensorium seen in 36 patients (63.16%), head ache in 30 patients (52.63%), seizures in 15 patients (26.32%) of the patients. These results were statistically significant for all neurological symptoms except headache. [ABSTRACT FROM AUTHOR]

Aim: The present study was undertaken with the aim and objective to study the electrocardiographic and echocardiographic changes in patients with chronic kidney disease on maintenance hemodialysis. Methods: A cross sectional study consisted of 100 patients with chronic kidney disease on maintenance hemodialysis in the Department of Medicine, Govt. Medical College, Patiala. Results: In age group <30 years, 15% of patients were involved. In age group 41-50 years 22% patients were involved. In age group 51-60 years, 29% patients were involved. In age group 61- 70 years, 17% patients were involved. In age group >70 years, 17% patients were involved. Out of 100 patients, there were 57% male and 43% females. 1% was having Autosomal dominant polycystic kidney disease, 8% having acute tubular necrosis, 18% having chronic glomerulonephritis, 23% having diabetes mellitus, 30% having diabetes Mellitus + hypertension, 15% having Hypertension, 2% having IGA nephropathy and 3% having obstructive uropathy. ECG was normal in 25% cases. LVH in 35% cases, LAD in 6%, conduction disturbances in 18%, Ischemic changes in 6%, Arrhythmia in 6% and P-mitrale in 4%. The most common ECG changes were 35% in LVH and 26% in conduction disturbance. The table shows echocardiographic changes in CKD cases on hemodialysis. Normal was found in 12 % of cases, left ventricular hypertrophy in 44% of patients, RWMA in 8%, pericardial effusion in 7%, diastolic dysfunction was found in 15% of patients and systolic dysfunction in 14% of patients. Conclusion: Left ventricular hypertrophy is the commonest abnormality observed in patient’s chronic kidney disease on maintenance hemodialysis both on ECG and Echocardiography. Echocardiography is a more sensitive diagnostic procedure to detect left ventricular hypertrophy. After Left Ventricular Hypertrophy the next common abnormality found on Echocardiography is Left Ventricular Diastolic Dysfunction. [ABSTRACT FROM AUTHOR]

Scientists and researchers have tried to establish a bond between the emotions conveyed and the subsequent mood perceived in a person. Emotions play a major role in terms of our choices, preferences, and decision-making. Emotions appear whenever a person perceives a change in their surroundings or within their body. Since early times, a considerable amount of effort has been made in the field of emotion detection and mood estimation. Listening to music forms a major part of our daily life. The music we listen to, the emotions it induces, and the resulting mood are all interrelated in ways we are unbeknownst to, and our survey is entirely based on these two areas of research. Differing viewpoints on this issue have led to the proposal of different ways of emotion annotation, model training, and result visualization. This paper provides a detailed review of the methods proposed in music mood recognition. It also discusses the different sensors that have been utilized to acquire various physiological signals. This paper will focus upon the datasets created and reused, different classifiers employed to obtain results with higher accuracy, features extracted from the acquired signals, and music along with an attempt to determine the exact features and parameters that will help in improving the classification process. It will also investigate several techniques to detect emotions and the different music models used to assess the music mood. This review intends to answer the questions and research issues in identifying human emotions and music mood to provide a greater insight into this field of interest and develop a better understanding to comprehend and answer the perplexing problems that surround us. [ABSTRACT FROM AUTHOR]

Forensic analyst skills are at stake for processing of growing data from IoT based environment platforms. Tangible sources often have the size limits, but that's not the case for communication traffic source. Hence, increasing the thirst for an efficient benchmarking for big data analysis. Available solutions to date have used an anomaly-based approach or have proposed approaches based on the deviation from a regular pattern. To tackle the seized bytes, authors have proposed an approach for big data forensics, with efficient sensitivity and precision. In the presented work, a generalized forensic framework has been proposed that use Google's programming model, MapReduce as the backbone for traffic translation, extraction, and analysis of dynamic traffic features. For the proposed technique, authors have used open source tools like Hadoop, Hive, and Mahout and R. Apart from being open source, these tools support scalability and parallel processing. Also, comparative analysis of globally accepted machine learning models of P2P malware analysis in mocked real-time is presented. Dataset from CAIDA was taken and executed in parallel to validate the proposed model. Finally, the forensic performance metrics of the model shows the results with the sensitivity of 99%. [ABSTRACT FROM AUTHOR]

Summary: With an exponential increase in the data size and complexity of various documents to be investigated, existing methods of network forensics are found not much efficient with respect to accuracy and detection ratio. The existing techniques for network forensic analysis exhibit inherent limitations while processing a huge volume, variety, and velocity of data. It makes network forensic a time‐consuming and resource‐consuming task. To balance time taken and output delivered, these existing techniques put a limit on the amount of data under analysis, which results in a polynomial time complexity of these solutions. So to mitigate these issues, in this paper, we propose an effective framework to overcome the limitation to handle large volume, variety, and velocity of data. An architectural setup that consists of MapReduce framework on top of Hadoop Distributed File System environment is proposed in this paper. The proposed framework demonstrates its capability to handle issues of storage and processing of big data using cloud computing. Also, in the proposed framework, supervised machine learning (random forest‐based decision tree) algorithm has been implemented to demonstrate better sensitivity. To train and validate the model, online available data set from CAIDA is taken and university network traffic samples, with increasing size, has been taken for experiment. Results thus obtained confirm the superiority of the proposed framework in network forensics, with an average accuracy of 99.34% (malicious and nonmalicious traffic). [ABSTRACT FROM AUTHOR]