Recognizing activities of daily living from patterns and extraction of web knowledge

The ability to infer and anticipate the activities of elderly individuals with cognitive impairment has made it possible to provide timely assistance and support, which in turn allows them to lead an independent life. Traditional non-intrusive activity recognition approaches are dependent on the use of various machine learning techniques to infer activities given the collected object usage data. Current activity recognition approaches are also based on knowledge driven techniques that require extensive modelling of the activities that needs to be inferred. These models can be seen as too restrictive, prescriptive and static as they are based on a finite set of activities. In this paper, we propose a novel “top down” approach to recognising activities based on object usage data, which detects patterns associated with the activity-object relationship and utilizes web knowledge in order to build dynamic activity models based on the objects used to perform the activity. Experimental results using the Kasteren dataset shows it is comparable to existing approaches

Implementation of lightweight machine learning-based intrusion detection system on IoT devices of smart homes

Smart home devices, also known as IoT devices, provide significant convenience; however, they also present opportunities for attackers to jeopardize homeowners’ security and privacy. Securing these IoT devices is a formidable challenge because of their limited computational resources. Machine learning-based intrusion detection systems (IDSs) have been implemented on the edge and the cloud; however, IDSs have not been embedded in IoT devices. To address this, we propose a novel machine learning-based two-layered IDS for smart home IoT devices, enhancing accuracy and computational efficiency. The first layer of the proposed IDS is deployed on a microcontroller-based smart thermostat, which uploads the data to a website hosted on a cloud server. The second layer of the IDS is deployed on the cloud side for classification of attacks. The proposed IDS can detect the threats with an accuracy of 99.50% at cloud level (multiclassification). For real-time testing, we implemented the Raspberry Pi 4-based adversary to generate a dataset for man-in-the-middle (MITM) and denial of service (DoS) attacks on smart thermostats. The results show that the XGBoost-based IDS detects MITM and DoS attacks in 3.51 ms on a smart thermostat with an accuracy of 97.59%.</p

Occult Hepatitis B in Patients with Decompensated Chronic Liver Disease: A Cross Sectional Study at Tertiary Care Hospital, Peshawar

OBJECTIVES The objective of this study was to find the frequency of occult hepatitis B in patients presenting with Decompensated chronic liver disease. METHODOLOGY This descriptive, cross-sectional study was conducted at a tertiary care hospital at Peshawar-KP from 31st December 2021 to 31st May 2022.143 patients were enrolled. Informed consent was taken from all patients who were enrolled in this study. We included patients aged 18-60 years of either gender. All patients admitted to medical units or visiting Medical OPDs having decompensated Chronic liver disease were enrolled. A consecutive sampling technique was used to enroll patients for our study. Baseline characteristics, demographics and laboratory data were collected on predesigned proforma. All patients were screened for Hepatitis B infection by performing HBsAg with ELISA, and patients with negative surface antigens were eligible for the study.RESULTSOur study population age range was from 18 to 60 years, with a mean age of 43.30±8.00 years. There were 100 male (69.9%) patients and 43 female (30.1%) patients. Occult Hepatitis B was observed in 40(28%) patients with decompensated chronic liver disease. Among these patients, 26 were male, and 14 were female. Stratification for Occult Hepatitis B was done concerning age &amp; gender to see any significant difference in distribution. There was no significant difference in the distribution of Occult Hepatitis B among different age groups and gender. CONCLUSION This study has shown that a significant proportion of decompensated chronic liver disease patients had evidence of occult hepatitis B infection. Methodology: This was a descriptive, cross sectional study that was conducted at a tertiary care hospital at Peshawar-KP from 31st December 2021 till 31st May 2022.. 143 patients were enrolled in this study. An informed consent was taken from all patients who were enrolled in this study. We included patients having age 18-60 years of either gender. All patients admitted in medical units or visiting Medical OPDs having decompensated Chronic liver disease were enrolled. Consecutive sampling technique was used to enroll patients for our study. Baseline characteristics, demographics and laboratory data was collected on predesigned proforma. All patients were screened for Hepatitis B infection by performing HBsAg with ELISA and patients with negative surface antigen were eligible for the study. Results: Our study population age range was from 18 to 60 years with mean age of 43.30±8.00 years There were 100 male (69.9%) patients and 43 female (30.1%) patients. Occult Hepatitis B was observed in 40(28%) of patients with decompensated chronic liver disease. Among these patients, 26 were male and 14 were female. Stratification for Occult Hepatitis B was done with respect to age &amp; gender to see any significant difference in distribution. There was no significant difference in distribution of Occult Hepatitis B among different age groups and gender. Conclusion: This study has shown that significant proportion of decompensated chronic liver disease patients had evidence of occult hepatitis B infection

Effect of Blood Pressure Lowering Therapy in Stroke Patients

Objective: The objective of the study is to assess the effect of blood pressure lowering with Candesartan in patients with stroke and elevated blood pressure admitted in this hospital.Study Design: Prospective descriptive observational study.Setting: Neurosurgery, Medical Emergency / OPD, Lady Reading Hospital, Peshawar.Materials and Methods: This descriptive study was done at the Department of Medicine and Neurosurgery, Postgraduate Medical Institute, Lady Reading Hospital Peshawar from January 2013 to May 2014 (for One year and 5 months period) in a total of 357 patients. In this descriptive study, patients presenting to Emergency department or OPD with stroke and elevated blood pressure, presenting within 30 hours of symptom onset and with SBP ≥ 140 mmHg, diastolic &gt; 90 mmHg, were eligible for inclusion. Exclusion criteria were contraindicat-ions to or ongoing treatment with an angiotensin receptor blocker, markedly reduced consciousness, patients with chronic heart failure and intolerance to ACE inhibitors, patient unavailability for follow-up and pregnancy or breast – feeding. The acute phase treatment was a fixed dose of 4 mg on day 1, 8 mg on day 2 and 16 mg on days 3 to 7. Blood pressure was measured daily with the patient in the supine position using a blood pressure monitor. All patients were follow-up on day 7 and at 1 and 6 months after discharged from hospital.Results: Among 357 cases, 68.06% were males and 31.93% females. Majority (37.25%) belongs to age group of 61 – 70 years. Out of these, 66.10% patients were found to have ischemic and 33.89% patients had hemorrhagic stroke. Highest (40.05%) patients belonged to severe hypertensive group i.e. ≥ 180/110 mmHg. Target was achieved in 75.91% patients.Conclusions: Our data suggests that lowering BP in acute ICH is probably safe; however, it remains to be seen if this decreases hematoma expansion or improves outcome

Activities of daily life recognition using process representation modelling to support intention analysis

Purpose – This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. Design/methodology/approach – This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients. Findings – A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches. Originality/value – The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features

Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the work in this paper, we have analyzed a range of widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. This study also focuses on each of the algorithm’s strengths and weaknesses for finding patterns among large item sets in database systems

Continuous authentication of smartphone users based on activity pattern recognition using passive mobile sensing

Smartphones are inescapable devices, which are becoming more and more intelligent and context-aware with emerging sensing, networking, and computing capabilities. They offer a captivating platform to the users for performing a wide variety of tasks including socializing, communication, sending or receiving emails, storing and accessing personal data etc. at anytime and anywhere. Nowadays, loads of people tend to store different types of private and sensitive data in their smartphones including bank account details, personal identifiers, accounts credentials, and credit card details. A lot of people keep their personal e-accounts logged in all the time in their mobile devices. Hence, these mobile devices are prone to different security and privacy threats and attacks from the attackers. Commonly used approaches for securing mobile devices such as passcode, PINs, pattern lock, face recognition, and fingerprint scan are vulnerable and exposed to several attacks including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To address these challenges, a novel continuous authentication scheme is presented in this study, which recognizes smartphone users on the basis of their physical activity patterns using accelerometer, gyroscope, and magnetometer sensors of smartphone. A series of experiments are performed for user recognition using different machine learning classifiers, where six different activities are analyzed for multiple locations of smartphone on the user's body. SVM classifier achieved the best results for user recognition with an overall average accuracy of 97.95%. A comprehensive analysis of the user recognition results validates the efficiency of the proposed scheme

Thermal-aware resource allocation in earliest deadline first using fluid scheduling

Thermal issues in microprocessors have become a major design constraint because of their adverse effects on the reliability, performance and cost of the system. This article proposes an improvement in earliest deadline first, a uni-processor scheduling algorithm, without compromising its optimality in order to reduce the thermal peaks and variations. This is done by introducing a factor of fairness to earliest deadline first algorithm, which introduces idle intervals during execution and allows uniform distribution of workload over the time. The technique notably lowers the number of context switches when compare with the previous thermal-aware scheduling algorithm based on the same amount of fairness. Although, the algorithm is proposed for uni-processor environment, it is also applicable to partitioned scheduling in multi-processor environment, which primarily converts the multi-processor scheduling problem to a set of uni-processor scheduling problem and thereafter uses a uni-processor scheduling technique for scheduling. The simulation results show that the proposed approach reduces up to 5% of the temperature peaks and variations in a uni-processor environment while reduces up to 7% and 6% of the temperature spatial gradient and the average temperature in multi-processor environment, respectively

Can bacterial endophytes be used as a promising bio-inoculant for the mitigation of salinity stress in crop plants? : a global meta-analysis of the last decade (2011-2020)

Soil salinity is a major problem affecting crop production worldwide. Lately, there have been great research efforts in increasing the salt tolerance of plants through the inoculation of plant growth-promoting endophytic bacteria. However, their ability to promote plant growth under no-stress and salinity-stress conditions remains largely uncertain. Here, we carried out a global meta-analysis to quantify the plant growth-promoting effects (improvement of morphological attributes, photosynthetic capacity, antioxidative ability, and ion homeostasis) of endophytic bacteria in plants under no-stress and salinity-stress conditions. In addition, we elucidated the underlying mechanisms of growth promotion in salt-sensitive (SS) and salt-tolerant (ST) plants derived from the interaction with endophytic bacteria under no-stress and salinity-stress conditions. Specifically, this work encompassed 42 peer-reviewed articles, a total of 77 experiments, and 24 different bacterial genera. On average, endophytic bacterial inoculation increased morphological parameters. Moreover, the effect of endophytic bacteria on the total dry biomass, number of leaves, root length, shoot length, and germination rate was generally greater under salinity-stress conditions than no-stress conditions. On a physiological level, the relative better performance of the bacterial inoculants under the salinity-stress condition was associated with the increase in total chlorophyll and chlorophyll-b, as well as with the decrease of 1-aminocylopropane-1-carboxylate concentration. Moreover, under the salinity-stress condition, bacterial inoculation conferred a significantly higher increase in root K+ concentration and decrease in leaf Na+ concentration than under the no-stress condition. In SS plants, bacterial inoculation induced a higher increase in chlorophyll-b and superoxide dismutase activity, as well as a higher decrease in abscisic acid content, than in ST plants. Under salinity-stress, endophytic bacterial inoculation increased root K+ concentration in both SS and ST plants but decreased root Na+ concentration only in ST plants. Overall, this meta-analysis suggests that endophytic bacterial inoculation is beneficial under both no salinity-stress and salinity-stress conditions, but the magnitude of benefit is definitely higher under salinity-stress conditions and varies with the salt tolerance level of plants