Capacity Dimensioning of HSDPA Urban Network

To launch a cellular network, prelaunch capacity dimensioning is performed which includes coverage estimation and throughput prediction. Cellular companies in developing countries like Pakistan are only providing 2G services, while 3G services are yet to be launched. Although a lot of research has been done on 3G services in developed countries but there is very little knowledge regarding practical aspects of planning and optimization of 3G networks in third world countries like Pakistan. This research paper includes a thorough analysis of factors that affect capacity of 3G networks, including radio propagation models. Various propagation models are studied and propagation constants of Standard Propagation Model are tuned according to topography of Islamabad. The performance analysis of these propagation models is done using Matlab and results are verified through planning tool Atoll and field measurements. Based on analysis of these results capacity dimensioning, in terms of number of sites, is carried out for an urban network of Islamabad

A review of natural language processing in contact centre automation

Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco

Utilisation of Open Intent Recognition Models for Customer Support Intent Detection

Businesses have sought out new solutions to provide support and improve customer satisfaction as more products and services have become interconnected digitally. There is an inherent need for businesses to provide or outsource fast, efficient and knowledgeable support to remain competitive. Support solutions are also advancing with technologies, including use of social media, Artificial Intelligence (AI), Machine Learning (ML) and remote device connectivity to better support customers. Customer support operators are trained to utilise these technologies to provide better customer outreach and support for clients in remote areas. Interconnectivity of products and support systems provide businesses with potential international clients to expand their product market and business scale. This paper reports the possible AI applications in customer support, done in collaboration with the Knowledge Transfer Partnership (KTP) program between Birmingham City University and a company that handles customer service systems for businesses outsourcing customer support across a wide variety of business sectors. This study explored several approaches to accurately predict customers' intent using both labelled and unlabelled textual data. While some approaches showed promise in specific datasets, the search for a single, universally applicable approach continues. The development of separate pipelines for intent detection and discovery has led to improved accuracy rates in detecting known intents, while further work is required to improve the accuracy of intent discovery for unknown intents

Design and Evaluation of a Button Sensor Antenna for On-Body Monitoring Activity in Healthcare Applications

A button sensor antenna for on-body monitoring in wireless body area network (WBAN) systems is presented. Due to the close coupling between the sensor antenna and the human body, it is highly challenging to design sensor antenna devices. In this paper, a mechanically robust system is proposed that integrates a dual-band button antenna with a wireless sensor module designed on a printed circuit board (PCB). The system features a small footprint and has good radiation characteristics and efficiency. This was fabricated, and the measured and simulated results are in good agreement. The design offers a wide range of omnidirectional radiation patterns in free space, with a reflection coefficient (S11) of −29.30 (−30.97) dB, a maximum gain of 1.75 (5.65) dBi, and radiation efficiency of 71.91 (92.51)% in the lower and upper bands, respectively. S11 reaches −23.07 (−27.07) dB and −30.76 (−31.12) dB, respectively, with a gain of 2.09 (6.70) dBi and 2.16 (5.67) dBi, and radiation efficiency of 65.12 (81.63)% and 75.00 (85.00)%, when located on the body for the lower and upper bands, respectively. The performance is minimally affected by bending, movement, and fabrication tolerances. The specific absorption rate (SAR) values are below the regulatory limitations for the spatial average over 1 g (1.6 W/Kg) and 10 g of tissues (2.0 W/Kg). For both indoor and outdoor conditions, experimental results of the range tests confirm the coverage of up to 40 m

An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis

Human communication is predominantly expressed through speech and writing, which are powerful mediums for conveying thoughts and opinions. Researchers have been studying the analysis of human sentiments for a long time, including the emerging area of bimodal sentiment analysis in natural language processing (NLP). Bimodal sentiment analysis has gained attention in various areas such as social opinion mining, healthcare, banking, and more. However, there is a limited amount of research on bimodal conversational sentiment analysis, which is challenging due to the complex nature of how humans express sentiment cues across different modalities. To address this gap in research, a comparison of multiple data modality models has been conducted on the widely used MELD dataset, which serves as a benchmark for sentiment analysis in the research community. The results show the effectiveness of combining acoustic and linguistic representations using a proposed neural-network-based ensemble learning technique over six transformer and deep-learning-based models, achieving state-of-the-art accuracy

Utilisation of Open Intent Recognition Models for Customer Support Intent Detection

Businesses have sought out new solutions to provide support and improve customer satisfaction as more products and services have become interconnected digitally. There is an inherent need for businesses to provide or outsource fast, efficient and knowledgeable support to remain competitive. Support solutions are also advancing with technologies, including use of social media, Artificial Intelligence (AI), Machine Learning (ML) and remote device connectivity to better support customers. Customer support operators are trained to utilise these technologies to provide better customer outreach and support for clients in remote areas. Interconnectivity of products and support systems provide businesses with potential international clients to expand their product market and business scale. This paper reports the possible AI applications in customer support, done in collaboration with the Knowledge Transfer Partnership (KTP) program between Birmingham City University and a company that handles customer service systems for businesses outsourcing customer support across a wide variety of business sectors. This study explored several approaches to accurately predict customers' intent using both labelled and unlabelled textual data. While some approaches showed promise in specific datasets, the search for a single, universally applicable approach continues. The development of separate pipelines for intent detection and discovery has led to improved accuracy rates in detecting known intents, while further work is required to improve the accuracy of intent discovery for unknown intents

A Review of Natural Language Processing in Contact Centre Automation

Contact centres have been highly valued by organisations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organisations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer recommendations for overcoming them, ultimately expediting the pace of contact centre automation

Pseudoaneurysm at anastomotic site in a case of renal transplant: A rare postsurgical complication

A rare case of a large pseudoaneurysm at the anastomotic site of internal iliac artery with the graft renal artery in a case of renal transplant is presented with a vision of documenting this condition and making clinicians familiar with this rare postsurgical complication in patients of renal transplant

Bedside Ultrasound-Guided Percutaneous Cholecystostomy in Critically Ill Patients—Outcomes in 51 Patients

Purpose The aim of this study was to report technical and clinical success of bedside ultrasound-guided percutaneous cholecystostomy (PC) tube placement in intensive care unit (ICU)

Anesthetic considerations for endovascular abdominal aortic aneurysm repair

Aneurysm is defined as a localized and permanent dilatation with an increase in normal diameter by more than 50%. It is more common in males and can affect up to 8% of elderly men. Smoking is the greatest risk factor for abdominal aortic aneurysm (AAA) and other risk factors include hypertension, hyperlipidemia, family history of aneurysms, inflammatory vasculitis, and trauma. Endovascular Aneurysm Repair [EVAR] is a common procedure performed for AAA, because of its minimal invasiveness as compared with open surgical repair. Patients undergoing EVAR have a greater incidence of major co-morbidities and should undergo comprehensive preoperative assessment and optimization within the multidisciplinary settings. In majority of cases, EVAR is extremely well-tolerated. The aim of this article is to outline the Anesthetic considerations related to EVAR